Three essays on the time series of returns

This dissertation consists of three essays on the time series of asset returns. The first essay in Chapter 1--Time-Varying Drivers of Stock Prices--provides novel evidence of the time-varying roles of subjective expectations in explaining stock price variations across the market and 30 industry portfolios monthly from 1976 to 2020. Cash flow expectations matter more under financial uncertainty and recessions, especially among the hardest-hit industries such as Telecommunications during the Dot-com Bubble, Financials during the Great Recession, and Healthcare during the Covid-19 pandemic. Conversely, discount rates explain more price variations during expansionary periods. Finally, inflation expectations, while accounting for 60 percent of price fluctuations in the high inflationary environment before 2000, play a negligible role thereafter. In the second essay in Chapter 2--Investor Sentiment and Asset Returns: Actions Speak Louder than Words--I analyze daily predictability of investor sentiment across four major asset classes and compares sentiment measures based on news and social media with those based on trade information. For the majority of assets, trade-based sentiment measures outperform their text-based equivalents for both in-sample and out-of-sample predictions. This outperformance is particularly noticeable in long-term forecasts. However, real-time mean-variance investors can only achieve economic gains using Bitcoin trade sentiment, suggesting the challenge of transforming sentiment into daily profitable trading strategies. In the last essay in Chapter 3--War Discourse and Disaster Premia: 160 Years of Evidence from Stock and Bond Markets--using a semi-supervised topic model on 7,000,000 New York Times articles spanning 160 years, I test whether topics of media discourse predict future stock and bond market returns to test rational and behavioral hypotheses about market valuation of disaster risk. Focusing on media discourse addresses the challenge of sample size even when major disasters are rare. Our methodology avoids look-ahead bias and addresses semantic shifts. War discourse positively predicts market returns, with an out-of-sample R2 of 1.35 percent, and negatively predicts returns on short-term government and investment-grade corporate bonds. The predictive power of war discourse increases in more recent time periods.

Name thesis:degree_name

Ph. D.

Level thesis:degree_level

Doctoral

Discipline thesis:degree_discipline

Business administration (MU)

Grantor dc:publisher

University of Missouri--Columbia

Year dc:date.issued

2023

Author dc:creator

• Mai, Dat Y

Advisor dc:contributor.advisor

• Pukthuanthong, Kuntara

Language dc:language.iso

eng, English

DOI

https://doi.org/10.32469/10355/96991

OAI identifier oai:identifier

oai:mospace.umsystem.edu:10355/96991