We study how firm ambiguity—Knightian uncertainty—affects investor trading behavior using the options market as a laboratory. Greater ambiguity in the underlying asset negatively relates to both ...options open interest and options trading volume. The reduction in options trading activity is stronger for options with shorter maturities and out-of-the-money options that are hard-to-value. Greater ambiguity is also associated with a reduction in the informativeness of options trading for future stock prices, and it is associated with lower delta-hedged options returns for both puts and calls. The effect of ambiguity is distinct from and contrasts with the well-documented effect of risk, and it shares a similar economic significance. These findings illustrate that even sophisticated market participants, like options traders, are influenced by ambiguity to limit their market participation and trade less.
This paper shows that public information arrival affects investor beliefs and disagreement through a new channel: the uncertainty of beliefs (UOB). Based on novel daily measurement of belief ...uncertainty and disagreement, disagreement and trading are lower when UOB is higher. Higher UOB also dampens the relationship between disagreement and trading volume. The paper also highlights novel patterns of disagreement and trading around public news. For clarifying news like earnings announcements, information arrival reduces UOB, which naturally amplifies disagreement and trading. Consistent with learning, UOB decreases more for events with more attentive investors and for firms with a better information environment. By contrast, unscheduled events with opaque information information increase UOB while decreasing disagreement and trading.
We use machine learning to analyze minute-by-minute Bloomberg online status data and study how the effort provision of top executives in public corporations affects firm value. While executives ...likely spend most of their time doing other activities, Bloomberg usage data allows us to characterize their work habits. We document a positive effect of effort on unexpected earnings, cumulative abnormal returns following firm earnings announcements, and credit default swap spreads. We form long-short, calendar-time, effort portfolios and show that they earn significant average daily returns. Finally, we revisit several agency issues that have received attention in the prior academic literature on executive compensation.
We use machine learning to analyze minute-by-minute Bloomberg online status data and study how the effort provision of top executives in public corporations affects firm value. While executives ...likely spend most of their time doing other activities, Bloomberg usage data allows us to characterize their work habits. We document a positive effect of effort on unexpected earnings, cumulative abnormal returns following firm earnings announcements, and credit default swap spreads. We form long-short, calendar-time, effort portfolios and show that they earn significant average daily returns. Finally, we revisit several agency issues that have received attention in the prior academic literature on executive compensation.
Working Paper No. 23274 Previously, academics have used the supply of information that arrives to market (e.g., macroeconomic announcements, earnings reports, or news releases) to study how ...information affects asset prices and anomalies, and for tests of market efficiency. In this paper, we instead use measures of institutional and retail demand for information. We show that institutional demand for information is associated with increased trading volume and significant price movements. Average returns and betas are higher on days with higher institutional demand for information. The magnitude of these effects is much larger than those associated with the supply of news. However, the impact of demand for information from retail investors, while statistically significant, is quite small in magnitude. We also show that higher institutional demand alleviates mispricing in the market. In particular, higher information processing by institutional investors dampens momentum and enhances long-term reversals. As such, when demand for information increases, the market becomes more efficient.
Daily mutual fund (MF) flows are highly persistent and price-destabilizing, and short-sellers (SSs) trade strongly in the opposite direction to these flows. This negative relation is associated with ...the expected component of MF flows (based on prior days' trading), as well as the unexpected component (based on same-day flows). The ability of SS trades to predict stock returns is up to 3 times greater when MF flows are in the opposite direction. The resulting wealth transfer from MFs to SSs is most pronounced for high-MF-held, low-liquidity firms, and is much larger during periods of high retail sentiment.
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