Market Logic

What should we test when we test technical trading rules?

Posted in economics, finance by mktlogic on May 13, 2009

I recently read Mebane Faber’s paper, A Quantitative Approach to Tactical Asset Allocation, which is apparently quite popular on SSRN.

Abstract:
The purpose of this paper is to present a simple quantitative method that improves the risk-adjusted returns across various asset classes. A simple moving average timing model is tested since 1900 on the United States equity market before testing since 1973 on other diverse and publicly traded asset class indices, including the Morgan Stanley Capital International EAFE Index (MSCI EAFE), Goldman Sachs Commodity Index (GSCI), National Association of Real Estate Investment Trusts Index (NAREIT), and United States government 10-year Treasury bonds. The approach is then examined in a tactical asset allocation framework where the empirical results are equity-like returns with bond-like volatility and drawdown.

Early in the paper Faber presents a simple and mechanical market timing procedure:

BUY RULE: Buy when monthly price > 10-month SMA.
SELL RULE: Sell and move to cash when monthly price < 10-month SMA.

The remainder of the paper describes the performance of a hypothetical portfolio that adheres to these two rules.

Faber’s procedure is, in fact, one instance in a class of procedures of the form “Buy (sell) when the price is above (below) the N-period SMA.” Whenever I see papers like his, I’m curious as to why the focus is on testing the instance rather than the class. That is, how would performance have been if the wrong lookback had been used?

I’m sure that there are plenty of similar classes of procedures based on channel breakouts, trendlines, volatility bands and so on that outperform buy and hold over the same period with the right lookback. Such procedures would probably also work well enough with non-optimal lookbacks.

Another class of market timing procedure might be “Buy on Jan 1, 1900 and switch back and forth from assets to cash every N days.” I’m very sure that for the right values of N, this timing procedure can generate results better than buy and hold. I’m also confident that for the wrong values of N this procedure would work very poorly.

This isn’t a complaint about data mining for the right lookback to make a trading strategy seem better than it is. (To his credit, Faber’s paper includes out of sample tests of his procedure.) Rather, knowing how well a market timing procedure works for any given lookback period is just not that useful. Since it is impossible to know the optimal lookback ahead of time, the relevant question either for tests of market efficiency or for active management is “How well does the class of market timing procedures work when the lookback used is non-optimal?”

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