Why does our Tail Reaper program work in times of market turmoil?

| | Trend-Following, Volatility

Guest post by Ernest Chan of QTS Capital Management

As the name of our Tail Reaper program implies, it is designed to benefit from tail events. It did so (+20.07%) during August-December, 2015’s Chinese stock market crash (even though it trades only the E-mini S&P 500 index futures), it did so (+18.38%) during February-March, 2018’s “volmageddon”, and now it did it again (+12.98%) during February, 2020’s Covid-19 crisis. (As of this writing, March is up over 21% gross.) There are many names to this strategy: some call it “crisis alpha”, others call it “convex”, “long gamma” or “long vega” (even though no options are involved), “long volatility”, “tail hedge”, or just plain old “trend-following”. Whatever the name or description, it usually enjoys outsize return when there is real panic. (But of course, PAST PERFORMANCE IS NOT NECESSARILY INDICATIVE OF FUTURE RESULTS.) Furthermore, our strategy did so without holding any overnight positions.

Why is a trend-following strategy profitable in a crisis? A simple example will suffice. If a short trade is triggered when the return (from some chosen benchmark) exceeds -1%, then the trade will be very profitable if the market ends up dropping -4%. Vice versa for a long trade. (As recent market actions have demonstrated, prices exhibit both left and right tail movements in a crisis.) The trick, of course, is to find the right benchmark for the entry, and to find the right exit condition.

Naturally, insurance against market crash isn’t completely free. Our goal is to prevent the insurance cost, which is essentially the loss that the strategy suffers during a stretch of bull market, from being too high. After all, if insurance were all we want, we could have just bought put options on the market index, and watched it lost premium every month in “good” times. To prevent the loss of insurance premium requires a dose of market timing, assisted by our machine learning program that utilizes many, many factors to predict whether the market will suffer extreme movements in the next day. In most years, the cost (loss) is negligible despite the long bull market, except in 2019 when we lost 8.13%. That year, which seems a long time ago, the SPY was up 30.9%. (It was in the August of that year that we added the machine learning risk management layer.) But most investors have a substantial long exposure. A proper asset allocation to both Tail Reaper and to a long-only portfolio will smooth out the annual returns and hopefully eliminate any losing year. (Again, PAST PERFORMANCE IS NOT NECESSARILY INDICATIVE OF FUTURE RESULTS.)

But why should we worry about a losing year?  Aren’t total return all investors should care about? Recently, Mark Spitznagel (who co-founded Empirica Capital with Nassim Nicholas Taleb) wrote a series of interesting articles. It argued that even if a tail hedge strategy like ours returns an arithmetic average return of 0%, as long as it provides outsize positive returns during a market crisis, it will be able to significantly improves the compound growth rate of a portfolio that includes both an index fund and the tail hedge strategy. I have previously written a somewhat technical blog post on this mathematical curiosity. The gist of the argument is that the compound growth rate of a portfolio is , where m is the arithmetic mean return and s is the standard deviation of returns. Hedging tail risk is not just for the psychological comfort of having no losing years – it is mathematically proven to improve long-term compound growth rate overall.

PAST PERFORMANCE IS NOT NECESSARILY INDICATIVE OF FUTURE RESULTS.

About the author

Ernest Chan, Ph.D., has been the Managing Member of QTS Capital Management, LLC., an CTA and CPO, since 2011. He has led research in the fields of Big Data, artificial intelligence and quantitative trading algorithms at IBM and Morgan Stanley, and has been a quantitative trader at Credit Suisse and other hedge funds since 1997. Ernie is the author of three popular books (Quantitative Trading, Algorithmic Trading, and Machine Trading) on quantitative finance, all published by Wiley. He also writes a well-known finance blog epchan.blogspot.com. He obtained his Ph.D. in theoretical physics from Cornell University.