Adaptive Market Timing with ETFs

18 Pages Posted: 29 Dec 2010

Date Written: December 28, 2010

Abstract

A method was earlier devised to harvest most of the potential gain in bull markets while avoiding most of the pain in bear markets. The method focuses on a buy-the-market and hold strategy when measured volatility is low. When this condition is violated, a moving average look-back with volatility (MALBV) algorithm is employed which has the investor in a long position at day n if and only if the ratio of the k-day simple moving average (SMA) at day n-1 to the same average at day n-j-1 exceeds a specified fixed value corresponding to an annual rate of return of i% (compounded daily) in the j-day period. Otherwise a cash position is taken. It was found that the values k = 200, j = 20 and i = 3 or 6 produced good results when applied to 6 of the most popular ETFs, yielding a highly diversified portfolio, and the results were shown to achieve the stated goal with an average of less than 1 trade per year.

Attempts to extend this algorithm to other ETFs and equities with the same parameter set produced inconsistent results. Here we report on a systematic approach to determine whether historical data can be used to help select appropriate parameters. It was found that each ETF or equity appears to have characteristic parameter sets that optimize performance. While the characteristic sets change over time, the number of such sets is small and the period over which each set is optimum is usefully long, typically a year or more. Furthermore, characteristic sets that are superseded by newer sets can still be used to produce results superior to both buy-and-hold and the one-size-fits-all approach described earlier.

Keywords: ETF, market timing, volatility, optimization, parameterization

JEL Classification: B41, C14, C51, C52, C53, C61, C81, E27

Suggested Citation

Glenn, Lewis A., Adaptive Market Timing with ETFs (December 28, 2010). Available at SSRN: https://ssrn.com/abstract=1732010 or http://dx.doi.org/10.2139/ssrn.1732010

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