Miscellaneous

Setting Expectations for Monthly Trading Systems

Systematic researchers overwhelmingly use monthly holding periods to test strategies. This is probably driven by the availability of long-term monthly total return data for a wide variety of indexes, where daily data is more scarce. This is fine to a point, but investors may not be aware of just how sensitive results might be to day-of-the-month effects which may not persist out of sample.

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World Cup Outcomes Are Mostly Random: So Who Cares?

This post will be short and sweet as it’s largely an addendum to our previous post NFL Parity, Sample Size and Manager Selection.  It was motivated primarily by an interesting analysis by Tom Murphy, a physics professor at the University of California – San Diego. We greatly admire Dr. Murphy and highly recommend his blog.

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The Evolution of Optimal Lookback Horizon

Many asset class rotational systems are optimized on lookback horizon (to our observation, most use 120 days), so we thought it would be interesting to investigate the evolution of optimal lookback horizon through time. This allows us to put ourselves in the position of an analyst at different points in history and try to speculate on the choices he might have made given the information at his disposal then.

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Why Skill Never Prevails in Your NCAA March Madness Office Pool

As quants and sports fans we often find ourselves analyzing statistics from the sports world. And seeing as college basketball dominates the sports landscape for the next few weeks, it’s no surprise we are inspired to write about the NCAA Men’s Basketball Tournament, aka March Madness.

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NFL Parity, Sample Size and Manager Selection

We’ve been discussing issues around statistical significance – most notably, what makes a tested model’s results significant and therefore likely to perform in a consistent fashion when implemented in real time. In our last article we discussed what constitutes robustness in the context of testing a trading model.

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