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Volatility-based filtering is proposed to pre-process historical daily return data of stock indexes before applying to price-based technical analysis trading rules. Any “nearly flat” days which have daily gains or losses less than a threshold about 20% of a daily volatility measure, is filtered out as noise. On six widely followed stock indexes over 1990-2012, the daily return sample sets after filtering retain largely the levels of return mean and skew, and have somewhat higher standard deviation and lower Kurtosis. The filtered sets indeed resemble white noise within the bounds of the daily return filter threshold. By excluding filtered “nearly flat” days from both the price input and the day counting, three types of well-known technical trading rules are tested, using daily returns of the SP dual moving average cross trend following; and long term channel breakout long/short trading. Filtering improves performance in all tested cases for the whole period, and two sub-periods of lower (1990-1999) and higher (2000-2012) market volatilities. Informational relevance by daily return magnitude, and better alignment with institutional investors or other price-targeted traders are argued as possible reasons for the observed efficacy of daily return filtering for technical trading rule adjustments. |
