Economics Commons^™

Three Essays On Financial Economics, Megersa Daksa

Dissertations

This study presents three essays on financial economics. In the first essay, we examine how monetary policy shocks affect risk aversion and uncertainty, as well as how risk aversion and uncertainty spread across financial markets. Although a recent study shows that monetary policy influences risk aversion and uncertainty in global stock markets, there are no studies on risk aversion and uncertainty spillover across stock, currency, and commodity markets. Following the method of Bekaert et al. (2013), we decompose the implied volatility indexes (VIX's) for the SP500, U.S. exchange rate, gold and crude oil into risk aversion and uncertainty. The decomposition …

Three Essays In The Economics Of Education, Md Ohiul Islam

Dissertations

This dissertation explores three distinct topics in the economics of education. These topics explore the relationship between factors such as race, gender, national origin, and educational and labor market outcomes. Educational attainment in the STEM (Science, Technology, Engineering, and Mathematics) areas receives a major focus in this dissertation; a college-level specialization in STEM areas generally leads to high incomeyielding career tracks. Below I briefly explain the research objectives and findings of each chapter.

The first chapter focuses on the impact of teacher-student demographic mismatch on student success in classrooms at the high school level. When students, particularly those of disadvantaged …

Representation Learning In Finance, Ajim Uddin

Dissertations

Finance studies often employ heterogeneous datasets from different sources with different structures and frequencies. Some data are noisy, sparse, and unbalanced with missing values; some are unstructured, containing text or networks. Traditional techniques often struggle to combine and effectively extract information from these datasets. This work explores representation learning as a proven machine learning technique in learning informative embedding from complex, noisy, and dynamic financial data. This dissertation proposes novel factorization algorithms and network modeling techniques to learn the local and global representation of data in two specific financial applications: analysts’ earnings forecasts and asset pricing.

Financial analysts’ earnings forecast …