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Risk plays a central role in financial markets. Households and companies adjust their consumption and investment behaviors, respectively, when facing risk. Financial markets then react to the adjustments accordingly. Whereas a positive risk-return relation is the first fundamental law of finance, however, empirical evidence does not always support such implication. My dissertation focuses on identifying the disagreements between existing asset-pricing theories and empirical evidence, and proposing new explanations that reconcile the disagreements. Essay 1 studies how aggregate consumption responds to macroeconomic shocks. Essay 2 shows how the revisions in aggregate consumption estimates affect the measure of asset prices. Essay 3 demonstrates how financial constraints affect corporate payout and investment policies.Essay 1: Leading consumption-based asset-pricing models have two major implications: First, investors expect higher future stock market returns when the expected stock market volatility increases. Second, stock market prices decrease monotonically with stock market volatility. Neither implication, however, is supported by data. In the first essay, I introduce a consumption-based model featuring two, fear and euphoria, variances to jointly explain the unstable relation between stock market variance and return, and between stock market variance and price. I also present empirical evidence that supports the model implications.Essay 2: We document novel empirical support for the CCAPM. Real-time consumption has significant explanatory power for the cross-section of expected stock returns, while previous studies have found elusive results using revised latest-vintage data. We also lends support to Kroencke’s (2017) conjecture that the Bureau of Economic Analysis filters consumption data by showing that it does so gradually through revisions. The revised data perform poorly in the CCAPM estimation because they are heavily filtered and contain substantial measurement errors. GDP and industrial production have similar asset pricing information and become insignificant when we control for real-time consumption.Essay 3: This study investigates whether firm dividend payout choices are influenced by the presence of a Dividend Reinvestment Plan (DRIP). Given that DRIPs help retain capital, we show that dividend-paying firms with a DRIP will tend to pay a high dividend and maintain a stable payout policy. Using a multinomial logistic model, we show that in comparison to REITs without DRIPs, REITs with DRIPs have a higher payout ratio and are less likely to: (1) pay regular dividends with extra dividends and share repurchases, (2) distribute extra dividends, repurchase shares, yet omit regular dividends and (3) omit all payouts. In addition, we find that REITs with a capital-retaining DRIP invest more aggressively and such increased investment activities are undertaken without raising the reliance on external financing.

This study examined the effect that variances in the U.S News and World Report rankings have on enrollment trends and practices in both top and not top-25 business schools. The purpose of this study was to determine whether mobility in the rankings was met with a statistically significant response to the research questions presented. While this was not the first study to look at business school rankings and their effect on enrollment trends, it was the first one to look at recent data associated with this phenomena, as well as, the first study to utilize panel data in order to determine the answer to its research questions. The four research questions put forth dealt with enrollment numbers, student quality, acceptance rates and peer assessment scores. The study determined that the research questions are not statistically significant across the board and that one group is more sensitive than the other to variation in the rankings. Possible practical implications for these findings and further research are discussed, as well as, the limitations of the study. Furthermore, a review of all current business school rankings and their methodologies can be found in the study.

Understanding the complex growth process of dendritic arbors is essential for the medical field and disciplines like Biology and Neurosciences. The establishment of the dendritic patterns has received increasing attention from experimental researchers that seek to determine the cellular mechanisms that play a role in the growth of neural trees. Our goal in this thesis was to prove the recurrence formula for the probability distribution of all possible neural trees, as well as the formulas of the expected number of active branches and their variances. We also derived formulas for the spatial locations of the optimal targeting region for a tree with branching probability. These formulas were necessary for the simplified stochastic computational model that Osan et al have developed in order to examine how changes in branching probability influence the success of targeting neurons located at different distances away from a starting point.

In this thesis, we develop a methodology for identifying and visualizing the important dimensions of variation for nonlinear functional data An algorithm for finding an appropriate pleasure of center for unregistered functional data aligned using dynamic time warping is presented. The strengths of dynamic time warping and isometric feature mapping are combined to produce a dimensionality reduction technique that provides intuitive, interpretable results for nonlinear functional data. This methodology is demonstrated on human motion data obtained from the University of North Carolina School of Dentistry Orthodontic and Dentofacial Clinics.

A factor analysis typically involves a large collection of data, and it is common for some of the data to be unrecorded. This study investigates the ability of several techniques to handle missing values in a factor analysis, including complete cases only, all available cases, imputing means, an iterative component method, singular value decomposition and the EM algorithm. A data set that is representative of that used for a factor analysis is simulated. Some of this data are then randomly removed to represent missing values, and the performance of the techniques are investigated over a wide range of conditions. Several criteria are used to investigate the abilities of the techniques to handle missing values in a factor analysis. Overall, there is no one technique that performs best for all of the conditions studied. The EM algorithm is generally the most effective technique except when there are ill-conditioned matrices present or when computing time is of concern. Some theoretical concerns are introduced regarding the effects that changes in the correlation matrix will have on the loadings of a factor analysis. A complicated expression is derived that shows that the change in factor loadings as a result of change in the elements of a correlation matrix involves components of eigenvectors and eigenvalues.