A recent article by Mark Hulbert in the NYTimes talked about the Value Line's rankings, and how this system is under-performing the market index in recent years. Mr. Hulbert asked Professor David Aronson of Baruch College whether this drop in performance means that the system has stopped working. Prof. Aronson says no: he believes that it takes 10 or more years [my emphasis] of under-performance of this strategy before one can say that it has stopped working! This statement, if taken out-of-context, is so manifestly untrue that it warrants some elaboration.
To evaluate whether a strategy has failed bears a lot of resemblance to evaluating whether a particular trade has failed. In my previous article on stop-loss, I outlined a method to determine how long it takes before we should exit a losing trade. This has to do with the historical average holding period of similar trades. This kind of thinking can also be applied to a strategy as a whole. If your strategy, like the Value Line system, holds a position for months or even years before replacing it with others, then yes, it may take many years to find out if the system has finally stopped working. On the other hand, if your system holds a position for just hours, or maybe just minutes, then no, it takes only a few months to find out! Why? Those who are well-versed in statistics know that the larger the sample size (in this case, the number of trades), the smaller the percent deviation from the mean return.
Which brings me to day-trading. In the popular press, day-trading has been given a bad-name. Everyone seems to think that those people who sit in sordid offices buying and selling stocks every minute and never holding over-night positions are no better than gamblers. And we all know how gamblers end up, right? Let me tell you a little secret: in my years working for hedge funds and prop-trading groups in investment banks, I have seen all kinds of trading strategies. In 100% of the cases, traders who have achieved spectacularly high Sharpe ratio (like 6 or higher), with minimal drawdown, are day-traders.
I believe the problem is that even when day-traders have their emotions in check, and know what they’re doing with respect to entries and exits, all too often they do not also have a statistically viable system whose expectancy is greater than the cost of trading. Costs, alone, can kill you, and beyond that, if your position size or variance is too large, well..., let’s just say it’s easy to blow yourself up. Day trading is best left to those with the necessary skill-sets and experience and who are also not swayed by advertisements that tend to lure those who are most unsuspecting. The problem is as much the industry’s fault as it is anything else.
Day Trading get's it's bad rap for good reason.
The old day trading model has beed displaced with the new - but nobody got the word out.
Actually how long it takes to verify (or disprove) a trading system has nothing to do with how frequently it trades. Fundamentally the question has to do with the error estimate on the Sharpe ratio, which is inversly proportional to the Sharpe ratio itself. Thus a very high Sharpe ratio strategy can be verified in a relatively short period of time. Low Sharpe ratio strategies take forever (10yrs or more) to achieve any kind of statistical significance.
A particularly obvious (and unrealistic) example is that of a strategy that never loses. All you need is a single loss to disprove it:)
I'd agree that in general it is the high frequency strategies that have high Sharpe ratios. And that therefore they are more easily confirmed than the others.
You have an interesting point, but I am not sure that the error estimate on the Sharpe ratio is solely determined by the inverse of the Sharpe ratio. It should be inversely proportional to the square root of the number of observations required to form that Sharpe ratio as well. (Clearly, if we just pick 3 consecutive winning days to calculate the Sharpe ratio, it will be very high indeed, but not really indicative of its long-term value!)
Hence I think both you and I are correct: the error on the Sharpe ratio is inversely proportional to both the Sharpe ratio and the sqrt of the number of observations. And for a fixed observation/backtest period, the number of observations is of course proportional to the frequency of trading.
Thank you for your insight.
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