Nothing lasts forever and this definitely stands true for equity markets where volatility can explode and investors can lose a lot of money very quickly. Because of equity market volatility investors often seek so-called “crisis alpha” instruments, or assets that tend to go up when equity markets are in crisis. (Here, here, and here are some prior write-ups on the topic). Unfortunately, identifying crisis-alpha strategies is difficult because investors need to venture into uncommon and esoteric asset classes and strategies.
Fortunately, we are Quantpedia are continually building a database of ideas for quantitative trading strategies derived out of the academic research papers. Motivated by the recent fall of the S&P500 index at the end of 2018, we have added a new “hedge/diversification” filtering field into our Screener. This screen links to strategies that seek to minimize/offset the equity market risk factor during bear markets. One strategy, “The Skewness of Commodity Futures Returns,” caught our attention and is related to the “lottery effect” in commodities.(1) In this piece, we 1) briefly discuss the lottery effect, 2) we discuss the research on this topic in the context of commodities, and 3) we conduct an independent replication effort of the commodity lottery effect identified in academic research.
The Lottery Effect
Lottery stocks are commonly described as stocks that have a probability of a large payoff with a small probability (i.e., high skewness). Individual investors tend to prefer stocks with lottery-like payoffs in the search for the as high profits as it is possible and they are willing to play the equity lottery. Unfortunately, in lottery games, there are a small number of winners, a large number of losers, and one happy lottery ticket issuer that has profited from selling the lottery tickets.
Next, we look at the lottery effect in the context of commodities.
Skewness and commodities
There is strong evidence that investors have a preference for lottery-like assets (the assets that have a relatively small probability of a large payoff or in other words, big skewness) and research has identified that these assets also include commodities. An academic paper written by Fernandez-Perez, Frijns, Fuertes, and Miffre (Commodities as Lotteries: Skewness and the Returns of Commodity Futures) has found that investors may prefer commodities with positive and high skewness.
Here is the abstract from the paper (emphasis our own):
This article studies the relation between the skewness of commodity futures returns and expected returns. A trading strategy that takes long positions in commodity futures with the most negative skew and shorts those with the most positive skew generates significant excess returns that remain after controlling for exposure to well-known risk factors. A tradeable skewness factor explains the cross-section of commodity futures returns beyond exposures to standard risk premia. The impact that skewness has on future returns is explained by investors’ preferences for skewness under cumulative prospect theory and selective hedging practices.
Our goal in this article is to perform an independent analysis of the article’s findings.
Skewness and commodities: Replication
Data and the strategy
Our investment universe consists of 22 commodity futures: soybean oil, corn, cocoa, cotton, feeder cattle, gold, copper, heating oil, coffee, live cattle, lean hogs, natural gas, oats, orange juice, palladium, platinum, soybean, sugar, silver, soybean meal, wheat and crude oil.
The backtesting period spans from 4/30/1992 to 1/31/2019. Our analysis started by calculating skewness each month from daily returns by going 12 months into the past for all futures in our sample. Next, we rank the commodities each month based on their skewness. The final execution of the strategy consists of going long four commodities with the lowest skewness and shorting four commodities with the highest skewness. The strategy is rebalanced monthly and each commodity has equal weight in the portfolio.
Clearly, the strategy is profitable, a dollar invested in 1992 would result in 9.45 dollars by 2019, which results in a yearly performance of 8.43%. Moreover, the risk of the strategy is relatively low, with the maximal drawdown of 16.27%, which results in a return to a drawdown ratio of 0.52. Not terrible.
Equity market and commodity skewness strategy
In the previous section, we show that the long/short commodity skewness strategy is reasonable on a stand-alone basis. However, a more interesting question is how this strategy performs during equity bear markets.
The S&P500 index is negatively correlated with the performance of the commodity skewness strategy. Moving into numbers, the correlation between the S&P500 index and the skewness commodity strategy is -0.32.
Moreover, this correlation is even more negative when the index was below its own 10 months moving average. During these times, the correlation is still negative at -0.36.
Next, we look at the worst months in the S&P 500 Index (blue) and compare it with the performance of the strategy (orange).
During our backtesting period, when the monthly performance of the S&P500 index was negative, the average return was -3.49%, while the average return during those months of the commodity strategy was 1.10%. A similar, but more obvious pattern, can be observed if we would look upon three months overlapping returns for the S&P 500 Index and the commodity skewness strategy.
During most of three months periods when the equities perform poorly the commodity strategy is actually profitable.
The lottery/skewness effect in commodities is attractive and appears to act as a portfolio insurance device akin to generic trend-following managed futures strategies. However, what we find interesting about this commodity skewness strategy is that this is not a trend-following strategy and may offer something unique and/or different for a portfolio.
Let us know what you think.
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