Profitability of technical stock trading: Has it moved from daily to intraday data, страница 2

The purpose of the present paper is to provide new insights into the performance of technical trading in the stock market. In particular, I re-examine the finding that the profitability of technical analysis has declined over the 1990s by analyzing the ex-post-profitability of 2580 moving average models, momentum models and relative strength models in the S&P 500 spot market (1960/2007) and in the stock index futures market (1983/2007). These models comprise trendfollowing as well as contrarian trading systems. My analysis is based on daily and 30-minute data. I find that the profitability of technical analysis prior to the 1990s was in fact not transitory. Rather, the type of technical models that is profitable has merely shifted from ones that are based on daily data to those that are based on higher frequency data. In particular, I find:

•  The 2580 models tested would have produced an average gross rate of return of only 1.9% per year when trading in the S&P 500 spot market based on daily prices between 1960 and2000. Theprofitability of these models has steadily declined from 8.6% per year (1960/71) to 2.0% (1972/82), −0.0% (1983/91), −5.1% (1992/2000) to−0.8% (2001/07).

•  The picture is very different for stock futures trading based on 30minutes-data. The 2580 models produce an average gross return of 7.2% per year between 1983 and 2000. The contrarian models perform much better (9.1%) than the trend-following models (4.8%).

Beyond examining ex-post profitability, I analyze the structure of the profitability of these models and relate the results to the implied pattern in stock price dynamics. I also simulate the process of model selection based on their performance in the past and test for the exante-profitability of the selected models. I find that:

•  The profitability of technical stock futures trading is exclusively due to the exploitation of persistent price trends around which stock prices fluctuate.

•  Those 25 models which performed best over the most recent subperiod (in sample=ex post) produce a significantly higher gross return over the subsequent subperiod (out of sample=ex ante) than all models in sample (14.5% and 7.5%, respectively).

Over the last subperiod 2004–2007 (based on 30-minutes-data) the 2580 models performed much worse than between 1983 and 2003. This result could be due to stock markets becoming more efficient recently or to stock price trends shifting from 30-minutes-prices to prices of higher frequencies.

2. How technical trading systems work

Technical analysis tries to profitably exploit the (purportedly) frequent occurrence of asset price trends (“the trend is your friend”). Hence, these trading techniques derive buy and sell signals from the most recent price movements which (purportedly) indicate the continuation of a trend or its reversal (trend-following or contrarian models).^[2] Since technical analysts believe that the pattern of asset price dynamics as a sequence of trends interrupted by “whipsaws” repeats itself across different time scales they apply technical models to price data of almost any frequency, ranging from daily data to tick data.

According to the timing of trading signals one can distinguish between trend-following strategies and contrarian models. Trendfollowing systems produce buy (sell) signals in the early stage of an upward (downward) trend, whereas contrarian strategies produce sell (buy) signals at the end of an upward (downward) trend, e. g., contrarian models try to identify “overbought” (“oversold”) situations.^[3]

According to the method of processing price data one can distinguish between qualitative and quantitative trading systems. The qualitative approaches rely on the interpretation of some (purportedly) typical configurations of the ups and downs of price movements like head and shoulders, top and bottom formations or resistance lines (most of these approaches are contrarian, e.g., they try to anticipate trend reversals). These chartist techniques turn out to be profitable in many cases though less than moving average and momentum models (Chang & Osler,1999; Osler, 2000; Lo, Mamaysky, & Wang, 2000).