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Essays In Empirical Asset Pricing, Landon James Ross

Arts & Sciences Electronic Theses and Dissertations

This dissertation examines several empirical questions regarding the determiniation of asset prices. The first chapter studies the effect of firm characteristics’ interactions on the cross-section of expected returns via a modified Fama-Macbeth regression suitable for estima- tion problems involving thousands of firm characteristics. The second chapter estimates eco- nomically significant risks from legally required risk disclosures in public companies annual filings via a novel regression specification designed for the estimation of firm characteristics that are both aligned with expected returns and semantically meaningful. The third chapter examines the aggregate financial consequences of firms’ cash holdings for shareholders.

The Use Of Introspective Reports To Predict Subsequent Memory: Implementing Machine Learning For Judgment-Of-Learning Paradigms, Nathan Lloyd Anderson

Arts & Sciences Electronic Theses and Dissertations

Recent advances in machine learning have allowed for the use of natural language responses to predict outcomes of interest to memory researchers such as the confidence with which recognition decisions are made. The present experiments were designed to leverage this novel methodological approach by soliciting free-response justifications of judgments of learning (JOLs) whereby people not only assess the probability with which they will later recognize individual items but also (for some items) justify the reasoning behind their judgment. Across all experiments and conditions, regression models trained on justification language showed above-chance prediction of subsequent memory success and outperformed models trained …

Essays In Corporate Finance And Machine Learning, Manish Jha

Arts & Sciences Electronic Theses and Dissertations

My dissertation focuses on two broad questions. First, why do shareholder’s preferences vary, and how various agents persuade them? And second, how public perceptions about the financial sector and regulations affect economic outcomes? While my research plan contributes to the two distinct fields of literature, a unifying theme of my research is the use of innovative machine learning techniques to overcome the empirical challenges that would typically prevent measuring these sentiments objectively.

In my Chapter 1, I use a supervised machine learning model on mutual fund family’s proxy voting choices to estimate their preferences. I find that hedge fund activists …

Wheelchair Propulsion For Everyday Manual Wheelchair Users: Repetition Training And Machine Learning-Based Monitoring, Pin-Wei Chen

Arts & Sciences Electronic Theses and Dissertations

Upper limb pain and injuries are prevalent among manual wheelchair users and can restrict their participation and daily activities. Due to the high repetition and force in wheelchair propulsion, chronic wheelchair propulsion has been linked to the risk of upper limb pain and injury. Prevention of upper limb pain and injury is a high priority in wheelchair-related research. Decades of research in wheelchair propulsion biomechanics have led to clinical practice guidelines (CPG). Unfortunately, a decade after the publication of the CPG, CPG-recommended propulsion is still uncommon. Hence, for the first aim, a randomized controlled trial pilot study with two groups …

Knowledge Driven Approaches And Machine Learning Improve The Identification Of Clinically Relevant Somatic Mutations In Cancer Genomics, Benjamin John Ainscough

Arts & Sciences Electronic Theses and Dissertations

For cancer genomics to fully expand its utility from research discovery to clinical adoption, somatic variant detection pipelines must be optimized and standardized to ensure identification of clinically relevant mutations and to reduce laborious and error-prone post-processing steps. To address the need for improved catalogues of clinically and biologically important somatic mutations, we developed DoCM, a Database of Curated Mutations in Cancer (http://docm.info), as described in Chapter 2. DoCM is an open source, openly licensed resource to enable the cancer research community to aggregate, store and track biologically and clinically important cancer variants. DoCM is currently comprised of 1,364 variants …

Application Of Genomic Technologies To Study Infertility, Nicholas Rui Yuan Ho

Arts & Sciences Electronic Theses and Dissertations

An estimated one in eight couples in the United States are diagnosed with infertility. There is a significant genetic contribution to infertility, with estimates of heritability ranging from 0.2 to 0.5. We know surprisingly little about the genetic causes, with only slightly more than a hundred genes known to cause human infertility. I have been translating recent advances in genomics to study infertility in a more efficient manner, in order to improve our knowledge of the genetic causes. By using high throughput genomics and proteomics datasets from other groups, I was able to feed that into a machine learning algorithm …

Application Of Machine Learning To Mapping And Simulating Gene Regulatory Networks, Hien-Haw Liow

Arts & Sciences Electronic Theses and Dissertations

This dissertation explores, proposes, and examines methods of applying modernmachine learning and Bayesian statistics in the quantitative and qualitative modeling of gene regulatory networks using high-throughput gene expression data. A semi-parametric Bayesian model based on random forest is developed to infer quantitative aspects of gene regulation relations; a parametric model is developed to predict geneexpression levels solely from genotype information. Simulation of network behavior is shown to complement regression analysis greatly in capturing the dynamics of gene regulatory networks. Finally, as an application and extension of novel approaches in gene expression analysis, new methods of discovering topological structure of gene …