Researcher ORCID Identifier
0009-0003-7632-0510
Graduation Year
2025
Date of Submission
12-2024
Document Type
Open Access Senior Thesis
Degree Name
Bachelor of Arts
Department
Economics
Reader 1
Murat Binay
Terms of Use & License Information
Rights Information
© 2024 Ingrid Guo
Abstract
This thesis examines the performance of two distinct sector-based portfolios — psychologically-based and non-psychologically-based — during three major economic downturns: the Dotcom Bubble (2000), the Great Recession (2008), and the COVID-19 Recession (2020). The psychologically-based portfolio includes industries like alcohol, beauty, and streaming services, which appeal to consumers seeking comfort or affordable indulgences during financial uncertainty. In contrast, the non-psychologically-based portfolio comprises sectors like healthcare and consumer staples, which provide essential goods and services regardless of economic conditions. Through regression analysis, this study evaluates the relative returns of both portfolios and compares them with each other. The results suggest that while the psychologically-based portfolio exhibited slightly higher average excess returns, there was no statistically significant difference between the performance of the psychological-based and non-psychologically-based portfolios. However, both portfolios demonstrated low beta, were comprised of large-cap and value stocks, and proved effective in lowering risk, making them viable hedging strategies during recessions. These findings have important implications for hedge funds and institutional investors seeking to protect their portfolios from volatility during economic downturns. Future research may explore how real-time data and different portfolio construction methods can further refine recession-proof investing strategies.
Recommended Citation
Guo, Ingrid, "Recession Resilience: Hedging with Psychologically-Based and Essential Sectors in Defensive Portfolios Amid Economic Turmoil" (2025). CMC Senior Theses. 3781.
https://scholarship.claremont.edu/cmc_theses/3781