Risk Without Return

Risk-only investment strategies have been growing in popularity as traditional in- vestment strategies have fallen short of return targets over the last decade. However, risk-based investors should be aware of four things. First, theoretical considerations and empirical studies show that apparently dictinct risk-based investment strategies are manifestations of a single effect. Second, turnover and associated transaction costs can be a substantial drag on return. Third, capital diversification benefits may be reduced. Fourth, there is an apparent connection between performance and risk diversification. To analyze risk diversification benefits in a consistent way, we introduce the Risk Diversification Index (RDI) which measures risk concentrations and complements the Herfindahl-Herschman Index (HHI) for capital concentrations

Sustainable Investing and the Cross-Section of Maximum Drawdown

We use supervised learning to identify factors that predict the cross-section of maximum drawdown for stocks in the US equity market. Our data run from January 1980 to June 2018 and our analysis includes ordinary least squares, penalized linear regressions, tree-based models, and neural networks. We find that the most important predictors tended to be consistent across models, and that non-linear models had better predictive power than linear models. Predictive power was higher in calm periods than stressed periods, and environmental, social, and governance indicators augmented predictive power for non-linear models

Systems genetic analysis of addiction-associated traits

Substance abuse disorders are heritable neuropsychiatric disorders with largely unknown genetic etiology. Distinct genetic factors likely contribute to the different stages and behaviors of addiction, including initial sensitivity to the subjective and physiological effects of drugs and physiological and psychological measures of withdrawal. Mammalian model organisms permit a comprehensive approach to gene mapping and to bridging genetic variation with neurobiological mechanisms of addiction-relevant behaviors. The focus of this dissertation is to investigate the genetic basis of the rewarding and aversive properties of opioids, utilizing a systems genetics approach that includes both forward and reverse genetics in combination with transcriptomics and bioinformatics as tools to determine the molecular mechanisms. The first aim of this research is to conduct a genetic linkage mapping study of addiction-associated traits in a reduced complexity cross of two nearly identical B6 substrains (C57BL/6J and C57BL/6NJ). Forward genetic techniques, such as quantitative trait locus (QTL) mapping was utilized to identify novel candidate genes involved in addiction-associated traits. We completed QTL mapping combined with genome-wide gene expression analyses to rapidly identify compelling candidate genes underlying addiction traits. Most notably, we identified a region on distal chromosome 1 that regulates opioid sensitivity and withdrawal. Using striatal expression QTL mapping, transcript/behavior covariance, and convergent haplotype analysis, we identified a strong positional candidate gene, Rgs7. The second aim of this research is to validate novel candidate genes and molecular mechanisms responsible for modulation of opioid reward and aversion. Using behavioral and expression QTL mapping, Csnk1e was previously identified as a candidate gene for psychostimulant sensitivity. Here, we utilized Csnk1e knockout mice to confirm the effect of Csnk1e deletion on opioid sensitivity and extend its role to opioid reward and a natural reward dependent on opioid signaling- sweetened palatable food consumption. Additionally, we have utilized striatal transcriptome analyses to identify potential molecular mechanisms, including aberrant myelination and neurodevelopment of the striatum. In summary, this dissertation research utilizes mouse forward and reverse genetics, in combination with transcriptome and bioinformatics analyses to identify the genetic and neurobiological underpinnings of addiction-associated traits

Understanding Self-Control, Motivation, & Attention in the Context of Eating Behavior

Obesity and overweight continue to be a growing problem in the United States and abroad. Maintaining self-control in tempting food environments is necessary in order to avoid weight gain, yet is difficult for many individuals to achieve. In order to understand how self-control resources interact with motivation to affect selective attention to foods, 128 college student participants (42 males) varied in their controlled and autonomous motivation for healthy eating were randomly assigned to either an ego-depletion (n = 61) or control manipulation (n = 67). Implicit selective attention to food stimuli was subsequently assessed using a food flanker task that contained healthy and unhealthy foods. Results showed that overall, participants responded more quickly to unhealthy food targets and experienced more distraction by unhealthy food flankers relative to healthy foods. Moreover, ego-depleted participants were faster in responding to unhealthy target food stimuli compared to healthy food stimuli, whereas those in the control group did not differ in their responses to healthy and unhealthy targets. Neither autonomous nor controlled motivation was associated with differences in attention to food stimuli and did not interact with ego-depletion. These results indicate that independent of motivations for healthy eating, reduced self-control resources lead to changes in processing speed for unhealthy food stimuli, which may have implications for subsequent consumption and ultimately weight regulation

A Hierarchical Linear Modeling Approach to Predicting Trajectories of Posttraumatic Growth in Veterans Following Acquired Physical Disability

The purpose of this study was to examine potential predictors of PTG across time in Veterans with acquired physical disabilities. Specifically, this study aimed to understand how various demographic and injury characteristics, coping styles, appraisals of injury, and social support might predict trajectories of PTG from discharge from inpatient rehabilitation through 12 months after baseline. Initial curvature analyses suggested that a cubic polynomial trend best fit the movement of PTG over time, generally conforming to an initial increase, decrease, and then plateau or slight increase. Four HLMs were run to examine whether demographic and injury characteristics, coping styles, appraisals of injury, and social support predicted the height of this cubic architecture of PTG across baseline, 1, 3, 6, and 12-month follow ups, and a final HLM examined whether any statistically significant fixed effects in the first four HLMs interacted with time in the prediction of participants’ PTG trajectories. Estimated premorbid IQ was negatively associated, while age was positively associated with the height of PTG over time. Reframing and religious coping were positively associated with PTG over time, as were challenge appraisals. Three types of social support did not independently predict PTG trajectories, although bivariate correlations suggested the presence of isolated relationships between different types of social support and PTG at certain time points. None of the significant predictors interacted with time in predicting participants’ PTG trajectories

The History of a Hunt for Simplicity and Coherence in the Field of 'Ownership', 'Possession', 'Property' and 'Title'

The History of a Hunt for Simplicity and Coherence in the Field of 'Ownership', 'Possession', 'Property' and 'Title

James–Stein for the leading eigenvector

Recent research identifies and corrects bias, such as excess dispersion, in the leading sample eigenvector of a factor-based covariance matrix estimated from a high-dimension low sample size (HL) data set. We show that eigenvector bias can have a substantial impact on variance-minimizing optimization in the HL regime, while bias in estimated eigenvalues may have little effect. We describe a data-driven eigenvector shrinkage estimator in the HL regime called "James-Stein for eigenvectors" (JSE) and its close relationship with the James-Stein (JS) estimator for a collection of averages. We show, both theoretically and with numerical experiments, that, for certain variance-minimizing problems of practical importance, efforts to correct eigenvalues have little value in comparison to the JSE correction of the leading eigenvector. When certain extra information is present, JSE is a consistent estimator of the leading eigenvector

Ownership of ESG Characteristics

A portfolio can be viewed as the collection of the businesses, policies and practices of constituent companies. We measure investors\u27 Ownership of this collection. Ownership metrics aggregate an assortment of company specific Environmental, Social and Governance (ESG) characteristics to the portfolio level, and they can inform investment and engagement decisions. Relative to a benchmark, investor Ownership is active and satisfies a zero-sum property, which underscores the distinction between Ownership and impact. Ownership of ESG characteristics may be interpreted as ascribing ethical responsibility, but that conclusion and any decisions that result from it belong to the investor