Abstract

My dissertation explores two broad questions. First, what is the role of intangible forces like ideological narratives in belief formation and asset prices? Second, how can we understand the systematic components in asset pricing? In Chapter 1, using cryptocurrencies as a laboratory, I examine the role of ideological narratives in asset prices—an area remains underexplored. Leveraging social media data and large language models to measure ideology dynamics, I find that fluctuations in two ideological narratives—anarchism and decentralization—are priced in the cross-section of cryptocurrency returns. Consistent with the view that factors proxy for state variables, ideology factors contain distinct information about future crypto market returns and user network growth. Positive shocks to ideology salience are associated with a significant positive spread between more ideology-aligned and less aligned cryptocurrencies, indicating a relative increase in demand for more aligned cryptocurrencies when collective attention to ideological narratives heightens. Neither investor sentiment nor attention explains the results of the ideology factors. Moreover, the role of ideological narratives extends beyond cryptocurrencies. Stocks with greater exposure to the anarchism narrative yield abnormally high returns that cannot be explained by common stock factor models. The results highlight how ideological narratives contribute to the emergence and adoption of new assets. In Chapter 2, with Ai He, Dashang Huang, and Guofu Zhou, we provide a reduced-rank approach (RRA) to extract a few factors from a large set of factor proxies, and apply the extracted factors to model the cross section of expected stock returns. Empirically, we find that the RRA five-factor model outperforms the well-known Fama-French five-factor model as well as the corresponding PCA, PLS and LASSO models for pricing portfolios. However, at the stock level, our RRA factor model still has large pricing errors even after adding more factors, suggesting that the representative factor proxies of our study do not have sufficient information for pricing individual stocks. In Chapter 3, with Songrun He, Lingying Lv, and Guofu Zhou, we find that three widely used survey forecasts fail to predict the stock market out-of-sample, raising important questions about the reliability of survey forecasts and the proper interpretation of the extensive literature that depends on them. In contrast, we demonstrate that a naive Bayesian learning model and analysts’ expectations can significantly predict the stock market out-of-sample. This suggests that these alternatives provide more meaningful insights into investors’ attitudes toward risk. As a result, studying these new sources of information may be more impactful and warrants greater attention compared to the reliance on survey forecasts.

Committee Chair

Asaf Manela

Committee Members

Andreas Neuhierl; Guofu Zhou; Todd Gormley

Degree

Doctor of Philosophy (PhD)

Author's Department

Finance

Author's School

Olin Business School

Document Type

Dissertation

Date of Award

5-22-2025

Language

English (en)

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