Friday, February 20, 2009

A Quantitative Approach to Tactical Asset Allocation

I'm not a big fan of market timing and/or technical trading rules. From what I've seen, the empirical evidence casts a lot of doubt on their effectiveness.

But I just read a very interesting paper titled "A Quantitative Approach to Tactical Asset Allocation", by Mebane Faber. Here's the 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, together with over thirty-five consecutive years of positive performance.

The paper is definitely worth discussing in class. In most investment classes, there's at least some mention of the relations between arithmetic average returns, geometric average returns, holding period returns, and volatility. The reported returns to the strategy in the paper result in arithmetic average returns that are about the same (if not slightly less) than a buy/hold strategy. However, because of the lower volatility of this timing strategy,, it yields a significantly higher geometric average (and holding period) returns
It's also a good paper for a discussion on the return patterns to a market timing strategy vs. to a buy and hold one.

So, regardless of your views on market timing, it's worth a read: a short paper, interesting results, and written from a practitioner's viewpoint, so it's an easy read - even for an undergraduate.

You can download it from SSRN here.

Ah well, enough bloggery - back to work.