Recent Academic Research
Federal Reserve officials' facial expressions, volatility and momentum trading, chicken and egg, and extreme weather.
The third post! I want to thank all of you who have recently subscribed. I hope you enjoy this one, as it may be the most interesting one yet (yes, I know it’s only the third post, but still). Five recent research papers by PhDs, distilled for practitioners. Cutting through the noise. Let’s get into it.
The Federal Reserve
Trading off of the Fed’s Facial Expressions
Paper Title: Strategic Control of Facial Expressions by the Fed Chair
Authors & Date: Hunter Ng - 10/26/2024
Summary:
The paper's findings suggest that investors do not simply react to the literal meaning of a Fed Chair's words, but also consider their nonverbal cues. The study shows that investors react negatively to facial expressions communicating negative emotions like anger and fear, even when the Fed Chair's verbal statements are not negative. These negative expressions are associated with increased market volatility, as measured by changes in stock prices, the VIX volatility index, and currency exchange rates. This suggests that facial expressions are processed as a separate and powerful communication channel that can influence investor behavior. The paper concludes that Fed Chairs may not be strategically controlling their facial expressions to minimize market volatility, as evidenced by the increase in negative expressions as Chairs gain experience and tenure, despite knowing the negative market reaction.
Thoughts:
As a Fed official’s tenure increases, their negative facial expressions become more frequent! It seems that serving as a Fed official is not an enviable position. I mean, you are scrutinized by thousands of very smart people for every decision that you make. It can’t be that fun of a job. Another insight I found was that investors process facial cues with dual-processing finite-state Markov memory. In short, that means that investors have an initial, emotionally-driven reaction (System 1) followed by a slower, analytical assessment (System 2). Hmmm. That sounds a lot like the basic takeaways from the book Thinking Fast and Slow. I wonder if an investment process to trade based on the emotional driven reaction would create an edge.
Volatility Trading
Difference in Bond and Equity Volatility Creates Opportunities
Paper Title: Divergence of Fear Gauges and Stock Returns
Authors & Date: Xinfeng Ruan & Xiaopeng Wei - 10/28/2024
Summary:
Divergence of Fear Gauges (DFG) measures the relative difference between implied volatility in the Treasury bond market (measured by the MOVE index) and implied volatility in the equity market (measured by the VIX index). A higher DFG suggests that investors anticipate greater volatility in the bond market compared to the stock market. To calculate DFG, the authors regress MOVE on VIX and utilize the residuals from this regression as their primary DFG measure. Empirical analysis covering the period from 2004 to 2022 demonstrates that DFG negatively predicts subsequent stock market returns. This implies that when DFG is high, indicating relatively greater volatility in the bond market, future stock market returns are likely to be lower. This predictive power of DFG persists even when controlling for other factors like the "flight to safety" effect and risk aversion.
Thoughts:
Well this one seems interesting. While it isn’t as simple as a difference between the prices of the indices, this strategy seems relatively easy to implement. I wonder how quickly this will get implemented (if it proves to be a significant indicator in a portfolio). Maybe the most important point from this paper is that the predictive power of DFG is still large even after controlling for other macroeconomic variables. Therefore, the predictive power in this model may be derived from a mispricing in the market. While this may not be applicable today, as the VIX has been outpacing the MOVE over the year, it may be something to keep an eye on.
Momentum Trading
Momentum Exploitation is Back
Paper Title: How to Improve Commodity Momentum Using Intra-Market Correlation
Authors & Date: Radovan Vojtko & Margaréta Pauchlyová - 10/29/2024
Summary:
This paper explains how to use an intra-market correlation filter to improve the performance of momentum strategies in commodity markets, especially given that momentum strategies have been less effective recently. When the short-term correlation among commodity ETFs surpasses the long-term correlation, it suggests that commodities are trending in a unified direction. This allows momentum strategies to differentiate between winners and losers more effectively. Under these conditions, the paper recommends a momentum strategy that goes long on the top-performing ETFs and short on the underperformers. Conversely, when short-term correlation falls below the long-term correlation, a reversal strategy is recommended. This involves going long on the worst-performing ETFs and short on the best-performing ones. This method of using the correlation filter to determine when to apply momentum and reversal strategies doubles returns while keeping risk at acceptable levels.
Thoughts:
Momentum was one of the first highly documented inefficiencies in the market. Since the 1993 paper by Jegadeesh and Titman was released, there have been countless papers on momentum in various asset classes and in markets around the world. However, if you try this strategy today, you will likely be disappointed with the results. But, since you stumbled across this paper (and this newsletter!), you now know of another way to (hopefully) exploit momentum in the markets. I’ll be testing this idea out among equities in the near future, so stick around for that. Maybe I shouldn’t be sharing the paper in this post…
Futures
Chicken and Egg Debate: Volatility and Returns
Paper Title: Which Way Does the Wind Blow Between SPX Futures and VIX Futures?
Authors & Date: Ekow Aikins & Alexander Kurov - 10/28/2024
Summary:
This paper examines the causal link between returns on the E-mini S&P 500 futures (ES), a proxy for stock market returns, and VIX futures (VX), representing changes in expectations of implied volatility. They note that while a negative correlation between stock market returns and volatility is widely recognized, the direction of causality remains debated in the existing literature. Their findings reveal that ES has a stronger and more consistent impact on VX than the other way around, suggesting causality flows from stock returns to volatility expectations. This pattern persists across different sample periods, indicating changes in stock futures prices drive adjustments in VIX futures prices, reflecting altered expectations of future volatility. The paper bolsters this conclusion by analyzing the relationship between market returns and realized volatility. They find that recent stock returns predict future realized volatility. This supports the idea that stock returns influence expectations of implied volatility because they contain information about future actual volatility. In simpler terms, VIX futures respond strongly to changes in E-mini S&P 500 futures because the latter provide insights into future volatility trends.
Thoughts:
The chicken and the egg debate began from a simple question: Which came first? Now, I unfortunately will not be answering that question for you. However, there is a similar question in the markets: Do equity returns influence market volatility, or does market volatility influence equity returns? It turns out the answer is the former. This paper shows that stock market returns cause not only volatility expectations, but also future realized volatility. Like the paper states, there is a clear relationship here. Stock returns contain information on changes in future implied volatility, and implied volatility contains information on changes in future realized volatility.
Behavioral Economics
Bad Weather Equals Less Investments
Paper Title: Extreme Weather and Household Stock Market Participation
Authors & Date: Xuefeng Xiang & Shuran Wang - 10/28/2024
Summary:
The study, using data from the China Household Finance Survey and county-level weather data, finds that more frequent extreme weather events in China correspond with fewer households owning stocks. This is likely due to heightened risk aversion, lower household income from agriculture and wages, and greater health risks caused by extreme weather. Households with higher financial literacy and those that pay close attention to financial news are more likely to reduce their stock holdings when extreme weather occurs, suggesting they are more aware of the associated risks. However, the impact is weaker for households with more brothers, indicating a potential role for familial risk-sharing. These findings suggest that a change in local climates, through its impact on extreme weather, could reshape financial markets and wealth distribution.
Thoughts:
While this paper takes data from households in China, it is probably safe to relate this to other markets, including the American stock market (don’t come after me if it doesn’t). Anyways, the findings are still interesting. While these households do reduce their ownership in the stock market, the study did not explore the corresponding return of equity markets after an extreme weather event. Maybe it is advantageous to take advantage of this change in sentiment, and buy when others sell? Also, weirdly enough, the paper found that the more brothers that a household has, the less of an effect that extreme negative weather has on reducing investments. I guess brothers do not care as much about the weather?
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Disclaimer
The content provided in this newsletter, "Alpha in Academia," is for informational and educational purposes only. It should not be construed as financial advice, investment recommendations, or an offer or solicitation to buy or sell any securities or financial instruments. Past performance is not indicative of future results. The financial markets involve risks, and readers should conduct their own research and consult with qualified financial advisors before making any investment decisions.
The interpretations, opinions, and analyses presented herein are those of the author and do not necessarily reflect the views of the original researchers, their institutions, or the full implications of the cited academic papers. While every effort is made to accurately represent the research discussed, readers should be aware that the summaries and interpretations may not capture the full scope or nuances of the original studies. The information contained in this newsletter is believed to be accurate and reliable at the time of publication, but accuracy and completeness cannot be guaranteed. The author and publisher accept no liability for any loss or damage resulting from reliance on the information provided.
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Appreciate your blog! Well done, like that a lot, keep going. Thanks!