Research

Abstract: 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 wellknown Fama–French five-factor model as well as the corresponding principal component analysis, partial least squares, and least absolute shrinkage and selection operator 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.

Abstract: Our paper shows that greater index ownership of a stock is associated with the average active fund trade in that stock appearing more informed. At the extensive margin, less-informed active funds are more likely to exit positions when index ownership increases in a stock. At the intensive margin, the remaining active funds tend to execute more informed trades in the stock, and this increase in informed trading is larger when the value of stock-specific information is greater. Our findings provide evidence that endogenous responses by active funds likely explain the lack of evidence that indexing negatively impacts stock price informativeness.

Abstract: Ideology often shapes belief formation, which is central to asset pricing. However, the role of ideological narratives as a source of asset pricing risk remains largely unexplored. Using cryptocurrencies as a laboratory, I examine the role of two ideological narratives—anarchism and decentralization—in the cross-section of cryptocurrency returns. Leveraging social media data and large language models to measure ideology dynamics, I find that fluctuations in these two ideology dynamics are priced in the cross-section of cryptocurrency returns. A two-factor model based on ideological narratives explains the cross-section of cryptocurrency returns better than a three-factor model of crypto market, size, and momentum. 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. Consistent with the view that factors proxy for state variables, ideology factors contain distinct information about future crypto market returns and user network growth. 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.

Abstract: We provide a novel performance analysis of three widely used survey forecasts along with naive and analyst predictions. We find that none of the popular survey forecasts can predict the stock market out-of-sample, and the surveys are not very informative about investors’ attitudes toward risk. Our study raises important questions on how to safeguard the use of survey forecasts and how to properly interpret the results from the large literature that relies on them. On the other hand, we show that a naive Bayesian learning and forecasts using analysts’ expectations can outperform the surveys, suggesting that the study on these can potentially be more important and deserve more attention than the study of survey forecasts.