Risk and Return of Meme Stocks (In Progress) - With Mo Chaudhury
The COVID pandemic of 2020-21 saw a surge in trading by retail investors and investment chats on social platforms that led to sudden volume spikes of a group of stocks, known as meme stocks. During this retail trading-meme mania, the meme stock returns surged but so did the risks, resulting in no superior alpha controlling for the six-factor extended Fama-French risk factors and idiosyncratic volatility. During this period, the extended model explains impressive 95% of the daily returns of the meme stocks portfolio. A trading ban that followed deflated the frenzy. Afterward, while the returns reverted back to pre-mania time, the total and idiosyncratic risks remained elevated. Only the meme leaders GMC and AMC ended up with negative market risk and better returns, but still no superior risk-adjusted performance. Throughout, the size premium is the only risk factor, other than the market risk, with a significant positive influence on meme stock returns.
Seeing is believing: The impact of corporate scandal documentaries on stock prices (In Progress) - With Gregor Dorfleitner
We investigate the behavior of stocks after the launch of Netflix’s scandal documentaries on the corresponding firms. We document a significant fall in prices after the release of the documentaries that is not reversed in the weeks following their launch, resulting in an average cumulative abnormal return of -15.34% three months after the event day. We also find a significant increase in stocks’ traded volumes and Google Search Volumes for the corresponding firms after the release of the documentaries. Moreover, we report a significant contemporaneous and lagged relation between stocks’ returns and traded volumes in the event window that is not seen before the release day. Taken together, these results suggest that the fall in stock prices is driven by investor attention. Our findings have significant implications for corporate misconduct and how market participants become informed and consequently price this behavior.
When to Bet Against Beta? Ask Google (link)
In this paper, I document that investor attention negatively predicts betting against beta returns. Using Google Search Volumes toward US market indices as my proxy to attention, I find that this relation holds after controlling for competitive factors and different search terminologies and in most of the other G7 countries. The results also indicate that investor attention presents a unique capacity to explain future BAB performance that is not shared by other famous variables, such as liquidity constraints, sentiment, lottery demand or volatility. On aggregate, the findings suggest that individual investors are a relevant barrier to arbitrage strategies such as BAB.
Naïve is as naïve does: The link between retail investor attention and value investing (link)
Individual investors are believed to trade on noise. Based on this assumption, this paper investigates whether noisy variables, such as price trends and market sentiment, attract more attention from these investors than value-related information such as the price-earnings ratio. The results suggest that price-earnings dynamics are more important in explaining changes in attention than noisy variables. Moreover, the negative sign exhibited by the value-attention relationship indicates that individual investors are more (less) attentive to stocks when they become cheaper (more expensive). I also demonstrate that this association is more representative during down markets but absent during positive periods, contradicting the stylized fact that retail traders are a driving force of bubbles. Furthermore, I find that these patterns are observable in all G7 countries. Overall, the results do not show that individual investors are consistent proxies for noise traders.
Finding the risk-return tradeoff with Google (link)
Investor attention is central to explaining the mean-variance puzzle. Using Google Search Volumes as a proxy to attention, I document a positive trade-off during low attention periods that is significantly undermined when attention is high. The negative association between on-line searches and the trade-off is also present in the time-varying analysis. I also find that this deterioration can be explained by the escalation of risk brought about by the entry of retail investors into the market. The results are robust for several alternative explanations, such as data periodicity, conditional variance measures, on-line search terminologies and macroeconomic variables, and provide further support for the importance of noise-traders to stock market inefficiency.