June 23, 2019

The following Quantopian post was to comment on the following: “There is such a thing as skill, but my read is that proving it might take a lifetime.” To which I agreed and added:

That kind of study has been done. It turns out it would take some 38 years for a professional money manager to show skill prevailed over luck at the 95% level based on sufficient data (10 years and more). No one is waiting or forward-testing for that long. And even if they did, they would again be faced with the right edge of their portfolio chart: uncertainty, all over again.

In all, it would be a monumental waste of time, opportunities, and resources. It is partly why we tend to do all those backtests just to demonstrate to ourselves that over some past data, our strategies succeeded in some way to outperform or not. Evidently, we will throw away those strategies that have little or no value whatsoever. If a trading strategy cannot demonstrate it could have survived over extended periods of time, why should we even consider them in our arsenal of trading strategies going forward?

Often, in the trading strategies I look at, the impact of the high degree of randomness in market prices is practically totally ignored. As if people are trying to look at the market price matrix P as if a database of numbers from which they can gather or extract some statistical significance of some kind, either at the market or stock level. And from there, trying to make some sense of all those numbers using all types of analysis methods.

Only under a high degree of randomness can any of the following methods have their moments of coincidental predominance, enough to make us think they might have something kind of predictive. Whether it be a sentiment indicator trying to show the wisdom of crowds, machine learning, deep learning, or artificial intelligence, they will all have their what-if moments. Should you use technical indicators, parameters, factors, residuals, principal component analysis, wavelets, multiple regressions, quadratic functions, and more, it might not help either in deciphering a game approaching a heads or tails type of game. Upcoming odds will change all the time and even show somewhat unpredictable momentary biases.

Nonetheless, it is within this quasi-random trading environment that we have to design our trading strategies. Design it in such a way as to not only provide positive results and outperform market averages but also, at the same time, outperform our peers, not just over the immediate momentum thingy but ultimately over the long term.

The end game is what really matters. Can our trading strategies get there? That is the real question. What kind of game can we design within the game that will allow us to outperform market averages and our peers? What kind of trading rules should we implement in order to do so? This goes back full circle to what our trading strategy H does, to how we manage our stock inventory, and what will be the outcome of our forward-looking payoff matrix:

Σ(H(ours)∙ΔP) > Σ(H(peers)∙ΔP) > Σ(H(spy)∙ΔP) > Σ(H(others)∙ΔP) ?

Created. June 23, 2019, © Guy R. Fleury. All rights reserved.