Date of Submission

5-5-2026

Document Type

Thesis

Department

Finance

Advisor

Jared Sheffield

Keywords

Market Volatility, Behavioral Biases, Trading Behavior, Investor Psychology, Market Episodes. Behavioral Finance Model

LCSH

: Financial planners—Marketing, Individual investors, Finance, Personal--Psychological aspects

Abstract

This study examines how behavioral biases influence short-term trading behavior during periods of high market volatility. While traditional financial theory assumes that investors act rationally, real-world market behavior often reflects the influence of psychological factors. Rather than treating behavioral bias as a single market force, this thesis compares how different biases appear across distinct market environments. Using an empirical, case-study-based approach, this study evaluates three recent market episodes: the COVID-19 market crash in 2020, the GameStop meme stock surge in 2021, and the rise of NVIDIA during the artificial intelligence boom from 2023 to 2024. Historical price and trading volume data are analyzed to identify patterns in investor behavior across these events. The findings suggest that behavioral biases appear differently depending on the emotional environment of the market. Loss aversion is most visible in the COVID-19 panic-selling case, herd behavior and overconfidence are most visible in the GameStop episode, and narrative-driven momentum is most visible in the NVIDIA case. Across all three cases, the observed price and volume patterns are consistent with behavioral finance theory. Overall, this study highlights the importance of behavioral finance as a complement to traditional financial models. The thesis shows that investor psychology can shape short-term market outcomes in different ways depending on whether the market environment is driven by fear, hype, or optimism.

Download

Share

COinS