Analyzing the Impact of Economic Policy Uncertainty and Investor Sentiment on Stock Market Dynamics (Returns & Volatility)
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Date
2024-09-12
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University of Liverpool
Abstract
This dissertation investigates the joint effects of Economic Policy Uncertainty (EPU) and investor sentiment on stock market dynamics, particularly focusing on the S&P 500 index. The study integrates sentiment analysis from real-time news and social media data with EPU indices to develop predictive models for stock returns and volatility over a 10-year period (2013-2023). By employing econometric techniques, such as LASSO regression, Ordinary Least Squares (OLS) regression, and GARCH models, the study aims to provide a more comprehensive understanding of how these psychological and macroeconomic factors influence market behavior. The findings highlight that investor sentiment plays a stabilizing role in periods of positive sentiment, reducing market volatility and enhancing stock returns. In contrast, negative sentiment amplifies volatility, especially when combined with high levels of policy uncertainty. EPU, particularly as measured by the News-Based Policy Uncertainty Index, emerges as a critical driver of volatility, affecting market stability during periods of fiscal and trade policy uncertainty. The interaction between sentiment and EPU is shown to provide better predictive accuracy for stock market behavior compared to traditional financial models. The research contributes to the growing body of literature by developing models that integrate real-time sentiment data with EPU, offering more nuanced insights into stock market volatility and returns. The practical implications are significant for both investors and policymakers, providing tools to improve risk management and decision-making. Investors are advised to consider sentiment and policy uncertainty together when assessing market risks, while policymakers are encouraged to ensure transparent communication to minimize uncertainty and stabilize markets. This study advances our understanding of the roles of sentiment and policy uncertainty in financial markets, highlighting their combined influence on stock market volatility and returns, and offering practical strategies for navigating periods of economic uncertainty.
Description
This thesis investigates the combined effects of Economic Policy Uncertainty (EPU) and investor sentiment on stock market dynamics, specifically focusing on the S&P 500 index. The study integrates sentiment analysis from real-time news and social media data with EPU indices to develop predictive models for stock returns and volatility over a 10-year period (2013-2023). Using econometric techniques such as LASSO regression, Ordinary Least Squares (OLS) regression, and GARCH models, the research aims to provide a comprehensive understanding of how psychological and macroeconomic factors influence market behavior. The findings suggest that positive investor sentiment stabilizes the market, reducing volatility and enhancing returns, while negative sentiment increases volatility, especially when combined with high policy uncertainty. The study highlights the importance of considering both sentiment and policy uncertainty in risk management and decision-making for investors and policymakers.
Keywords
conomic Policy Uncertainty, Investor Sentiment, Stock Market Dynamics, S&P 500 Index, Stock Returns, Market Volatility, Sentiment Analysis, Predictive Models, LASSO Regression, Ordinary Least Squares (OLS) Regression, Social Media Data, Policy Uncertainty, Financial Markets