My Performance Numbers

My career path has not been a common one. Not many policy economists take a wrong turn into finance, and those who do, do so as analysts, strategists, or pundits. Then they stay there. Very few cross over into front-line risk taker/portfolio manager—and those who try tend not to survive. I was repeatedly told economists are rarely good investors and could never be good traders.

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I saw this as a great challenge—like learning to shoot a basketball with your off hand (which, back when I was playing basketball, wasn’t done). Plus, putting your money where your mouth also seemed like fun—a lot more fun than writing reports, making recommendations, and being a portfolio manager’s scapegoat. So, when given the chance to transition into actually managing money, I jumped at it.

Over the years I went from mutual fund sovereign analyst, to mutual fund portfolio manager, to senior risk taker in a trading-oriented hedge fund. Along the way I figured out why economists tend to be bad risk takers, and, most importantly, I discovered the primacy of risk management. I also got to know myself, my risk tolerances, natural biases, and how similar my cognitive shortcomings were to everyone else’s.

For the past six-plus years I have been running money for myself and a handful of “friends and family”. This has allowed me to vastly improve the quality of life for my family and me, and, almost equally importantly, to run money the way I want to, suiting my abilities and personality.

More specifically, I had had in my head a trading approach that I thought improved upon what I had been doing as a hedge fund manager, and I wanted to test it.

I now have six full years of running the Trading strategy. It’s levered, hyper-liquid, and macro and, on average, short term. When not in cash waiting for opportunities, it tends to be very aggressive. It relies a lot on pattern recognition, risk management and interpreting the pendular swings of market narratives.

This last January I had Ursa Fund Solutions compile a Schedule of Investment Performance from 2013-2019. I’m pleased to say that the results are, on the whole, good. Despite one disastrous year (actually, one disastrous quarter), the average annualized return was 24.3%. The cumulative return was 269%. I have attached that schedule here. I know six years is not a huge sample size, but, at a minimum, it’s an encouraging start.

There are some salient take-aways.

  1. The style is uncorrelated to the S&P. I haven’t run the numbers, but even a cursory glance at them (and me living them) suggest it’s true.
  2. It can also be uncorrelated to my own ideas. Anyone following me closely in 2018 knows how many things I got wrong—themes both big and small. Yet the fund returned 42 percent.
  3. Well over half of the returns were generated from only three assets: S&P futures, gold (precious metals), and the euro. (For those who are interested, bitcoin was the third largest contributor in 2018; and the outsized return in April 2013 was the collapse in gold and precious metals).

I didn’t have these numbers verified because I plan to launch a proper fund or raise money. I did it to put an official stamp on the process. And I wanted the stamp for three reasons: to show the subs on @behavioralmacro that the process works, and that hopefully can be learned from; to have hard numbers in my back pocket to shut up the occasional insistent naysayer who insists pattern recognition and TA can’t work, and, most of all, to document for myself that I had learned to shoot with my off hand.