Physical Sciences and Mathematics Commons^™

Deep Reinforcement Learning Pairs Trading, Andrew Brim

All Graduate Plan B and other Reports, Spring 1920 to Spring 2023

This research applies a deep reinforcement learning technique, Deep Q-network, to a stock market pairs trading strategy for profit. Artificial intelligent methods have long since been applied to optimize trading strategies. This work trains and tests a DQN to trade co-integrated stock market prices, in a pairs trading strategy. The results demonstrate the DQN is able to consistently produce positive returns when executing a pairs trading strategy.

Predictive Distributions Via Filtered Historical Simulation For Financial Risk Management, Tyson Clark

All Graduate Plan B and other Reports, Spring 1920 to Spring 2023

Filtered historical simulation with an underlying GARCH process can be used as a valuable tool in VaR analysis, as it derives risk estimates that are sensitive to the distributional properties of the historical data of the produced predictive density. I examine the applications to risk analysis that filtered historical simulation can provide, as well as an interpretation of the predictive density as a poor man’s Bayesian posterior distribution. The predictive density allows us to make associated probabilistic statements regarding the results for VaR analysis, giving greater measurement of risk and the ability to maintain the optimal level of risk per …

Unclogging The Pipeline: Advancement To Full Professor In Academic Stem, Helga Van Miegroet, Christy Glass, Ronda R. Callister, Kimberly Sullivan

Ecology Center Publications

Purpose: Women remain underrepresented in academic STEM, especially at the highest ranks. While much attention has focused on early-career attrition, mid-career advancement is still largely understudied and undocumented. This paper analyzes gender differences in advancement to full professor within academic STEM at a mid-size public doctoral university in the western US, before and after the NSF-ADVANCE Program (2003-2007).

Methodology: Using faculty demographics and promotion data between 2008 and 2014, combined with faculty responses to two waves of a climate survey, the magnitude and longevity of the impact of ADVANCE on mid-career faculty advancement across gender is evaluated.

Findings: This study …