Consider two statistically identical strategies (identical information ratios, sample size, ratio of transaction costs to total profit, etc.) except that one has a much shorter average holding period. Is there a statistical reason to favor one over the other?

At first blush, it would appear the statistical confidence in the expected return to the two strategies is identical. One way to measure statistical confidence is to regress the P&L time-series on a constant and examine the t-statistic. Using OLS, these two strategies would yield identical t-stats. However, the longer holding period strategy may be expected to have greater serial correlation, and so perhaps we should adjust the standard errors. Of course, even short horizon strategies can display autocorrelation in P&L beyond the holding period, so perhaps this is not a reason to prefer one over another.

My intuition says I should prefer the short holding period strategy, but I have been unable to find a solid reason why, and in particular how I would measure the strength of this preference.

Note: This question is a follow-up to my previous question:
How much data is needed to validate a short-horizon trading strategy?

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    $\begingroup$ I don't have a quantitative answer, but I prefer shorter holding periods simply because they require less capital commitment. All else being equal, it's less of a headache. $\endgroup$ Sep 14, 2011 at 23:57

1 Answer 1


This is a partial explanation in that trading strategies with longer horizons have higher information ratios, t-statistics, slope coefficients, and R^2 in general.

In other words, if information ratios for both strategies are identical then the longer-term trading strategy is already worse.

John Cochrane illustrates how longer horizons have higher t-stats and information ratios in various lectures including "Discount Rates" -- a fantastic read for its other ideas as well.

Fama and French (1998) initially documented the fact that long-horizon models have higher fits. This spawned a slew of statistical research to understand why this peculiar feature would hold.

This research is summarized in Boudoukh, Richardson, and Whitelaw (2006) who find that it is not the case that predictability somehow "emerges" at long-horizons but rather higher R^2 occurs when you have i) persistency (auto-correlation) of the predictive variable and ii) overlapping returns, and therefore iii) common sampling error in the coefficient estimates.

If you can contrive a scenario where these effects do not hold or are controlled for then your question stands. On that front, I would argue that shorter-term horizon trading strategies generate optionality and so should be preferred to longer-horizon models.


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