Prior literature shows that implied volatility spreads between call and put options are positively related to future underlying stock returns. In this paper, however, we demon- strate that the volatility spreads are negatively related to future out-of-the-money call option returns. Using unique data on option volumes, we reconcile the two pieces of evidence by showing that option demand by sophisticated, firm investors drives the posi- tive stock return predictability based on volatility spreads, while demand by less sophis- ticated, customer investors drives the negative call option return predictability. Overall, our evidence suggests that volatility spreads contain information about both firm funda- mentals and option mispricing.

Using three natural experiments, we test the hypothesis that investor overconfidence produces overpricing of high idiosyncratic volatility stocks in the presence of binding short-sale constraints. We study three events: IPO lockup expirations, option introductions, and the 2008 short-sale ban on financial firms. Consistent with our prediction, we show that when short-sale constraints are relaxed, event stocks with high idiosyncratic volatility tend to experience greater price reductions, as well as larger increases in trading volume and short interest, than those with low idiosyncratic volatility. These results hold when we benchmark event stocks with non-event stocks with comparable idiosyncratic volatility. Overall, our findings suggest that biased investor beliefs and binding short-sale constraints contribute to idiosyncratic volatility overpricing.

Behavioral theories predict that firm valuation dispersion in the cross-section (‘‘dispersion’’) measures aggregate overpricing caused by investor overconfidence and should be negatively related to expected aggregate returns. This paper develops and tests these hypotheses. Consistent with the model predic- tions, I find that measures of dispersion are positively related to aggregate valuations, trading volume, idiosyncratic volatility, past market returns, and current and future investor sentiment indexes. Disper- sion is a strong negative predictor of subsequent short- and long-term market excess returns. Market beta is positively related to stock returns when the beginning-of-period dispersion is low and this rela- tionship reverses when initial dispersion is high. A simple forecast model based on dispersion signifi- cantly outperforms a naive model based on historical equity premium in out-of-sample tests and the predictability is stronger in economic downturns.

We study how investor composition is related to future return, trading volume, and price volatility in the cross- section of the music-content non-fungible tokens (music NFTs). Our results show that the breadth of NFT ownership negatively predicts weekly collection-level median-price returns and trading counts. In contrast, ownership concentration and the fraction of small wallets are positive predictors. The fraction of large NFT wallets is a bearish signal for future collection floor-price returns. Investor composition measures have weak predictive power on price volatility. Further analysis indicates that an artist’s Spotify presence moderates the predictive power of investor composition for future NFT returns and trading volume, consistent with the notion that reducing information asymmetry helps improve price efficiency.

We investigate the impact of severe snowfall induced operational disruptions on insider trading. Applying geospatial analytics to an extensive dataset of snow cover, we conduct granular analyses of snowstorms across firms at establishment level. When analyzing a sample of firms that operate in snowfall-impacted areas, we find that corporate insiders significantly adjust their trading behavior during these events. These insiders not only predict lower future returns but also increase the size of their sales in response to snowfall crises. Further, we explore the salience and operational insights channels through which snowfall triggers informed insider sales. Our findings show that insiders residing in impacted regions, as well as senior insiders with unique operational insights, effectively avoid losses during these periods. The snow intensity test reveals that these phenomena are more pronounced for snowstorms of greater severity. We also provide direct evidence that establishments under severe snow strikes experience lower total sales volumes. Our study highlights the capacity of insiders to anticipate and respond to weather-related business risks.

This paper investigates the impact of Covid-19 outbreak on households’ preferences for subway proximity, using housing transaction data from eight major cities with the highest metro commuting volumes. Contrary to what we expect from remote working which has been popular since Covid-19 outbreak, we find no evidence of a smaller housing price premium for subway proximity after the outbreak, based on a difference-in-difference empirical strategy.

This article applies natural language processing (NLP) to extract and quan- tify textual information to predict stock performance. Leveraging an exten- sive dataset of Chinese analyst reports and employing a customized BERT deep learning model for Chinese text, this study categorizes the sentiment of the reports as positive, neutral, or negative. The findings underscore the predictive capacity of this sentiment indicator for stock volatility, excess re- turns, and trading volume. Specifically, analyst reports with strong positive sentiment will increase excess return and intraday volatility, and vice versa, reports with strong negative sentiment also increase volatility and trading volume, but decrease future excess return. The magnitude of this effect is greater for positive sentiment reports than for negative sentiment reports. This article contributes to the empirical literature exploring sentiment anal- ysis and the response of the stock market to news on the Chinese stock market.

We construct a stock learning index in China A-share market, which is based on a theoretical model of information and investment choice. The higher the learning index value, the more thoroughly the individual stock is learned. Our study shows that a stock with a high learning index will have a lower expected future return compared to a stock with a low learning index. Additionally, decomposition of predictive power shows that the predictive power of the learning index mainly comes from the persistence of its own predictive power, while the rest cannot be explained by changes in the volume of news (proxy for information flow). Moreover, the learning index can explain many market anomalies in China A-share market.

This paper empirically evaluates the impact of a Government-Initiated Non-Profit Ecommerce Platform (GNEP) on specialty agricultural sales, focusing specifically on Pu’er tea in China. Using a difference-in-differences methodology and a comprehensive panel dataset that covers over 90% of local tea farmers, we uncover a marked substitution effect. The implementation of GNEP leads to an average decline of 11.22% in offline household sales, while online sales see an uptick of 16.88%. Further analysis confirms a universal channel shift from offline to online sales, irrespective of both production levels and tea quality. Contrary to expectations, the overall tea sales volume remains largely stable post-launch. Additionally, premium-quality teas experience a 2.42% price boost online, while regular teas show a 0.40% decrease compared to offline prices. Mechanism analyses further indicate that the increase in online sales is driven primarily by the intensive margin instead of the extensive margin. Although the platform does not significantly expand the number of farmers engaging in online sales, it succeeds in offering a cost-effective avenue for diversifying product offerings and achieving higher prices for premium-quality products. Our study illuminates the transformative role of e-commerce platforms in rural economic development and provides essential insights for policymakers and practitioners.

This paper studies how regulatory changes affect investors’ reactions at shareholder meetings in China. The objective of this paper is twofold: first, to analyse the information content transmitted to the shareholders of the largest Chinese companies listed on the China Securities Index 300 when an Annual General Meeting is held. A distinction is made between ordinary and extraordinary general meetings. Second, to find out if regulatory changes related to the Company Law of China and online voting in Annual General Meetings affect the information content of those meetings. The abnormal return obtained is examined through an event study using the Fama-French five-factor model. The results of our study indicate that the release of information and involvement of minority shareholders in general meetings during the research period led to higher return volatility and traded volume.