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8002 - II- Mathematical Foundations of Risk(R) Measurement - Dump Information

Vendor : PRMIA
Exam Code : 8002
Exam Name : II- Mathematical Foundations of Risk(R) Measurement
Questions and Answers : 132 Q & A
Updated On : December 11, 2018
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8002 II- Mathematical Foundations of Risk(R) Measurement

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8002 exam Dumps Source : II- Mathematical Foundations of Risk(R) Measurement

Test Code : 8002
Test Name : II- Mathematical Foundations of Risk(R) Measurement
Vendor Name : PRMIA
Q&A : 132 Real Questions

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PRMIA PRMIA II- Mathematical Foundations

Brandeis foreign enterprise college | killexams.com Real Questions and Pass4sure dumps

Robert R. Reitano is Professor of the follow of Finance at the Brandeis international enterprise school where he specializes in possibility administration and quantitative finance, and has previously served as Senior Director of academics. He also teaches as Adjunct Professor within the Wuhan college of technology faculty of Economics, and prior to now was traveling Professor at Reykjavik college faculty of company, and Adjunct Professor in Boston institution's Masters degree software in Mathematical Finance.

Dr. Reitano is a expert in funding approach and economic chance management, was Chief funding Officer of managed chance insurance business (CRICO), and previously had a 29 yr career at John Hancock/Manulife in asset/legal responsibility possibility administration and funding strategy, advancing to govt vice chairman & Chief funding Strategist.

His research papers have seemed in a couple of journals and have received an Annual Prize of the Society of Actuaries and two biennial F.M. Redington Prizes awarded through the funding element of the Society of the Actuaries. His book, “Introduction to Quantitative Finance: A Math tool kit,” turned into posted by using The MIT Press in January, 2010, and translated into chinese language with the aid of fact & knowledge Press in March, 2015. he is self-publishing an advanced follow-on sequence, “Foundations of Quantitative Finance,” of which the first four books were published in 2017 and available on his website at http://www.robertrreitano.com/fqf/.

Dr. Reitano has served as Vice Chair of the Board of administrators of the professional chance Managers foreign association (PRMIA) and on the government Committee of the PRMIA Board, is presently a member of the PRMIA Boston steering Committee, the fiscal research Committee of the Society of Actuaries, and serves on different now not-for-profit boards and investment committees.

He has a Ph.D. in arithmetic from MIT, is a Fellow of the Society of Actuaries, and a Chartered commercial enterprise possibility Analyst.

levels:Massachusetts Institute of know-how, Ph.D.university of Massachusetts, Amherst, M.A.

Publications:

  • Reitano, Robert R.. Foundations of Quantitative Finance, booklet three: The Integrals of Lebesgue and (Riemann-)Stieltjes. First ed. Self-posted, 2018.
  • Reitano, Robert R.. Foundations of Quantitative Finance, e-book 4: Distribution services and Expectations. First ed. Self-published, 2018.
  • Reitano, Robert R.. Foundations of Quantitative Finance, e-book 1: Measure spaces and Measurable features. First ed. Self-published, 2017.
  • Reitano, Robert R.. Foundations of Quantitative Finance, publication 2: probability areas and Random Variables. First ed. Self-posted, 2017.
  • Reitano, Robert R.. Introduction to Quantitative Finance: A Math device equipment (chinese edition). First ed. Shanghai, China: fact & wisdom Press, 2015.
  • Reitano, Robert R.. teacher's guide to Accompany Introduction to Quantitative Finance: A Math tool kit. First ed. Cambridge, MA: The MIT Press, 2010.
  • Reitano, Robert R.. Introduction to Quantitative Finance: A Math device package. First ed. Cambridge, MA: The MIT Press, 2010.
  • Reitano, Robert R.. pupil solutions manual to Accompany Introduction to Quantitative Finance: A Math device kit. First ed. Cambridge, MA: The MIT Press, 2010.
  • Reitano, Robert R.. "Yield Curve possibility management." handbook Of Finance. First ed. vol. Valuation, monetary Modeling, and Quantitative tools; Ed. Frank J. Fabozzi. Hoboken, NJ: John Wiley & Sons, Inc., 2009. pp 215-232.
  • Reitano, Robert R.. "Two Paradigms for the Market cost of Liabilities." North American Actuarial Journal quantity 1. quantity four (1997): 104-122.
  • Reitano, Robert R.. "Non-Parallel Yield Curve Shifts and Stochastic Immunization." Journal of Portfolio management extent 22. quantity 2 (1996): 71-seventy eight.
  • Reitano, Robert R.. "Multivariate Stochastic Immunization idea." Transactions of the Society of Actuaries XLV. (1994): 425-461.
  • Reitano, Robert R.. "Non-Parallel Yield Curve Shifts and Convexity." Transactions of the Society of Actuaries XLIV. (1993): 479-499.
  • Reitano, Robert R.. "Non-Parallel Yield Curve Shifts and Immunization." Journal of Portfolio management volume 18. quantity three (1992): 36-forty three.
  • Reitano, Robert R.. "Multivariate duration evaluation." Transactions of the Society of Actuaries XLIII. (1991): 335-376.
  • Reitano, Robert R.. "Multivariate Immunization idea." Transactions of the Society of Actuaries XLIII. (1991): 393-428.
  • Reitano, Robert R.. "Non-Parallel Yield Curve Shifts and spread Leverage." Journal of Portfolio administration volume 17. number three (1991): eighty two-87.
  • Reitano, Robert R.. "A Statistical evaluation of Banded facts with applications." Transactions of the Society of Actuaries XLII. (1990): 375-404.
  • Reitano, Robert R.. "Non-Parallel Yield Curve Shifts and Durational Leverage." Journal of Portfolio administration quantity sixteen. number four (1990): sixty two-67.
  • Reitano, Robert R.. "Mortality charge Valuation of Underwriting requirements." Transactions of the Society of Actuaries XXXIV. (1982): 277-322.
  • Awards & Honors:

  • (2017) Excellence in teaching Award for Full Time college: 2016-2017, overseas enterprise faculty.
  • (2008) Chartered business chance Analyst (CERA) credential awarded by means of the Society of Actuaries as attention for being a "concept leader" in the field.
  • (2007) Excellence in teaching Award for Full Time college: 2006-2007, overseas business school.
  • (1996) investment part of the Society of Actuaries1994-ninety five F.M. Redington Prize for "Multivariate Stochastic Immunization theory", Transactions of the Society of Actuaries, XLV, 1994.
  • (1994) investment part of the Society of Actuaries1991-93 F.M. Redington Prize for "Multivariate duration analysis", TSA, XLIII, 1991.
  • (1992) Society of Actuaries1991 Annual Prize for "Multivariate duration analysis", TSA, XLIII, 1991.

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    8002 II- Mathematical Foundations of Risk(R) Measurement

    Study Guide Prepared by Killexams.com PRMIA Dumps Experts


    Killexams.com 8002 Dumps and Real Questions

    100% Real Questions - Exam Pass Guarantee with High Marks - Just Memorize the Answers



    8002 exam Dumps Source : II- Mathematical Foundations of Risk(R) Measurement

    Test Code : 8002
    Test Name : II- Mathematical Foundations of Risk(R) Measurement
    Vendor Name : PRMIA
    Q&A : 132 Real Questions

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    Stability of core language skill from infancy to adolescence in typical and atypical development | killexams.com real questions and Pass4sure dumps

    Abstract

    Command of language is a fundamental life skill, a cornerstone of cognitive and socioemotional development, and a necessary ingredient for successful functioning in society. We used 15-year prospective longitudinal data from the Avon Longitudinal Study of Parents and Children to evaluate two types of stability of core language skill in 5036 typically developing and 1056 atypically developing (preterm, dyslexic, autistic, and hearing impaired) children in a multiage, multidomain, multimeasure, multireporter framework. A single core language skill was extracted from multiple measures at multiple ages, and this skill proved stable from infancy to adolescence in all groups, even accounting for child nonverbal intelligence and sociability and maternal age and education. Language skill is a highly conserved and robust individual-differences characteristic. Lagging language skills, a risk factor in child development, would profitably be addressed early in life.

    INTRODUCTION

    Early language skills merge into higher-order verbal and mental functioning (1) and so have predictive validity for the development of speech, grammar, reading, academic achievement, and intelligence (2–4). Language skills also predict behavioral adjustment in children (5, 6), even after controls for prior levels of behavior problems and taking into consideration children’s nonverbal intellectual functioning and performance, gender, and ethnicity, as well as their mothers’ verbal intelligence, education, parenting knowledge, and social desirability bias, and their families’ socioeconomic status (7, 8). Achievements in language and literacy open doors to education, occupation, income, and health (9).

    Individual differences are a central and manifest characteristic of child language (10–12), as children of the same chronological age vary dramatically in terms of their language skills. One fundamental conceptual issue that has framed debates about individual differences in theory and research across the history of language study and developmental science is their stability (13). Stability is consistency in individual differences over time. Stability in language therefore occurs when some children display relatively high levels of language at one point in time vis-à-vis their peers and continue to display high levels at a later point in time, while other children display consistently lower levels. Language is among the most complex skills a child must master, and so understanding individual differences in language and their developmental stability is of compelling interest to professionals, practitioners, and parents.

    Here, we distinguish and study two kinds of stability in child language. One is homotypic stability, maintaining individual rank order in the same characteristic measured in the same metric over time. In language, vocabulary size exemplifies a characteristic that might be indexed in the same way at different ages and show homotypic stability. The other is heterotypic stability, maintaining individual rank order on different manifest characteristics over time, where the different characteristics are theoretically related and presumed to share the same underlying construct. In language, the shared constructs, vocabulary size at one age and reading comprehension at a later age, might show heterotypic stability.

    The present study aims to advance our understanding of child language and its homotypic and heterotypic stability in several novel and substantial ways. (i) This study assesses stabilities of individual variation in language development in large and independent samples. (ii) The study begins earlier in life (6 months) and extends later in life (15 years) than ever before. (iii) The study follows a 13-wave granular longitudinal design that is unprecedented in developmental science. (iv) The study uses a wide range of age-appropriate language domains (from general communication and vocabulary in infancy and early childhood to spelling, reading, and narrative in later childhood and adolescence) broadly construed across a diversity of methods and measures. (v) The study evaluates homotypic and heterotypic stabilities in typically developing children as well as children in four at-risk groups: preterm children and children diagnosed with dyslexia, autism spectrum disorders (ASDs), and hearing impairment. Improved survival and diagnosis have meant markedly increasing numbers of very preterm children and children with ASD, respectively, in the community.

    To accomplish these several aims, we identified four major challenges to the study of language stability in children and reached solutions to each. The first two challenges were to capture the multiple domain, method, and measurement approaches to language and their changing age appropriateness. Here, we define “language” broadly to comprise many domains (including phonology, lexicon, grammar, pragmatics, reading, spelling, and narrative), and each can be assessed in multiple ways. Moreover, language differs phenotypically at different ages. The organizational perspective on development posits that the proper way to study development over time is to examine age-appropriate and therefore different, yet conceptually related, measures of the same underlying construct (14). In consequence, no single representation of language development across childhood is best, and no single approach to measurement can predominate. Rather, assessment selection must be guided by tradition, tractability, goal, convenience, and age appropriateness. Developmental scientists today advocate the wisdom of applying multiple assessments and using converging operations of different strategies targeted to the same phenomenon.

    Thus, the first two challenges are to identify sensitive, reliable measures of language with different contents derivable from varying methods and sources that track child age appropriately. We met these challenges, first, by implementing caregiver reports and direct assessments of multiple different aspects of children’s language and, second, by extracting shared variance among different measures using latent variables (15). Latent variables constitute a solution to the time-varying requirement of language development because they accommodate multiple age-appropriate indicators and different loadings for the same indicators on child language across age. Latent variables capture empirical covariation among indicators that may manifest differently at each age. Latent variables thereby identify what we call here core language skill. As stated, language assessment across a prolonged developmental timescale perforce entails dynamically changing (age-appropriate) measures. Procedurally, some methodologies can evaluate children directly (as in testing), but others must (or even best) rely on parental report because of the very young age of the child, the possibility of child reactivity to testing or observation, or the experienced and knowledgeable posture of the child’s caregiver. Substantively, some measures (grammar or literacy) may be appropriate only at certain ages, whereas others (vocabulary) may be applicable across multiple ages.

    The third challenge was to pinpoint stability “in” the child. Stability is often readily ascribed to temporal consistency of a characteristic in the individual. However, valid attribution necessitates simultaneous examination of factors that pervasively influence stability or confound its interpretation. To assess whether core language skill is stable in itself or if any of several third variables that covary with child language underlie stability in core language skill, we assessed and accounted for multiple candidate endogenous (child nonverbal intelligence and sociability) and exogenous (maternal age and education) variables.

    The fourth challenge was to evaluate the robustness of stability. Language is a sensitive and demonstrable indicator of human development. Biological risks of many kinds are known to perturb the normal acquisition of language. As reviewed in greater depth in the Supplementary Materials, mean-level differences in language are common in atypically developing preterm children and children with dyslexia, autism, and hearing impairment when compared to typically developing children. However, less is known about how biological risk alters language stability. It is possible that the mechanisms that produce mean-level group differences in language also generate variability in stability. For example, stability of language in children with a language disability, such as dyslexia, may be higher than stability in children without a disability because the processes that restrict language skills also maintain language-disabled children’s fixed order relative to one another. Here, we explored several biological and health moderators of language stability in children, including preterm birth, dyslexia, autism, and hearing impairment. Fuller discussions and justifications of the significance of stability, latent variables, and the moderators appear in the Supplementary Materials.

    RESULTS Study 1: Long-term language stability in typical development

    Study 1 evaluated long-term language stability in typical development in a 13-wave prospective longitudinal study that used data from the Children in Focus (CiF) group of the Avon Longitudinal Study of Parents and Children (ALSPAC) (16–18). The final study sample consisted of 925 (429 girls, 46.4%) white, term, monolingual singletons (M gestation = 39.75 weeks, SD = 1.29) free of dyslexia, autism, and hearing impairment. Mothers averaged 29.26 years (SD = 4.48; range, 14 to 43) at childbirth. This study sample was very diverse in terms of maternal education and social class. Table 1 presents the sample size and child age at each data collection wave.

    Table 1 Study 1: Sample size, child age, and language measures at each data collection wave.

    N represents the number of available observations. Except for wave 1 (child age in months), child ages are in years.

    Table 1 also presents language measures, scale scores, and sources used at each data collection wave. Table S1 shows the means, SDs, and ranges of the language measures and covariates for the total sample.

    We assessed the fit of a structural model to the data to assess the common convergence of multiple measures on single latent variables of the core language skill, where applicable, and the stability between those language variables (measurement models are discussed in the Supplementary Materials). The a priori model fit the data, scaled Yuan-Bentler (Y-B) χ2(618) = 2399.16, P < 0.001, robust comparative fit index (CFI) = 1.00, standardized root mean square residual (SRMR) = 0.11, and root mean square error of approximation (RMSEA) = 0.00. Figure 1 presents the standardized solution of this stability model. Although SRMR was greater than the usual cutoff of 0.09 (19), it is sensitive to estimation technique and sample size (20) and model complexity (21). Given the excellent fit indicated by the CFI and RMSEA as well as the large sample size and complex model, we deemed it acceptable. All indicators of child language loaded significantly on their factors at each age, which indicated that diverse measures of language formed stable, single factors of core language skill at each age. The stabilities of language were large between successive waves except for one medium-sized stability between the 6-month variable and year 1 factor.

    Fig. 1 Study 1.

    Standardized solution for stability model (N = 925). Numbers associated with single-headed arrows are standardized path coefficients; numbers associated with dotted single-headed arrows are error variances or disturbances, the amount of variance not accounted for by paths in the model. Indicators of each latent variable are listed below the latent variable with their factor loadings. †Marker indicators of the latent factors (loadings set to 1 to scale and identify the factor). Covariances that were in the model, but not shown in the figure, included year 2 MCDI vocabulary and RDLS comprehension, standardized coefficient = 0.22, P < 0.001; year 5 RDLS comprehension and Initial Consonant Detection Test, standardized coefficient = −0.26, P < 0.001; year 5 Bus Story information and Bus Story sentence length, standardized coefficient = 0.78, P < 0.001; and year 9 word and nonreal word reading, standardized coefficient = 0.39, P < 0.001. Correlations of 0.10, 0.30, and 0.50 correspond to small, medium, and large effect sizes, respectively (78).

    On the basis of an extensive body of research on constructs associated with child language (22–25), and to guard against threats to validity, we controlled for four prominent constructs that might affect child language stability: children’s nonverbal intelligence and sociability and mothers’ age and education. We then re-evaluated the stability model, taking into consideration these covariates. Figure S1 shows the final covariate model; it fit the data well: scaled Y-B χ2(670) = 2014.22, P < 0.001, robust CFI = 1.00, SRMR = 0.09, and RMSEA = 0.00. The attenuation of stability estimates ranged from 0.02 to 0.19 controlling for covariates. Stabilities of language were still medium to large between successive waves over the first 15 years of life.

    Study 2: Long-term language stability in typical and atypical development

    Study 2 replicated long-term language stability in an independent sample of typically developing children and evaluated long-term language stabilities in five atypically developing samples. The whole study sample consisted of 5167 (2594 girls, 50.2%) white, monolingual singletons. Mothers averaged 29.03 years (SD = 4.51; range, 15 to 44) at childbirth. This sample was also very diverse in terms of maternal education and social class. Of the 5167 children, 4111 were born term (M gestation = 39.78 weeks, SD = 1.29), were reported free of dyslexia and autism, and with tested bilateral normal hearing, served as the typically developing sample. Atypically developing samples included 435 moderate-late preterm (32 to 36 weeks’ gestation, M gestation = 35.05 weeks, SD = 1.19; range, 32 to 36) and 51 very preterm (<32 weeks’ gestation, M gestation = 28.92 weeks, SD = 1.75; range, 25 to 31) children, 322 children with dyslexia (M gestation = 39.53 weeks, SD = 1.80; range, 27 to 45), 89 children with autism (M gestation = 39.47 weeks, SD = 2.30; range, 27 to 42), and 221 children who had mild and/or moderate hearing impairment in one ear or in both ears (M gestation = 39.31 weeks, SD = 2.07; range, 27 to 42). Table S2 shows sample sizes and child ages for each group at each wave.

    Study 2 followed the same procedures, language measures, and covariates as those described in study 1, with three small exceptions: Children in study 2 were not tested in the Reynell Developmental Language Scales (RDLS) at 2 years 1 month, and they were not assessed for language at 4 or 5 years. Table S3 shows the means, SDs, and ranges of the language measures and covariates by groups.

    The a priori model for the whole sample fit the data, scaled Y-B χ2(344) = 7417.69, P < 0.001, robust CFI = 0.94, SRMR = 0.07, RMSEA = 0.045, and 90% confidence interval (CI) = 0.044 to 0.046. Figure S2 presents the standardized solution of this stability model. All indicators of child language loaded significantly on their factors at each age. The stabilities of language were medium to large between successive waves. Table 2 shows zero-order and partial correlations controlling for covariates between language measures across ages by group, and Table 3 shows point estimates of average stability and their 95% CIs for these correlations. Figure 2 depicts these average stabilities by group. In at-risk groups, all stabilities were medium or large except for the stabilities between 6 months and 1 year after accounting for covariates in moderate-late preterm children and in children with autism (Table 2). Most medium-sized stabilities were observed at the earliest ages, between 6 months and 1 year, and between 3 and 7 years across a longer 4-year time span. Atypically developing children’s language performance showed medium-to-large stabilities between successive waves over the span of 15 years, even accounting for child nonverbal intelligence and sociability and maternal age and education.

    Table 2 Study 2: Stability of language across age by groups.

    Numbers before the slashes represent correlations controlling for child age only (to control age variation within waves); numbers after the slashes represent correlations controlling for child age, nonverbal intelligence, sociability, and maternal age and education. Correlations of 0.10, 0.30, and 0.50 correspond to small, medium, and large effect sizes, respectively (78).

    Table 3 Study 2: Average stability of language by groups.

    Average stability represents the mean of correlation coefficients controlled for child age only. Average stability controlled for covariates represents the mean of partial correlations controlled for child age, nonverbal intelligence, sociability, and maternal age and education. The relatively small sample sizes in the very preterm and autism groups contributed to somewhat diminished precision in the point estimates of average correlation and, thus, wider 95% CIs.

    Fig. 2 Study 2.

    Average stabilities and their 95% CIs of language by group. Average stability represents the mean of correlation coefficients between language measures controlled for child age only. Average stability controlled for covariates represents the mean of partial correlations controlled for child age, nonverbal intelligence, sociability, and maternal age and education.

    DISCUSSION

    We investigated the longitudinal stabilities of child language from 6 months to 15 years using multiple age-appropriate methods, measures, and reporters, involving a wide variety of different language domains, in relatively large samples of typically developing children, as well as children born preterm and with childhood diagnoses of dyslexia, autism, and hearing impairment, in a prospective long-term microgenetic design. Individual differences tell us about the distribution of language skill, and their stability tells us about the nature and ontogeny of that language skill. We also tested whether a diverse set of controls for third variables and background characteristics accounted for stability in child language.

    With respect to the four challenges posed at the outset, clear evidence emerged for individual variation in a core language skill at each of 11 ages, for convergence of multiple indices of language at each age on latent variables representing a core language skill, for the homotypic and heterotypic stability of core language skill over the long term, and for the robustness of long-term stability of core language skill in atypically developing children with several different types of health risk.

    As with all developmental constructs, language (and its stability) is a joint product of biology and experience (26, 27). For example, a 2- to 12-year behavior genetics study identified genetic/biological and environmental/experiential sources of individual differences in developing language skills (28). Thus, a consistent personological characteristic, experience, or environment can carry stability. To address this point, we included child and maternal factors known to affect child language as covariates. Long-term stability was obtained separate and apart from both (nonlanguage) endogenous and exogenous covariates. The fact that stability of core language skill across so long a period began so early, was sustained so long, transcended several heterogeneous moderating factors, and was maintained over and above covariates points to a highly conserved and robust individual-differences characteristic in human beings. It further suggests that the search for mechanism(s) underlying stability of core language skill in children is likely to reward basic science as well as applied clinical research.

    Limitations to these study results include, among others, the heavy (if necessary) reliance on caregiver report in the early years and the limited (by necessity) number of language domains actually assessed relative to the possible number (see the Supplementary Materials). At three ages, only single language measures were collected; more varied early language measures, or having the same language measure assessed by multiple reporters, would strengthen the study. We did not measure (and so did not eliminate) all possible endogenous factors in children (brain function, motivation, and persistence), but we did measure and so controlled child age, nonverbal intelligence, and sociability as factors in stability. It is challenging to assess many aspects of language in very young children, and measurement of language at an early stage perforce cannot include all components of language (e.g., grammar). These data were also collected beginning in the 1990s; since then, the treatment of preterm and other at-risk children has changed. Except for hearing impairment, diagnoses of other atypicalities relied on maternal report. Because hearing impairment was measured only once, we do not know whether hearing loss persisted or whether it originated at birth or later in development.

    Nonetheless, these results prompt several notable considerations. First, a corollary of the prevailing multidimensional and componential conceptualization of language might be that phenotypically distinguishable language domains are independent of one another. Here, we confirmed that diverse indices of language deriving from different language domains, measures, methods, sources, and contexts, each of which showed individual variation, were positively associated across different ages (23, 29–34). On this basis, we could compute single latent variables of a core language skill at diverse ages. The significant amounts of variance accounted for by each latent variable at each age tested add to the validity of the stability model.

    Second, individual differences in core language skill were present from the first years of life, and so relatively stable individual differences in child language seem to be established early. However, the lowest observed stability coefficient occurred between 6 months and 1 year. As children aged past 1 year, there was more stability (less inconsistency) in language; that is, stabilities from 1 to 13 years were large. A characteristic may not be stable at one age in the life course but may stabilize at a later age. Generally, infancy and early childhood are thought to be less stable (or predictive) periods in life (35), and people are thought to become increasingly consistent in relation to one another as they age (36, 37). Strong stability after 1 year implies that changes among children in their relative rank in core language skill later in development are rare. By contrast, the smaller stability coefficient between 6 months and 1 year indicates that nearly 90% of the variance in 1-year core language skill is not explained by 6-month language. This difference suggests that core language skill is relatively more malleable in early life. It is also possible that lower stability early in life reflects the difficulty in validly assessing language in preverbal infants. In general, however, our findings underscore the importance of identifying lagging language skills early in life and promoting the child’s language environment well before formal schooling as a means to enhancing language skill.

    Large stability coefficients can mislead researchers and practitioners to conclude that language skill in children is set in infancy or toddlerhood. This is not necessarily the case. Stability is a key developmental barometer, but to be stable does not mean to be immutable or impervious to change or intervention. Focusing solely on stability in language overlooks or minimizes dramatic and normative developmental changes in mean level of language. The life-span perspective in developmental science specifies that human beings are open systems, and the plastic nature of psychological functioning ensures both consistency and change across the life course (38). The language skills of individual children (relative to their peers) still shift across time, and even large relative stability leaves significant amounts of common variance unaccounted for. Language is ultimately modifiable by experience or intervention. In language acquisition, development appears to balance the advantages of stability with the adaptive value of early susceptibility to experience.

    A third contribution of this study distinguishes homotypic stability (as of vocabulary between year 1 and year 15) from heterotopic stability (as between vocabulary in year 1 and literacy in year 13). The measures, reporters, and contexts for language sampling at the different ages perforce differed. From one point of view, this procedural variation attenuates stability. That is, heterotypic stability between different individual indices of child language likely represents lower-bound estimates of stability considering differences in assessment measures and procedures used at different times. Thus, heterotypic stability is conservative and probably underestimates true stability. By contrast, homotypic stability of identical measures and of latent variables (as we used here) may more closely approximate true stability in language development. Nonetheless, the heterotypic approach to stability assessment is faithful not only to a developmental perspective but also to a systems perspective on the hierarchical integration of lower-order into high-order abilities with development (1). Literacy, the end goal of our assessments, is conventionally understood as the ability to read, write, spell, listen, and speak (39), but encompasses a progression of skills that begins with comprehension and expression of sounds and then words and culminates with grammar and reading and writing. The latent variable solution to the challenge of heterotypicality is therefore valuable to developmental science in general. An additional point with respect to heterotypic stability was the small associations uncovered in both studies between nonverbal measures of intelligence and language. Of course, to be able to perform in an evaluation of even a nonverbal assessment requires some language ability (if only to be able to follow instructions), and the history of general psychological testing and specific intelligence testing tells us that nonverbal and verbal components of cognition are not strictly independent. For example, the Bayley Scales of Infant Development (40) have a mental development index and a psychomotor development index that correlate, and the Wechsler (41) series of intelligence tests have verbal and performance intelligence quotient (IQ) indices that correlate. In our case, as is also typical, the shared variance in language and nonverbal intelligence measures was small (study 1 range, 2 to 25%; study 2 range, 6 to 21%).

    The fourth contribution of this study is analyses of stabilities of individual differences in the language of large numbers of children identified as preterm, dyslexic, autistic, and hearing impaired. From very early in development, core language skill was stable in each group. The findings therefore have implications for psycholinguists and psychologists, pediatricians and psychiatrists, practitioners and professors, and parents and the public. All stakeholders should be aware that very young children who perform poorly relative to their peers are likely to continue to perform poorly at later ages, which reinforces the desirability of early assessment of language performance and the need for early intervention. Our data suggest that core language skill anticipates verbal and literacy achievements as child development unfolds. Through regular well-child checkups, pediatricians could identify children who have lagging language skills and connect them to early intervention services.

    Given the increasing importance of replication in science (42, 43), it is noteworthy that the results of the present studies internally replicate and then extend previous studies with single or fewer language measures taken over shorter periods of time (24).

    Last, the present empirical findings articulate with clinical practice; we distinguish between language screening and the accuracy of the multiple domain, measure, and source latent variable approach. Clinically, our approach to estimating child language as latent variables is not a quick tool for early diagnosis or screening; most clinicians do not have the benefit of a rich array of measures or the technical support at hand to estimate latent variables. A screening instrument may be practically valuable, but the latent variable provides a more fundamental understanding of the core construct. Nonetheless, a multimeasure approach to child language has been applied productively in the past for predicting continued language delay (44–47). Notably, interventions that enhance language skills also improve behavioral regulation in children (48–50).

    MATERIALS AND METHODS Study 1: Long-term language stability in typical development

    Participants. The ALSPAC is a prospective, population-based, longitudinal transgenerational observational study investigating influences on health and development across the life course. All births in the former Avon Health Authority with an expected date of delivery between 1 April 1991 and 31 December 1992 were eligible. Of the initial 14,541 pregnancies, there were a total of 14,676 fetuses, resulting in 14,062 live births and 13,988 children who were alive at 1 year of age. Because only 2.6% of the ALSPAC sample was non-White (of those participants who provided this data point), and this group was heterogeneous (0.9% Asian, 1.0% Black, and 0.7% other), we focused on the majority group (51, 52). From the 12,075 White European participants in the ALSPAC data, the following exclusion criteria were followed (some children might fall into multiple categories): Children who (i) were twins (n = 306), (ii) were born preterm (born less than 37 weeks, n = 677), (iii) were hearing impaired (n = 233), (iv) had dyslexia (n = 332), (v) were diagnosed with autism (n = 91), and (vi) were bilingual or spoke a language other than English as their main language (n = 280) were excluded, resulting in 9794 children. An additional 8869 children were excluded from study 1 because they were not from the CiF cohort and/or did not have the additional CiF assessments used in the current study. Children who did not fall into the exclusion criteria and provided data at any of the data collection waves were included in study 1.

    Maternal education, collected at 32 weeks of pregnancy as an ordinal variable according to increasing levels of achievement, was varied: certificate of secondary education (13.0%), vocational (10.8%), O level (35.2%), A level (25.9%), and university degree (15.1%). Maternal social class ranged from unskilled (2.0%), partly skilled (7.8%), skilled manual (7.2%), skilled nonmanual (42.8%), managerial and technical (33.8%), to professional (6.4%).

    Procedures. Child language data were derived from caregiver reports and direct child assessments by trained psychologists during research clinics. The ALSPAC study website includes descriptions of measures used and scoring methods. In addition to the language measures detailed below, caregivers completed questionnaires that supplied demographic information about children’s health status, family language, and the like.

    Language assessments. We used data collected across 13 ALSPAC collection waves (Table 1). However, we aggregated data collected at ages 1 year 3 months and 1 year 6 months (two waves) into year 1 measures, and those collected at ages 2 years and 2 years 1 month (2 waves) into year 2 measures; thus, we studied 13 waves but calculated stability across 11 ages.

    Under 1 year

    Caregiver report: At age 6 months, caregivers completed an ALSPAC-modified Denver Developmental Screening Test (hereinafter referred to as modified DDST) (53) adapted for caregiver completion. The “communication” scores were used.

    Year 1

    Caregiver report: At age 1 year 3 months, the understand, vocabulary, and social (nonverbal) communication scores on the ALSPAC-modified MacArthur Communication Development Inventories (hereinafter referred to as modified MCDI) (11) Words and Gestures were used. Twelve of the 28 questions (called “phrases” on the original MCDI) were asked for the understand scale, and the vocabulary checklist was cut by removing entire sections (i.e., toys, small household items, people, action words, words about time, pronouns, question words, prepositions and locations, and quantifiers), as well as some items from other sections from the original MCDI. Furthermore, some American English items were adapted or replaced with the British English equivalents (e.g., lorry instead of truck; sweater or jumper). Ten of the 12 social communication items (called “first communicative gestures” on the original MCDI) were asked. See the Supplementary Materials for more details about the modified MCDI.

    Year 2

    Caregiver report: At age 2 years, the vocabulary, grammar, plurals, and tense scores on the modified MCDI Words and Sentences were used. The vocabulary checklist was cut by removing sound effects and animal sounds, small household items, and connecting words as well as by removing items from other sections from the original MCDI. The 4 grammar items (called “word endings” on the original MCDI), 5 irregular plurals (called “word forms—nouns” on the original MCDI), and 20 past tense (called “word forms—verbs” on the original MCDI) items were unmodified.

    Direct assessment: At age 2 years 1 month, children in the CiF cohort were administered the RDLS (54). The RDLS comprehension scale measures a child’s verbal comprehension by administering a series of activities where the child is asked to respond to and carry out a series of spoken tasks. The raw score was used.

    Year 3

    Caregiver report: At age 3 years 2 months, the vocabulary, plurals, past tense, and word combination scores on the modified MCDI Words and Sentences were used. The vocabulary checklist was the same as the one used at year 2. The 5 irregular plurals (called “word forms—nouns” on the original MCDI) and 20 past tense (called “word forms—verbs” on the original MCDI) items were unmodified. In addition, two items that ask whether the child uses plurals by adding “-s” to the end of words or uses past tense by adding “-ed” to the end of words were included in the plurals and past tense scales, respectively. The word combination items were modified from the 14 “complexity” items of the original MCDI. Two items were dropped, and some items were adapted to add a third option (e.g., two feet, two foots, two foot) or to change the object (e.g., “that’s my book” versus “that’s my truck”).

    Year 4 (CiF cohort only)

    Direct assessment: At age 4 years 1 month, four verbal subscale scaled scores (M = 10 and SD = 3) of the Wechsler Preschool and Primary Scale of Intelligence—Revised UK Edition (WPPSI) (41) were used: information, comprehension, vocabulary, and similarity.

    Year 5 (CiF cohort only)

    Direct assessment: At age 5 years 1 month, the Bus Story Test (55), a screening test of verbal expression, was administered. The assessment involves children listening to a spoken narrative about a bus, accompanied by pictures depicting the events that occur in the story. Children then retell the story with the pictures as support. The child’s narrative is recorded orthographically and scored for information content (number of relevant pieces of information given) and sentence length (mean sentence length of the five longest sentences). In addition, the same RDLS comprehension scale that was used at 2 years 1 month was repeated at age 5 years 1 month. Last, the Initial Consonant Detection Test (56) asked children to identify which two of three words illustrated by line drawings began with the same initial consonants. A total of 10 trials were given, and the number of correct responses was recorded.

    Year 7

    Direct assessment: At age 7 years 6 months, reading was assessed with measures on the basis of the Wechsler Objective Reading Dimensions (WORD) (57). Pictures and words were used to assess decoding and word reading. The child was shown a series of four pictures. Each picture had four short, simple words underneath it. The child was asked to point to the word that had the same beginning or ending sound as the picture. This request was then followed by a series of three pictures, each with four words beneath, each starting with the same letter as the picture. The child was asked to point to the word that correctly named the picture. The child was then asked to read aloud a series of 48 unconnected words that increased in difficulty. Total numbers of correct responses were used. Spelling was assessed by a series of 15 words that were piloted and chosen by the ALSPAC team (e.g., chin, brought, and telephone). Each word was read aloud on its own, within a specific sentence incorporating the word, and lastly read alone again. The child was asked to write down the spelling of the word even if he or she was just guessing. The total number of words spelled correctly was tallied and used. In addition to the WORD, the Phoneme Detection Task (58) comprised 40 test items of increasing difficulty. It involved asking the child to repeat a word and then to say it again, but with some part of the word (a phoneme or number of phonemes) removed. Total numbers of correct responses were used.

    Year 8

    Direct assessment: At age 8 years 6 months, four verbal subscale raw scores on the Wechsler Intelligence Scale for Children—III UK Edition (WISC) (59) were used: information, comprehension, vocabulary, and similarity. Two subtests of the Wechsler Objective Language Dimensions (WOLD) (60) were used to measure listening comprehension and oral expression. Listening comprehension involves the child listening to the tester reading aloud a paragraph about a displayed picture. The child then answers questions on what was heard. The child has to make inferences about what was read to them and answer the questions verbally. Expressive vocabulary was assessed by a series of 10 pictures. The total numbers of correct responses on comprehension and expressive vocabulary were each tallied and used.

    Year 9

    Direct assessment: At age 9 years 6 months, reading was assessed using the basic reading subtest of the WORD (57). Children were asked to read aloud 10 real words (e.g., huge, union, and unusual), followed by 10 nonreal words (e.g., duter, uningest, and smape). Both the real and nonreal words were selected from a larger list of words taken from research conducted by Nunes et al. (61). Total numbers of correct responses on real and nonreal words were each tallied and used. The revised Neale Analysis of Reading Ability (NARA II) (62) was used to assess children’s reading skills and comprehension. In this test, children read aloud short passages of stories that resulted in an accuracy score, and their answers to a series of questions about the content of the story resulted in a reading comprehension score.

    Year 13

    Direct assessment: At age 13 years 6 months, word reading efficiency was assessed by word and pseudoword tests of the Test of Word Reading Efficiency (TOWRE) (63). Children were asked to read out loud 104 real words (e.g., complete and wonderful), followed by a list of 63 nonreal words (e.g., glack and framble). Total numbers of correct responses on real and nonreal words were tallied; because of a very high correlation between the two reading scores, r = 0.81, a mean standard score was computed and used in analysis.

    Year 15

    Direct assessment: At age 15 years 6 months, the vocabulary subscale raw score on the Wechsler Abbreviated Scale of Intelligence (WASI) (64) was used.

    Covariates. We assessed the possibility that child nonverbal intelligence (29) and sociability (65), both of which are known to be associated with child language, and mothers’ age and education, both of which are also known to be associated with child language, would account for some of the stability of language competence and performance. Specific covariates (child nonverbal intelligence and sociability) were presumed to be associated with child language variables concurrently or prospectively (but not retrospectively). General covariates (maternal age and education) were presumed to be associated with all child language variables, regardless of the child’s age.

    Children’s nonverbal intelligence was assessed three times at clinic visits. At age 4 years 1 month, the performance IQ score of the WPPSI (41) was used. At age 8 years 6 months, the performance IQ score of the WISC (59) was used. At age 15 years 6 months, nonverbal intelligence was measured by the Matrix Reasoning subtest of the WASI (64).

    Child sociability was obtained from caregiver reports across data collection waves. At ages 6 months, 1 year 6 months, and 2 years 6 months, the social achievement scores of the adapted DDST (53) were used. At ages 3 years 2 months, 4 years 9 months, and 5 years 9 months, the sociability scores from the Emotionality, Activity, Sociability Temperament questionnaire (66, 67) were used.

    Maternal age at childbirth was calculated from the date of delivery and the mother’s date of birth recorded at enrollment. Educational attainment was obtained from a questionnaire sent home at 32 weeks gestation.

    Statistical analysis. The SDs and ranges of all language measures (table S1) indicated considerable variation, as is common in the literature and prerequisite to assessments of stability. Variable distributions were examined for univariate normality (68), and transformations were applied to improve distributions. Because of the range of child age at each wave, we explored correlations of child age with all raw test scores to determine whether age adjustment was warranted. Age-adjusted scores were computed for all language variables that showed significant concurrent correlations with child age and were used in structural equation models (SEMs).

    Language stability was evaluated by fitting SEMs using maximum likelihood functions (MLFs) and followed the mathematical models of Bentler and Weeks (69), as implemented in EQS 6.1 (70). SEM is a robust tool for assessing stability because latent variables capture shared variance among their indicators, and so variance uniquely associated with rater bias, random measurement error, or specific error (variance arising from some characteristic unique to a particular indicator that was not accounted for by the factor) is relegated to its error term.

    Missing data points (20.4% of the total data) were handled in EQS using full information maximum likelihood with a two-stage Expectation-Maximization estimation of the structured model and the MLF (71). Monte Carlo studies have demonstrated the general superiority of the structured-model EM method implemented in EQS 6.1 compared to other techniques to recover missing data (72, 73). In the course of fitting SEMs, we evaluated Mardia (74) coefficients of multivariate kurtosis and the cases that contributed most to those estimates, as well as the stability of parameter estimates and the cases that contributed disproportionately to parameter estimates. No significant problems with influential cases emerged. Model fit was assessed using scaled Y-B χ2 statistic, robust CFI, standardized SRMR (75), and RMSEA. Cutoff values ≈0.95 for CFI and ≈0.09 and ≈0.06 for SRMR and RMSEA, respectively, are indicative of a relatively good fit between the hypothesized model and observed data (21). We gave greater weight to the incremental/approximate fit indices than to χ2 because the χ2 value is known to be sensitive to sample size (76) and the size of the correlations in the model (77). Standardized path coefficients are presented in text and figures.

    For correlations and standardized path coefficients, we adopted conventional magnitudes of r corresponding to small, medium, and large effect sizes as ≈0.10, 0.30, and 0.50, respectively (78, p. 61). All stabilities were large except two medium-sized stabilities between 6 months and year 1 and between the single observed variables at 13 and 15 years.

    Next, we explored whether specific covariates were associated with child language measures. We calculated correlations of (i) year 4 WPPSI performance IQ with years 4, 5, 6, and 7 language variables; (ii) year 8 WISC performance IQ with years 8, 9, and 13 language variables; (iii) year 15 WASI matrix reasoning with vocabulary; (iv) child sociability with concurrent language variables from ages 6 months through year 5; and (v) year 5 child sociability with all language variables from years 7 through 15. Children’s nonverbal intelligence significantly correlated with all language variables (r values ranged from 0.15 to 0.50, all P ≤ 0.001); however, children’s sociability related to only some language variables, with significant correlations ranging from 0.07 (P < 0.05) to 0.46 (P < 0.001). To test whether the stability model held controlling for specific covariates and the two general covariates, we re-evaluated the a priori model (Fig. 1) using the adjusted language scores with the shared variance with specific covariates removed and adding the two general covariates as exogenous variables to the SEM. Direct paths from maternal age and education to all eight language-latent variables, and the three observed variables at 6 months and 13 and 15 years, were added to the model.

    Study 2: Long-term language stability in typical and atypical development

    Participants. Table S2 presents sample sizes and child ages at each data collection wave by groups. Maternal education ranged from secondary education (13.2%), vocational (8.5%), O level (36.3%), A level (26.4%), to university degree (15.6%). Maternal social class ranged from unskilled (1.4%), partly skilled (7.7%), skilled manual (6.3%), skilled nonmanual (42.6%), managerial and technical (35.4%), to professional (6.6%).

    At age 7, hearing function was assessed using air conduction pure tone audiometry carried out by audiologists and trained physiology staff during a clinic visit. All measurements were carried out as described in Hall et al. (79), and hearing thresholds were measured in both ears according to audiometry procedures recommended by the British Society of Audiology (80). At age 9, children’s primary caregivers were asked whether they were ever told that the child has “dyslexia” or “autism,” “Asperger’s syndrome,” or “autistic spectrum disorder.”

    Procedures. We used data collected across 10 ALSPAC collection waves. However, we aggregated data collected at ages 1 year 3 months and 1 year 6 months (2 waves) into the year 1 measures; thus, we studied language stability across nine ages (fig. S2).

    Statistical analysis. The SDs and ranges of all language measures (table S3) again indicate considerable variation. A language stability model was fit on the total sample by using SEM with EQS 6.1 (70). See study 1 for SEM applications. Age-adjusted scores were used in analysis, and missing data points (11.7% of the total data) were handled in EQS using full information maximum likelihood. In the full-sample stability model (fig. S2), all stabilities were medium or large.

    Given the complexity of the stability model and the relatively small sizes of the at-risk comparison groups, we generated the generalized least squares factor scores from the SEM and retained them for further analysis. Study 2 language stability of the at-risk subgroups was assessed using Pearson correlation coefficients. Missing data points for the three observed language variables at 6 months and 13 and 15 years were imputed in the control and in each at-risk group (missingness ranged from 7.9 to 17.8% of the total data) separately using the Expectation-Maximization algorithm (81) in SPSS (82). First, zero-order correlations showed language stability before taking specific and general covariates into consideration. Then, language stability was reassessed using partial correlations controlling for general and specific covariates. For specific covariates, we first calculated correlations of (i) year 8 WISC performance IQ with years 8, 9, and 13 language variables; (ii) year 15 WASI matrix reasoning with vocabulary; (iii) child sociability with concurrent language variables from ages 6 months through year 3; and (iv) year 3 child sociability with all language variables from years 7 through 15. Again, child nonverbal intelligence significantly correlated with all language variables (r values ranged from 0.25 to 0.46, all P < 0.001), and child sociability related to only some language variables with significant correlations ranging from 0.03 (P < 0.05) to 0.51 (P < 0.001). Unstandardized residuals of the related language variables controlling for significant specific covariates, where applicable, were computed before performing partial correlations. Last, language stability was reassessed by computing partial correlations of adjusted language scores (with the shared variance with specific covariates removed) controlling for maternal age and education.

    SUPPLEMENTARY MATERIALS

    Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/4/11/eaat7422/DC1

    Study 1 Supplementary Text

    Study 1 Materials and Methods

    Study 1 Measurement Models

    Study 2 Supplementary Text

    Table S1. Study 1: Child language measures and covariates: Descriptive statistics.

    Table S2. Study 2: Sample size and child age at each data collection wave.

    Table S3. Study 2: Child language measures and covariates: Descriptive statistics.

    Fig. S1. Study 1.

    Fig. S2. Study 2.

    References (83–144)

    This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.

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  • Acknowledgments: We are grateful to all the families who took part in this study, the midwives for help in recruiting them, and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists, and nurses. Funding: The UK Medical Research Council and Wellcome (grant ref. 102215/2/13/2) and the University of Bristol provide core support for ALSPAC. This publication is the work of the authors, and M.H.B. will serve as guarantor for the contents of this paper. This research was also supported by the Intramural Research Program of the NIH/NICHD, USA, and an International Research Fellowship in collaboration with the Centre for the Evaluation of Development Policies (EDePO) at the Institute for Fiscal Studies (IFS), London, UK, funded by the European Research Council (ERC) under the Horizon 2020 research and innovation programme (grant agreement no. 695300-HKADeC-ERC-2015-AdG). Author contributions: M.H.B., C.-S.H., and D.L.P. conceptualized the study. R.M.P. curated the data. C.-S.H. analyzed the data. M.H.B. and C.-S.H. wrote the original draft of the manuscript. D.L.P. and R.M.P. reviewed and edited the manuscript. Competing interests: The authors declare that they have no competing interests. Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Data used for this submission will be made available on request to the Executive (alspac-exec{at}bristol.ac.uk). The ALSPAC data management plan describes in detail the policy regarding data sharing, which is through a system of managed open access.


    Voya Financial Announces Updated Financial Targets, Including Plans for Significant Growth in Adjusted Operating Earnings Per Share | killexams.com real questions and Pass4sure dumps

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    -- Adjusted operating earnings per share (EPS) growth of at least 10% annually through 2021, on a normalized1 basis, to be driven by organic growth, cost savings and capital deployment -- Organic growth: Voya's core businesses -- Retirement, Investment Management and Employee Benefits -- to drive high quality and sustainable earnings; expected total free cash flow conversion of 85% to 95% -- Cost savings: Voya is targeting an additional $100 million of run-rate savings, incremental to expense savings previously announced, expected by the end of 2020 -- Capital deployment: Continued share repurchases planned; Voya's common stock dividend expected to increase to achieve at least a 1% dividend yield by mid-2019 -- Adjusted operating earnings per share growth to be achieved while maintaining strong returns and generating an enterprise operating return on equity of 13% to 15%2 -- Management to Discuss Growth Plans and Financial Targets at Investor Day NEW YORK--(BUSINESS WIRE)--November 13, 2018--

    Voya Financial, Inc. (NYSE: VOYA), announced today its plans to achieve an adjusted operating earnings per share (EPS) annual growth rate of at least 10% (on a normalized basis) through 2021. The company expects to achieve its plan through a combination of organic business growth, cost savings and share repurchases. Senior management will discuss further details about Voya's growth strategy at the company's 2018 investor day meeting beginning at approximately 1 p.m. ET today.

    "With our competitive advantages and renewed focus on our chosen businesses, Voya Financial is poised to achieve significant earnings per share growth of at least 10% annually over the next three years," said Rodney O. Martin, Jr., chairman and chief executive officer of Voya Financial, Inc. "Importantly, we believe that we can generate this level of adjusted operating EPS improvement while also achieving an operating return on equity of between 13% and 15%."

    Organic Growth

    "The foundation of our plans starts with our higher-growth, higher-return, capital-light Retirement, Investment Management and Employee Benefits businesses. With our established positions in the workplace and with institutions, we will continue to expand our offerings to existing clients, while also attracting new customers. In addition to executing on growth plans that are specific to each business, we will continue to leverage opportunities to deliver holistic, comprehensive solutions across our businesses. With our significant reach and strong brand awareness, we are well positioned to help our customers achieve their goals with confidence," Martin added.

    On a normalized basis, Voya expects its Retirement business to grow earnings by 4% to 7%; Investment Management to grow earnings by 5% to 8%; and Employee Benefits to grow earnings between 7% and 10%, in each case annually through 2021. Voya will also continue to benefit from earnings in its Individual Life segment, which Voya recently announced will cease new business sales effective Dec. 31, 2018.

    "Voya has a favorable profile and diverse businesses that are expected to generate high-quality and sustainable earnings as well as significant free cash flow. Specifically, we expect total free cash flow conversion of 85% to 95%, which includes the benefit of improved free cash flow from our Individual Life segment," said Martin.

    Cost Savings

    "Over the past several years, we have demonstrated our ability to execute and become more efficient as we've narrowed our focus and simplified our company. We have also leveraged our Continuous Improvement program to achieve efficiencies and advance operational excellence. At the same time, we have invested in our company, ensuring that we are well positioned to grow.

    "Based on our track record, we believe that we can achieve an additional $100 million of cost savings by the end of 2020 -- this is in addition to our previously shared target of $110 to $130 million of cost savings by the middle of 2019 and the $20 million of expected savings from our recent decision to cease sales of individual life insurance," added Martin.

    Capital Deployment

    "We will continue to be good stewards of shareholder capital and build upon the nearly $5 billion in excess capital that we have returned to shareholders via stock buybacks. With an expected strong free cash flow that will support excess capital generation, our capital deployment plans will continue to emphasize share repurchases.

    "In addition to share repurchases, we also intend to increase our common stock dividend to a dividend yield of at least 1% by mid-2019. The board of directors will determine the appropriate level of dividend as we continue to execute on share repurchases at these attractive valuation levels. As we advance our plans next year and beyond, we may further grow the dividend to deliver even greater shareholder value as well as attract new investors seeking a higher-yielding dividend," said Martin.

    "We are excited about our plans and are committed to building upon our financial, operational and cultural improvements over the past few years as we focus on achieving our vision to be America's Retirement Company," concluded Martin.

    Webcast and Slide Presentation

    Voya Financial will host an audio and video webcast of its 2018 investor day beginning at approximately 1 p.m. ET today. The webcast, which will include a slide presentation, will be available live via the internet and can be accessed at investors.voya.com. Participants should join the webcast at least 15 minutes prior to the start of the event to download and install any necessary software. A replay of the webcast will be available at investors.voya.com starting at approximately 10 a.m. ET on Wednesday, Nov. 14, 2018.

    About Voya Financial(R)

    Voya Financial, Inc. (NYSE: VOYA), helps Americans plan, invest and protect their savings -- to get ready to retire better. Serving the financial needs of approximately 14.3 million individual and institutional customers in the United States, Voya is a Fortune 500 company that had $8.6 billion in revenue in 2017. The company had $543 billion in total assets under management and administration as of September 30, 2018. With a clear mission to make a secure financial future possible -- one person, one family, one institution at a time -- Voya's vision is to be America's Retirement Company(R) . Certified as a "Great Place to Work" by the Great Place to Work(R) Institute, Voya is equally committed to conducting business in a way that is socially, environmentally, economically and ethically responsible. Voya has been recognized as one of the 2018 World's Most Ethical Companies(R) by the Ethisphere Institute; one of the 2018 World's Most Admired Companies by Fortune magazine; as a member of the Bloomberg Gender Equality Index; and as a "Best Place to Work for Disability Inclusion" on the Disability Equality Index by Disability:IN. For more information, visit voya.com. Follow Voya Financial on Facebook, LinkedIn and Twitter @Voya.

    Forward-Looking and Other Cautionary Statements

    This press release contains forward-looking statements. Forward-looking statements include statements relating to future developments in our business or expectations for our future financial performance and any statement not involving a historical fact. Forward-looking statements use words such as "anticipate," "believe," "estimate," "expect," "intend, " "plan," and other words and terms of similar meaning in connection with a discussion of future operating or financial performance. Actual results, performance or events may differ materially from those projected in any forward-looking statement due to, among other things, (i) general economic conditions, particularly economic conditions in our core markets, (ii) performance of financial markets, including emerging markets, (iii) the frequency and severity of insured loss events, (iv) mortality and morbidity levels, (v) persistency and lapse levels, (vi) interest rates, (vii) currency exchange rates, (viii) general competitive factors, (ix) changes in laws and regulations, such as those relating to Federal taxation, state insurance regulations and NAIC regulations and guidelines, including those affecting reserve requirements for variable annuity policies and the use of and possible application of NAIC accreditation standards to captive reinsurance entities, those made pursuant to the Dodd-Frank Wall Street Reform and Consumer Protection Act, and the U.S. Department of Labor's final rules and exemptions pertaining to the fiduciary status of providers of investment advice, or any amendments thereto, (x) changes in the policies of governments and/or regulatory authorities, and (xi) our ability to successfully manage the separation of the fixed and variable annuities business that Voya sold to VA Capital LLC on June 1, 2018, including the transition services, on the expected timeline and economic terms. Factors that may cause actual results to differ from those in any forward-looking statement also include those described under "Risk Factors" and "Management's Discussion and Analysis of Results of Operations and Financial Condition - Trends and Uncertainties" in our Annual Report on Form 10-K for the year ended Dec. 31, 2017, which the company filed with the Securities and Exchange Commission on Feb. 23, 2018 and in our Quarterly Report on Form 10-Q for the three-month period ended Sept. 30, 2018, which the company filed with the Securities and Exchange Commission on Nov. 1, 2018.

    VOYA-IR

    ______________________________ (1) When presented on a "normalized" basis, amounts are adjusted to exclude (i) unlocking of deferred acquisition costs, value of business acquired, and other intangibles, (ii) prepayments and alternatives income to the extent such income is above or below our long-term expectations, and (iii) in the case of 2018 financial results against which Adjusted EPS growth targets are measured, investment management adjusted operating earnings associated with the fixed and variable annuities business that Voya sold to VA Capital LLC on June 1, 2018. (2) Operating return on equity excludes deferred tax assets (including AMT receivables) and accumulated other comprehensive income but includes all other sources of shareholders' equity under GAAP. In distinction to the definition of adjusted operating return on equity used by the company from 2013 -- 2017, this measure includes the operating results of all company segments, including its Corporate segment.

    View source version on businesswire.com: https://www.businesswire.com/news/home/20181113005998/en/

    CONTACT: Voya Financial, Inc.

    Media:

    Christopher Breslin, 212-309-8941

    christopher.breslin@voya.com

    or

    Investors:

    Michael Katz, 212-309-8999

    IR@voya.com

    SOURCE: Voya Financial, Inc. Copyright Business Wire 2018

    Campbell Soup Company (CPB) CEO Keith McLoughlin on Q1 2019 Results - Earnings Call Transcript | killexams.com real questions and Pass4sure dumps

    Campbell Soup Company (NYSE:CPB) Q1 2019 Results Earnings Conference Call November 20, 2018 10:00 AM ET

    Executives

    Ken Gosnell - VP, Finance Strategy and IR

    Keith McLoughlin - Interim President and CEO

    Anthony DiSilvestro - SVP and CFO

    Analysts

    Bryan Spillane - Bank of America

    David Driscoll - Citi

    Chris Growe - Stifel

    Ken Goldman - JPMorgan

    Robert Moskow - Credit Suisse

    Steve Strycula - UBS

    Andrew Lazar - Barclays

    Jonathan Feeney - Consumer Edge

    Operator

    Good day, ladies and gentlemen, and welcome to the Campbell Soup First Quarter 2019 Earnings Call. [Operator Instructions] As a reminder, this conference call may be recorded.

    I would now like to introduce your host for today's conference, Mr. Ken Gosnell, Vice President, Finance Strategy and Investor Relations. Sir, you may begin.

    Ken Gosnell

    Thank you, Crystal. Good morning, everyone. Welcome to the first quarter earnings call for Campbell Soup's fiscal 2019. With me here in New Jersey are Keith McLoughlin, Interim CEO; and Anthony DiSilvestro, CFO.

    As usual, we've created slides to accompany our earnings presentation. You will find the slides posted on our website this morning at investor.campbellsoupcompany.com. This call is open to the media, who participate in a listen-only mode.

    Today, we will make forward-looking statements, which reflect our current expectations. These statements rely on assumptions and estimates, which could be inaccurate and are subject to risk. Please refer to Slide 2 or our SEC filings for a list of factors that could cause our actual results to vary materially from those anticipated in forward-looking statements. Because we use non-GAAP measures, we have provided a reconciliation of these measures to the most directly comparable GAAP measure, which is included in the appendix of this presentation.

    With that, I'll turn the call over to Keith McLoughlin, Interim President and CEO. Keith?

    Keith McLoughlin

    Thanks, Ken, and good morning, everyone.

    Today, we will discuss the progress we've made executing the significant actions we announced on August 30, following our comprehensive Board led strategy and portfolio review and in that context, review our first quarter results. As we stated at that time, fiscal 2019 will be a transition year for Campbell, as we take steps to turn around the Company and the year-over-year results we reported today reflect that.

    This morning, I will give you an overview of the steps we're taking to implement our new strategy and in that context, share my perspective on our performance. Then, our Chief Financial Officer, Anthony DiSilvestro, will walk through the financial details of the quarter and our fiscal 2019 guidance which we reaffirmed today.

    Moving to Slide 4. As you'll recall, we announced on May 18, that the Board was launching its own strategy and portfolio review process, one with outside advisors and which all options were on the table. Together with those advisors, we evaluated a full slate of potential options for Campbell, including optimizing our portfolio and divesting assets, splitting the company in two and selling the entire company.

    After considerable analysis and evaluation and as discussed on August 30, the Board concluded that at this time, the best path forward to maximize shareholder value and maintain flexibility going forward is a three-pronged strategy. First, optimize our portfolio and focus on our core businesses with an emphasis on execution; two, divest certain non-core businesses in order to focus the Company, while significantly paying down debt; and three, to increase our successful multi-year cost saving efforts, while driving improved asset efficiency.

    We have not wasted time since then and we're doing what we said we would do. We are actively making Campbell a highly focused company that is built around our two core North American businesses, Snacks and Meals and Beverages. These are strong businesses, where we have the right to win with franchise brands, best-in-class product and significant market positions.

    I will get into more specifics about each business in a moment, but I want to share a specific example of the greater operating discipline, resulting from our increased intensity, particularly in our core North American operations. Put simply, we are improving our execution and delivering on our commitments to create a stronger, more focused and more disciplined company.

    In late September, we had noted in our 10-K, that we were experiencing significant higher than expected costs, as well as considerable shipment delays across our Meals and Beverages portfolio, because of supply chain challenges we faced early in the quarter related to the start-up of a new distribution center in Ohio.

    This facility, which is operated by a third-party, will ultimately enable us to serve a broader customer base and provide greater service flexibility. During that same time period, our plant in Maxton, North Carolina, literally became an island as the floodwaters rose from Hurricane Florence.

    In response, we deployed across functional team led by supply chain experts and sales leaders that displayed amazing teamwork and moved with urgency to overcome these issues with our third-party logistics provider.

    They made substantial improvements in the final month of the quarter to recapture the vast majority of these sales. It was a herculean effort and is indicative of new Campbell that we're building. This focus is carrying over into other areas of the Company as well.

    Let's start with soup. Moving to Slide 5. Within our Meals and Beverages business, our top priority is to stabilize and improve the performance of our soup business. As we have discussed, soup is a great business and we are taking it back to basics approach, leveraging our market leading brands and driving improved execution across the portfolio.

    In fiscal 2019, we are resetting the value proposition for U.S. soup, this starts with increased focus on our key brands, each of which we're managing with rigor and according to a specific portfolio role. Campbell's, Swanson and Chunky are being managed to maximize margins and cash flow and Pacific and our well U.S. brands are being managed to drive strong profitable growth.

    Despite the sales decline in the quarter, there are many reasons we're optimistic about soup, including greater operating disciplined, improved merchandising and a new management team, that is moving quickly and decisively to improve performance. The supply chain challenges I mentioned earlier hamper the start of the quarter, but soup gained momentum as the quarter progressed and soup sales grew in October.

    As we look to improve performance in soup, we outlined several areas of actions, when we spoke in August and let me highlight them here. First, our emphasis on adjusting price gaps is showing signs of progress, despite the challenging retail environment. This year we have more competitive pricing on key segments as we enter soup season versus a year ago.

    Second, we are optimizing our merchandising with increased frequency and breadth compared to a year ago. And third, we have refocused our marketing efforts around a new campaign with the iconic Campbell's brand front and center.

    In October, we launched a new contemporary campaign that features our Campbell's condensed, as the recipe starter for delicious and affordable family meals. We are driving efficiencies in our spend and focusing our marketing dollars on our most profitable brands to both increase purchase intent and strengthen long-term brand equity.

    We launched soup advertising later than usual this past quarter due to the distribution facility start-up issues that I previously mentioned. As we saw to ensure marketing aligned with distribution capability.

    Soup advertising began appearing in the last two weeks of October, compared to September a year ago. While soup consumer marketing spend was lower than a year ago, we expect our soup marketing investments to normalize in the second quarter as we enter the heart of the soup season.

    Additionally, our Swanson broth business had a particularly good quarter. Swanson sales and share growth are driven by category momentum, expanded distribution and a new marketing campaign that also started in October. Part of our back to basics approach on soup, includes selected consumer-driven innovation. In the quarter, we launched Campbell's Well Yes! sippable soups for affordable on-the-go snacking to attract new consumers to the category. It's early days, but the launch has gained strong distribution and early velocity is ahead of our expectations.

    Turning to Pacific. We are pleased with the performance of the brand and the progress of the integration. We are taking steps to increase our production efficiency and distribution capabilities at Pacific, as it continues to perform against this portfolio role to drive strong profitable growth and in line with our acquisition expectations.

    Our focus on operating discipline has been elevated across the division, particularly on soup. The result is a more effective Meals and Beverages leadership team that is executing well. Stabilizing soup is our top priority, given the importance of this business. We are executing the plans we outlined back in August with increased emphasis on price realization, optimized merchandising support, targeted consumer-driven innovation and more effective and contemporary marketing focus on the iconic Campbell's master brand.

    We are doing the right things and are encouraged that our plans are beginning to have an impact. As we said last quarter in fiscal 2019, we will rebase soup and strengthen our value proposition in the marketplace. We have made progress against that objective to start the year. That said, we acknowledge that there's much more work to be done and it will take time to fully stabilize the business.

    I want to talk about the focus and execution in our snacking business and the combination of the Pepperidge Farm and Snyder's-Lance portfolios to form Campbell Snacks. The combination of these powerful portfolios establishes Campbell's as a leading player in the attractive and growing U.S. snacking market. We continue to see opportunities to drive significant top line growth and margin expansion in Campbell Snacks, as we invest and innovate across our portfolio of leading brands, capture cost synergies, and create an enhanced culture of performance and accountability.

    The two businesses that comprise Campbell Snacks are robust. This quarter marks the 16th consecutive quarter of organic growth in Pepperidge Farm, and the underlying Snyder's-Lance business is strong as well with share growth in six of our eight key brands this quarter, including Lance, KETTLE, Cape Cod, Pretzel Crisps, Emerald, and high double-digit growth in our Late July brand.

    As we've discussed previously, it's worth noting that prior to the closing of the acquisition, the former Snyder's-Lance management team executed SKU rationalization and price realization plan that didn't materialize, which have been a headwind to consumer takeaway, particularly for the Snyder's of Hanover brand.

    We are driving innovation across Campbell Snacks by executing against key consumer snacking insights. The combination of Pepperidge Farm and Snyder's-Lance enables us to leverage a vast manufacturing network to create new innovations such as Goldfish, epic crunch. A new line in that older kids, which was developed in Pepperidge Farm's kitchens and made Snyder's-Lance bakery.

    There are numerous examples where we're transferring expertise across the portfolio, such as applying our real food knowledge to drive reinventions and products like Lance crackers with color source from plants, and Pop Secret made with natural flavors.

    As we integrate and drive synergies, we are diving into the portfolio role of these brands across our portfolio and making capital investments to support our growth franchises. Specifically, we are expanding capacity in Goldfish, Milano, and KETTLE, all of which are examples of brands that are growing and driving share growth in their respective categories.

    From a value capture standpoint, our Snyder's-Lance integration and synergy actions are on track. The team is delivering synergies across the business, including and manufacturing procurement, warehousing and distribution, as well as, streamlining our managing processes. In supply chain, we're leveraging our scale to reduce input cost and we are accelerating our investments in automation to improve our cost structure.

    We're delivering on our integration and synergy commitments, and we expect continued progress going forward. We are confident in the long-term growth and margin expansion potential for Snyder's-Lance.

    We have created a single Campbell snacks leadership team accountable for our North American snacks business that is applying a consumer first approach to growth, delivering against our integration efforts, and fully leveraging our deep knowledge of snacking that spans both Pepperidge Farm and Snyder's-Lance.

    Moving to Slide 7. The second leg of our new strategy is to divest non-core assets. This serves a number of purposes, it allows us to accelerate our focus significantly pay down debt and strengthen our balance sheet.

    On August 30, we announced our intention to begin this divestiture process by selling Campbell International and Campbell Fresh as we focus on our core businesses in North America. We started to work immediately after our last call, to begin the process of divesting these two businesses, our financial advisors have been actively soliciting.

    As expected, there has been very strong initial interest from a range of potential strategic and financial buyers for these assets, because both are solid businesses made up of great brands. We continue to believe these businesses will be a greater value to new owners, who are focused on these categories and geographies.

    Combined, these businesses represented approximately $2.1 billion in annual net sales in fiscal 2018. We intend to use the proceeds to pay down debt and combined with ongoing strong free cash flow, we aim to achieve a target leverage ratio of 3x net debt to EBITDA by the end of fiscal 2021.

    We continue to expect to announce buyers for these businesses before the end of the fiscal 2019. But our overwriting goal remains to run a highly disciplined process on a timeline that will achieve the maximum value for these attractive assets. As we also stated in August, we are not complete, we will continue to review additional actions to further focus and refine our portfolio against our go-forward strategy.

    Moving to Slide 8. The third leg of our strategy is something that we have delivered on effectively over the last several years, cost savings. On August 30, we announced plans to cut another incremental $150 million from our overall cost as well as steps to drive asset efficiency in working capital and capital expenditures, as we build a leaner, more focused, and more agile company.

    We have already started on this work and have continued to deliver meaningful cost savings with an additional $45 million realized in the first quarter. This remains a core Campbell strength and we are confident in our ability to deliver the full $945 million in cumulative annualized savings by the end of fiscal 2022.

    Our focus on cost reductions and asset efficiencies help drive improved cash flow, which continues to allow us to return value to our shareholders with $107 million in quarterly cash dividends, while continuing to invest in our core business. As we said previously, fiscal 2019 will be a transition year for Campbell as our new management team guided by the Board operationalizes our plans to focus our portfolio and dramatically improve our execution.

    I want to emphasize that the actions we're implementing are the right ones at this time to create shareholder value. The Board and management team are committed to look deleveraging the Company, maintaining our investment grade credit rating and rewarding our shareholders through long-term earnings growth and competitive cash dividends.

    I also want to reiterate that the Board remains committed to evaluating all strategic options if they can demonstrably enhanced value above and beyond the significant actions that we are currently undertaking.

    With that as context, we are pleased with our performance in the quarter, we are improving our execution and our results, we're on track with our expectations, leading us to reiterate our fiscal 2019 guidance for this transition year. We're hard wiring our operating plans to our key priorities, KPIs, employee objectives, and compensation practices to build a culture of performance, we're enhanced speed, decision making, and accountability are foundational.

    We are breaking down silos and working together in new ways across the company as evidenced by the continued successful integrations of both Snyder's-Lance and Pacific and the speed at which we acted to fix the supply chain issues at the new distribution center, this is quite encouraging.

    As you can see we are moving quickly to implement the plans we announced back in August and we are making measurable progress across all of our key priorities.

    And now, let me turn it over to Anthony for a discussion of our financial results. Anthony?

    Anthony DiSilvestro

    Thanks Keith.

    Before getting into the detail, I'll make a few comments on our performance. Overall, our results were in line with our expectations and we are on track to achieve our fiscal year goals. As we disclosed in our 10-K in connection with the transition to our new U.S. warehouse optimization model, we experienced start-up issues at our new Findlay, Ohio distribution center early in the quarter, which were impacting our ability to ship product to our customers.

    The Findlay facility, which is operated by third-party logistics provider serves as the Midwest hub for distribution or a majority of our Meals and Beverage product. In October, we are able to recover quickly from the start-up challenges and despite $12 million of incremental cost, we finished the quarter with financial results that were in line with our original expectations.

    As we called out on the August 30 call, we expected the first quarter to be negatively impacted by a change in revenue recognition, the voluntary recall of flavor blasted Goldfish and some continued pressure on U.S. soup as we implement our promotional programs for the upcoming soup season.

    The impact from a change in revenue recognition which accelerates the timing of expense related to promotional programs at a 1 point negative impact on net sales, a 50 basis point impact on gross margin, and a 4 point negative impact on adjusted EBIT, the equivalent of $0.04 per share.

    Our organic sales declined 3%, including the 1 point negative impact from the change in accounting. And while the Goldfish brand have recovered well, we experienced some negative impact from the July 2018 voluntary recall of flavor blasted Goldfish. Excluding these two items that were more one-time in nature, the balance of the sales decline was mostly U.S. soup.

    During the quarter, we implemented our promotional programs for the upcoming soup season and are encouraged by the improving trend. We continue to achieve our cost savings goals against our aggregate program, which includes Snyder's-Lance. We generated $45 million of incremental cost savings in the quarter, bringing the program to-date total to $500 million.

    As we've discussed, we are now targeting to reach $945 million of costs and synergy savings by the end of 2022. We are pleased with the progress made on the acquisitions of Snyder's-Lance and Pacific Foods. The integration of these businesses is on track and the financial performance is meeting our expectations. Combined, the acquisitions were neutral to our adjusted EPS results in the quarter.

    Given our first quarter performance and outlook for the balance of the year, we are reaffirming our guidance for fiscal 2019. And in connection with our plan announced August 30, we intend to divest our international snacking business and the Campbell Fresh business. Together with our financial advisors, we've initiated divestiture processes and have seen significant buyer interest for both businesses.

    I will now review our detailed results. For the first quarter, net sales on an as reported basis increased 25% to approximately $2.7 billion, reflecting the recent acquisitions of Snyder's-Lance and Pacific Foods, and as I mentioned, organic sales declined 3%.

    Adjusted EBIT decreased 2% to $410 million, excluding the 4 point negative impact from the change in revenue recognition, adjusted EBIT increased 2%, driven primarily by the incremental earnings from the recent acquisitions, partially offset by 12 point decline on the base business reflecting gross margin pressure.

    Adjusted EPS decreased 14% or $0.13 to $0.79 per share, reflecting adjusted EBIT declines in the base business, including the $0.04 negative impact from the change in revenue recognition, partly offset by lower adjusted tax rate. In aggregate, the acquisitions of Snyder's-Lance and Pacific Foods had no net impact on adjusted EPS in the quarter.

    Breaking down our net sales performance for the quarter, organic net sales declined 3%, driven by higher promotional spending and lower volume. Lower volumes were primarily the result of decline in U.S. soup. Promotional spending negatively impacted net sales by two points, one point of which was the result of the new revenue accounting guidance, which accelerates the timing of promotional expense.

    The impact of the accounting change is not expected to be material for the fiscal year. The balance of the increase in promotional spending primarily reflects increased spending on the U.S. soup business. There was a one point negative impact on net sales from currency translation this quarter. And the recent additions of Snyder's-Lance and Pacific Foods to the portfolio, added 29 percentage points, bringing our as reported net sales increase to 25%.

    Our adjusted gross margin percentage decreased 4.9 point in the quarter. Excluding a 190 basis point dilutive impact from the acquisitions of Snyder's-Lance and Pacific Foods, our adjusted gross margin percentage declined three points, while the acquisitions are reducing our overall margins as we add them to the portfolio, we are confident that the margins on these businesses will increase over time as we integrate them into Campbell and achieve targeted cost and synergy savings.

    Cost inflation and other factors had a negative impact of 290 basis points, a majority of which was cost inflation, which on a rate basis increased approximately 4.5%, reflecting higher prices on steel cans, vegetables, wheat, dairy and resins as well as the continuing escalation of transportation and logistics costs.

    The balance of the margin decline was driven primarily by higher than expected distribution costs associated with the start-up of the Findlay, Ohio, distribution facility. These negative drivers were partly offset by benefits from our cost savings initiatives. Higher promotional spending including the 50 basis points impact of the change in revenue recognition, had a negative impact of 140 basis points.

    Portfolio mix had a negative impact of 20 basis points. Pricing had a positive impact of 30 basis points, reflecting actions taken in our Global Biscuits and Snacks segment. Lastly, our supply chain productivity program, which is incremental to our cost savings program contributed 120 basis points of margin improvement. All in our adjusted gross margin percentage decreased to 31.6%.

    Moving on to other operating items. Adjusted marketing and selling expenses increased 12% in the quarter, due primarily to the impact of recent acquisitions, probably offset by lower advertising on the base business within Meals and Beverages. The reduction in spending reflects a reallocation of support from advertising to promotional spending, reduced support levels in light of the distribution challenges, faced earlier in the quarter and a later start to our U.S. soup campaigns relative to the prior year.

    Adjusted administrative expenses increased 19% to $163 million, due primarily to the impact of recent acquisitions. Excluding the impact of acquisitions, adjusted administrative expenses increased slightly, reflecting costs associated with the current proxy contest.

    For additional perspective on our performance, this chart breaks down our adjusted EPS change between our operating performance and below the line items. Adjusted EPS decreased $0.13 from $0.92 in the prior year quarter to $0.79 per share in the current quarter.

    On a currency neutral basis, adjusted EBIT had a negative $0.01 impact on EPS, reflecting lower EBIT on the base business, inclusive of a $0.04 negative impact from the change in revenue recognition, partly offset by the addition of Snyder's-Lance and Pacific Foods. Net interest expense increased by $63 million, a $0.16 negative impact to EPS, driven by an increase in the debt levels to fund our recent acquisitions and reflecting the impact of higher interest rates.

    Our adjusted EPS benefited from a lower adjusted effective tax rate, increasing EPS by $0.04. Our adjusted effective tax rate was 24.3% in the quarter, which declined by 3.9 percentage points, due primarily to the lower U.S. federal tax rate, offset partly by the favorable settlement of certain U.S. state tax matters in the prior year quarter.

    And lastly, there was a $0.01 negative impact on EPS from currency translation this quarter, completing the bridge to $0.79 per share. And although not shown on the chart, in aggregate, the acquisitions of Snyder's-Lance and Pacific Foods were neutral to adjusted EPS.

    Now turning to our segment results. In Meals and Beverages, organic sales declined 5%, driven primarily by declines in U.S. soup, Prego and Canada partly offset by gains in V8 beverages. The segment sales were negatively impacted by one point from the change in revenue recognition. Excluding the benefit from the acquisition of Pacific Foods and the impact from the change in revenue recognition, sales of U.S. soup decreased 6%, driven by declines in ready-to-serve and condensed soups, partly offset by gains in broth. The sales decline in U.S. soup, reflects continued competitive pressure across the market and increased promotional spending.

    We are encouraged by the improved trends through the quarter as we implemented our promotional plans for the upcoming soup season, while consumer takeaway dollar sales declined 7% in the quarter, they were comparable to the prior year in the last four-week period. We are also encouraged by the performance of V8 beverages, which achieved sales gains in the quarter driven by V8 +Energy and the core vegetable juice business.

    Segment operating earnings declined 11% to $294 million. The decrease was driven primarily by a lower gross margin percentage, partly offset by lower advertising expenses. Gross margin performance reflects the impact of higher levels of cost inflation, increased promotional spending, including the impact from the change in revenue recognition and higher than expected distribution costs associated with the Findlay, Ohio distribution facility start-up.

    Here's a look at U.S. wet soup category performance and our share results as measured by IRI. For the 52-week period ending October 28, 2018, the category showed a decline, decreasing 40 basis points. Our sales in measured channels, including Pacific on a pro forma basis declined 4.6%, as we continue to wrap the major customer issue that we started to face a year ago. We had a 58.6% market share for the 52-week period, down 250 basis points from the year ago period.

    Private label grew share, increasing 160 basis points, primarily reflecting gains and brought finishing at 15.9%. All other branded players collectively had a share of 25.5%, increasing 90 basis points. Although not shown on the chart for the four-week period ending October 28, 2018, our sales, the measured channels were up 20 basis points, a notable improvement versus the latest 52-week period.

    In Global Biscuits and Snacks sales were $1,218 billion in the quarter, including $554 million from the acquisition of Snyder's-Lance. Please note that we've moved the Latin America business to the Meals and Beverages segment and have adjusted prior period results. Excluding the benefit from the acquisition of Snyder's-Lance, and the negative impact from currency translation, organic sales decreased 1%, driven primarily by declines in Kelsen cookies in the U.S.

    Sales of Pepperidge Farm, Goldfish crackers increased slightly in the quarter. As expected sales of Goldfish crackers were negatively impacted by the voluntary product recall in July 2018, although we are very pleased with how the brand has recovered.

    On Snyder's-Lance, it is important to note that the SKU rationalization and price realization initiatives are having a negative impact on sales growth, particularly on the Snyder's of Hanover brand. While SKU rationalization is having a short-term impact on sales, this action will result in a more streamlined and profitable portfolio going forward.

    Snyder's-Lance sales performance was in line with our expectations with the core brands achieving sales growth as measured by consumer takeaway and with six of the eight core brands achieving market share gains. Segment operating earnings increased 32% to $154 million, reflecting a 45 point benefit from the acquisition of Snyder's-Lance. Excluding the impact of the acquisition, segment operating earnings declined, due primarily to a lower gross margin, reflecting higher level of cost inflation.

    In the Campbell Fresh segment, organic sales decreased 1% to $232 million driven by declines in refrigerated soup, Garden Fresh Gourmet, and Bolthouse Farms refrigerated beverages, partly offset by gains in carrots. Segment operating loss was $3 million compared to a loss of $6 million in the prior year. Although modest, the stick this $3 million year-over-year improvement reflects improved operational efficiency in beverages, partly offset by the impact of refrigerated soup volume decline.

    As disclosed in our non-GAAP reconciliation in corporate, we recorded a non-cash impairment charge on the fixed asset of our refrigerated soup plant as we consider a potential sale as part of our planned divestiture of the Campbell Fresh segment.

    On a companywide basis, cash from operations increased to $231 million compared to $188 million in 2018, as lower working capital requirements and lower payments and hedging activities were probably offset by lower cash earnings.

    The cash outlay for capital expenditures, with $111 million, $53 million higher than the prior year, reflecting the timing of cash payments as well as investment to support our cost savings initiatives and the addition of Snyder's-Lance and Pacific Foods to the portfolio. We continue to forecast CapEx of approximately $400 million for fiscal 2019. We paid dividends totaling $107 million compared to $111 million in 2018.

    As previously announced, we suspended our share repurchases in the second quarter of fiscal 2018 as a result of the acquisition of Snyder's-Lance. Net debt of $9.6 billion is up from $3.3 billion a year ago, reflecting the impact of the $6.1 billion acquisition of Snyder's-Lance, and the $700 million acquisition of Pacific Foods, partly offset by positive cash flow generated by the base business.

    As part of our August 30 plan, we have initiated processes to divest our international snacking business and Campbell Fresh and we'll use the proceeds from these divestitures to reduce debt and improve our leverage ratio.

    Now, I'll review our 2019 guidance, which is unchanged from August 30. As we did previously, we are providing guidance based on our existing portfolio of businesses and also on a pro forma basis, assuming planned divestitures were completed as of the start of the fiscal year with proceeds used to reduce debt. I'll start with the guidance pre-divestitures. We expect sales to increase to a range of $9,975 billion to $10,100 billion as we benefit from the incremental impact of both the Snyder's-Lance and Pacific Foods acquisitions.

    This top line guidance implies that organic sales are expected to decline slightly, while we're seeing improved trends as we implement our promotional programs, we anticipate that U.S. soup sales will decline in 2019. In addition, we expect sales in Campbell Fresh to be negatively impacted as two major private label refrigerated soup customers will in-source production starting in 2019.

    We expect adjusted EBIT to be in the range of $1,370 billion to $1,410 billion as declines on our base business are mostly offset by the incremental acquisition impact of Snyder's-Lance and Pacific Foods. Both of these acquired businesses are performing in line with our expectations and represent significant long-term growth opportunities.

    The EBIT decline on the base business reflects the anticipated decline in organic sales, the negative impact of 4% to 5% cost inflation on gross margin, and the negative impact from higher incentive compensation, which was significantly reduced in 2018.

    We are forecasting a decline in our gross margin percentage of approximately 2 point as cost inflation and higher promotional spending, are only partly offset by 3% cost productivity and benefits from cost savings. Gross margin trends are expected to improve in the back half of the year for several reasons.

    A positive impact from the change in accounting, wrapping the Snyder's-Lance acquisition, pricing actions were currently implementing in the marketplace, phasing of productivity gains and some moderation of year-on-year cost inflation. We expect adjusted EPS to be in the range of $2.45 to $2.53 per share. The delta between EBIT and EPS performance is primarily driven by the interest expense associated with the acquisition of Snyder's-Lance and Pacific Foods.

    We expect interest expense in the range of $375 million to $390 million, and an adjusted tax rate of approximately 25%. And against our cost and synergy target, we expect to retrieve $120 million of savings. We are also providing forecast for 2019 on a pro forma basis, assuming the planned divestitures were completed as of the beginning of the fiscal year and based on the use of estimated proceeds to reduce debt. As you can see on the chart, our sales base decline to about $8 billion, adjusted EBIT to a range of $1,230 billion to $1,270 billion, and adjusted EPS to a range of $2.40 to $2.50.

    The overall anticipated dilution from the divestitures is modest given the current level of profitability of the Campbell Fresh division. As I've stated, we've initiated divestiture process for both Campbell International and Sea Fresh and have seen significant buyer interest for both businesses.

    That concludes my remarks. And now, I'll turn it back to Keith.

    Keith McLoughlin

    Okay. Thank you, Anthony.

    And before we open up the call to questions, I just want to touch on the CEO search process. The Board has been extremely thorough to make sure it selects the right candidate to lead Campbell through this important time. This is a very attractive role to a number of highly qualified internal and external candidates.

    The Board continues to have extensive discussions with a number of candidates who possess deep experience in consumer packaged goods and a strong track record of proven results. The Board continues to expect to name a permanent CEO by the end of the calendar year.

    In summary, we're seeing the early signs of improved execution and performance this quarter. We're confident that our plans provide a clear strategic path forward and a strong foundation for executing the Campbell turnaround. We are squarely focused on our plan and will not be distracted from our mission, executing the plan to maximize value for all shareholders, much more work lies ahead, but we're pleased with the overall pace of our progress.

    And now, let me turn it back to Ken to open up the call for your questions. Ken?

    Ken Gosnell

    Thanks Keith.

    We will now start our Q&A session. Since we have limited time, I request out of courtesy to the other callers, please ask only one question at a time. Okay, Crystal.

    Question-and-Answer Session

    Operator

    [Operator Instructions] And our first question comes from Bryan Spillane from Bank of America. Your line is open.

    Bryan Spillane

    So I guess my question is just around the cost savings, I think it was $45 million of savings in the first quarter and you're still guiding to a $120 million for the year and I think, Anthony, you said that you expected more productivity in the back half. So could you just square, I guess if we're running at $45 million in the first quarter, why the savings will be more than $120 million for the full year?

    Anthony DiSilvestro

    Yes, sure, Bryan. So as you pointed out, we are at $45 million against the full year guidance of $120 million. So that's about two-thirds of that will come through cost, the other third through SG&A. My comment earlier about cost savings was meant to address productivity.

    So in addition to these cost savings program, we target 3% of cost - of productivity savings every year and it's those savings that are a little bit more phase to the back half than the first half this year. So that was what, I was addressing.

    Bryan Spillane

    So the comps productivity was less than 3%, than in the first quarter?

    Anthony DiSilvestro

    Yes.

    Operator

    Our next question comes from David Driscoll from Citi. Your line is open.

    David Driscoll

    I wanted to ask a key, you got a bit of a chance here to see Campbell's from the Chief Executive Officer role, obviously you've been on the Board for years. You have $945 million laid out in the synergy capture, $500 million already achieved, $445 million left to go. Simple math would say that this is worth more than a $1 a share, in terms of gross impact to Campbell's. Consensus estimates, couple of years out Keith, they only go up like $0.20.

    The question to you is, if you got a chance to look at these savings, are they real? Do you believe in this bucket share or better in savings potential? And then how much of this can actually go down to the bottom line on Campbell's or do you foresee most or all of this needing to be reinvested back into the business to restore top line growth?

    Keith McLoughlin

    Yes, that's a good question. I think you partly answered a lot of that actually. So I think these numbers are real and having been on the Board for a few years, I've watched the Company execute on these cost initiatives and now, of course, been with the management team here, I see why they're good at it. It's a very disciplined and robust process and we track it rigorously and, of course, now in the middle of those meetings.

    So my confidence that we'll do it as high, as you know to get to the $945 million, you have the $0.5 billion of the current program which is getting close to being complete or at least meeting that number. We've got the $295 million from Snyder's-Lance and we're reporting on that frequently as you can imagine. And we put the challenge up for another $150 million.

    So I would say that of the $945 million, that's $150 million - additional $150 million is where we got to get more traction, we've got to get more lot of side, we've got obviously work under way to do that, but there is more work to do there. But Anthony, you've been leading the big part of those for us for several years and now with the new. Can you add to that?

    Anthony DiSilvestro

    Yes, totally agree Keith, that we're highly confident against the $945 million. I think the other way to come out is in the context of our long-term growth algorithm and how do you get from 1% to 2% sales to 4% to 6% EBIT growth.

    And that delta, implies about 50 basis points of margin expansion every year and if you do the math against where we are today to the $945 million, it implies a little bit of - little over half of those cost savings need to go to expand margins. The other chunk of it can go back into the business to reinvest to grow the brand.

    Operator

    Our next question comes from Chris Growe from Stifel. Your line is open.

    Chris Growe

    Anthony, you outlined $12 million in cost, I think it was related to the Ohio facility. I want to understand in the first quarter, if you were to add up to the Ohio costs or the incremental freight cost, the hurricane cost, did you give a number for what the totality of those costs were? And then as you offset that, obviously, marketing was little lower, was that the main offset to that incremental costs in the quarter?

    Anthony DiSilvestro

    Yes, that's pretty close. I think if you look at the $0.13 decline in EPS and back out taxes and currency, you've got a $0.17 decline, of that $0.17 decline, $0.04 is revenue recognition, $0.03 is Findlay, the hurricane did not really have a significant impact in all said and done. And then the balance of it which is $0.10, our combination of the gross margin pressure, the lower organic sales, partly offset by the reduction in advertising and consumer spend is how you get there.

    Chris Growe

    And did you say, how much the advertising, just that piece that was down in the quarter?

    Anthony DiSilvestro

    No, I think in the press release, we say, we're up 13% selling and marketing and 28 points of that increase is the acquisition. So we're down double-digit.

    Operator

    Our next question comes from Ken Goldman from JPMorgan. Your line is open.

    Ken Goldman

    When you talk on Slide 5 about your plan to stabilize soup, there's some, I think low hanging fruit there at the right move in terms of price gaps and merchandising and so forth. But what I think is still to me not apparent in the plan is how you will drive consumers to get more excited by and interested in your core soup product? Reason I'm saying that is the only innovation I see here is on the Well Yes!, sipping soup side, which is not that big of a product, right?

    And then on Slide 23, you talk about everyone want what's next, so there's not a single product in the next column, that's a core Campbell Soup or Chunky product also. So I guess, what I'm trying to figure out what is the plan to spark sustained consumer interest in the core Campbell Soup red and white can as well as Chunky, any help there would be, I think very useful?

    Keith McLoughlin

    Sure. It's good question. Of course, you have - at the end of the day, what we're going to get paid for in soup is relevant consumer innovation. So you're right on target with the question. We've got to bring more relevant innovation at a faster rate to consumers. We're going to do that in a couple of ways.

    One is a big theme, as you know is focus, right. How do we focus on those core brands, those core categories, where we have strength, where we got more competition. And so the redeploy or deploy the R&D dollars against those key categories. It's going to be in areas like convenience.

    It's going to be in areas like meal preparation, right. How do you deal - how do you take away that 4:00 PM to 6:00 PM nightmare that happens in every household, like what's for dinner, it's going to be in healthy and vegetables, cooking - leveraging our cooking expertise, right and helping people deal with that timeframe to say here's an easy way to take some chicken or salmon and with this ingredients, with these capabilities make a healthy meal for your family. So it's going to be in those areas.

    Actually we've got lots of neat stuff happening, I'm very excited to get together with you all during Investor Day to show you some of the stuff that Roberto and his team have under the hood there, but it's not yet ready for prime time, but your point is well made and well taken, it's - we're going to paid for relevant innovations, that could bring excitement back into the category.

    Operator

    Our next question comes from Robert Moskow from Credit Suisse. Your line is open.

    Robert Moskow

    I guess I had a similar question as Ken did, but it was specifically about millennials. I thought, I remember 3 months ago that there was going to be a more concerted effort to target younger consumers with the Campbell brand and specifically in cooking. And I didn't see much here in terms of how you're doing that maybe your answer is the same wait until the Analyst Day.

    But then secondly, what I did see in the market a lot, was a lot of 10 for 10 - 10 cans of condensed soup for $10, what was the decision to go back to that, in the past I've heard that the Company really didn't want to discount that deeply in condensed, is that changing now?

    Keith McLoughlin

    Yes, I would say actually to your first part of your question around the millennials, we are - probably maybe you haven't seen yet, but we started in the last two weeks of October, a new campaign to buy condensed specifically against millennials and especially to show millennials, how to take the Campbell's condensed soup and use it for meal preparation.

    So you remember exactly correctly and actually the new campaign is right on that specific target segment with that featured benefit that you mentioned. In terms of the actual promotion, I can't speak the detail. I don't know, Anthony, if you have an experience on the 10 for one?

    Anthony DiSilvestro

    Yes, I mean, historically, our issue on 10 for 10 has been on our RTF , right, more though and that's what we wanted to get away from we have, it's not unusual to see 10 for 10 on some of the condensed side of it.

    Operator

    Our next question comes from Steve Strycula from UBS. Your line is open.

    Steve Strycula

    Quick question, on the gross margin piece what Anthony, what builds the confidence for the second half margin recovery on the gross margin rate maybe speak to what inflation assumptions are some of the headwinds that kind of dissipate? And then a quick follow-on would be on the international snacking piece, is that about a $1.2 billion revenue entity was call it $230 million of EBITDA, just some parameters would be helpful? Thanks.

    Anthony DiSilvestro

    Yes, so on the gross margin point. Obviously, we're down in the first quarter, there is some unusual things in there 60 basis points related to Findlay, 50 basis points on revenue recognition from higher trade on U.S. soup, cost inflation is running in that 4% to 5% range as we expected. Key drivers being steel, wheat, vegetables, dairy and resins. As we look ahead to the balance of the year, we do expect improving trends, right.

    There is a number of reasons for that. First is the accounting change, the revenue recognition is a bad guy in the first half is a positive in the second half. Second and this is an important one, we're going to wrap the acquisition of Snyder's-Lance end of March. So it turns from a being dilutive mix impact to a positive contributor to organic margin expansion.

    Third, we have communicated to our customers pricing actions that we're taking in a number of brands and is - these will go into effect in the second half have some impact and certainly have more impact as we move into 2020. Also we'll start to wrap some of the more significant inflation, so that should moderate a little bit.

    And as I said earlier on the call, our ongoing 3% productivity program, there's a little back-end loaded this year relative to other. So those are the four or five things that give us confidence that these trends will improve throughout the year. And on your other question, you're - I think you're right on the international sales. We're not going to break down the EBITDA at that level given where we are in these divestiture processes.

    Operator

    Our next question comes from Andrew Lazar from Barclays. Your line is open.

    Andrew Lazar

    Just two quick things, one, I think on the last call you talked about U.S. soup probably worth about 1 point of a decline for the full year in total Company sales. I want to get a sense if that was still around what you were thinking?

    And then second, I'm just trying to square your comments from an earlier question around reinvesting maybe a little less than half of sort of the savings over the period of time back into the business. Were there some comments last quarter where you weren't necessarily, you didn't sound that you were expecting a significant reinvestment more of a reallocation from some brands to more disproportionate spending on some others, I wanted to make sure I just understood how to square those two comments? Thank you.

    Anthony DiSilvestro

    Yes, so on the sales part of it. Yes. So U.S. soup performance, we do expect soup sales will be down on a year of probably worth comes to a point to total company sales. The other headwind that we're facing is that the two major private label fresh soup customers moving to in-sourcing that will have a negative impact as we go through the year.

    In terms of the reinvestment, Andrew, I think if you just look at our - like, I said, if you look at our EBIT versus our sales, long-term algorithm is worth about 50 basis points, and that 50 basis point is about $60 million to $70 million of incremental EBIT every year.

    And then if you just work out the math on the program to go in terms of the annual savings, you need a little over half of that to go to that margin expansion. Now that's a little bit simplistic because obviously you have a basket of items that are going up or down in that EBIT bridge. But I think high level, that's about what we would expect to see happen going forward.

    Andrew Lazar

    And Anthony around the reinvestment is a lot of it more in the marketing seeing consumer spend side or are there maybe spending that needs to be done behind capabilities in terms of what you need going forward that maybe we're less aware of?

    Anthony DiSilvestro

    Yes, no, I think that's a good question, it's just probably all of the above, right, I mean, it's not just limited to one area of investment, right. You're going to increase capabilities, one way or another whether in technology, or in people, or in brands, or in products. So yes, we've talked about this before and things like digital and e-commerce, we have been stepping up our level of investment and we'll continue to do.

    Operator

    And our next question will come from Jonathan Feeney from Consumer Edge. Your line is open.

    Jonathan Feeney

    Just wanted to ask a question on the gross margin, I guess, a related issue, which is the affordability. When I look at the 2-year progress, 1-year progress in gross margin even backing out the acquisition impact, it now compares unfavorably to I think a lot of your peers directly. And juxtapose that with the conversation about affordability, I guess I just wonder me looking at the data, I wonder how much of the issue is affordability per se and where is that data coming from? Is that the consumer, the retailer that's telling you that the products aren't affordable, particularly I assume that relate largely to the meals business and soup specifically.

    So I guess where's that data coming from? And are you confident that gross margins have bottomed and are going up from here on an organic basis you're setting aside whatever effect of the planned divestitures might have? Thank you very much.

    Keith McLoughlin

    Yes, I might need you to say a little bit more about the affordability part of the question, but just kind of generally relative to gross margins. Of course as our gross margins as you stated have been compressed here in the last couple of years, primarily driven by the under the performance in Sea Fresh and the major issue we've had with a major customer in soup. I mean that's a not insignificant challenge to our margins. And as we're stating and as you're seeing, we're starting to see getting traction, we are divesting the Sea Fresh side.

    And then in soup, it looks like we're getting traction both with a key customer and also in general. So that gives us confidence, we'll get the cost that work, we'll get the synergies from Snyder's-Lance, get their gross margins back closer to where Pepperidge Farm's are and that combination gives us confidence that the gross margin will expand. Would you had another question around affordability, I just want to make sure I get the question?

    Operator

    And it actually looks like the caller has disconnected.

    Keith McLoughlin

    Okay.

    Operator

    So that will conclude our question-and-answer session for today's conference. I'd now like to turn the call back over to Keith McLoughlin for any closing remarks.

    Keith McLoughlin

    Okay. Thank you, and thanks, everyone. I just - a couple of concluding remarks, hopefully, you can kind of get the little bit of the picture here that there are changes happening at Campbell's. We're in a turnaround and we're executing against the plan we laid out on August 30.

    We're pleased that the first quarter results give us the ability to reaffirm guidance for the full year. We're getting traction, we're seeing early signs of progress for the turnaround, but there is still a ton of work in front of us. So we are by no means declaring victory, this is the beginning - this is the beginning of the turnaround with the Campbell Soup Company.

    Thank you all for joining us this morning. We look forward to reporting to you back on Q2 earnings call. Have a great day, and for those in the U.S., a very Happy Thanksgiving. Thanks everyone.

    Operator

    Ladies and gentlemen, thank you for participating in today's conference. This does conclude the program. And you may all disconnect. Everyone, have a wonderful day.

    SeekingAlpha


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