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February 13th, 2010, 12:18 PM
This paper investigates how technical trading systems exploit the momentum and reversal effects in the S&P 500 spot and futures market. The former is exploited by trend-following models, while the latter by contrarian models. In total, the performance of 2580 widely used models is analyzed. When based on daily data, the profitability of technical stock trading has steadily declined since 1960 and has become unprofitable over the 1990s. However, when based on 30-minutes-data the same models produce an average gross return of 8.8% per year between 1983 and 2000. These results do not change substantially when trading is simulated over six subperiods. Those 25 models which performed best over the most recent subperiod produce a significantly higher gross return over the subsequent subperiod than all models. Over the out-of-sample-period 2001-2006 the 2580 models perform much worse than between 1983 and 2000. This result could be due to stock markets becoming more efficient or to stock price trends shifting from 30-minutes-prices to prices of higher frequencies.
In the recent debates over the informational (in)efficiency of the stock market, particular attention has been paid to two "anomalies,” the momentum and reversal effects. The first effect refers to the phenomenon of stock price trends that can be profitably exploited by following "momentum strategies” (Fama-French, 1989; Jegadeesh-Titman, 1993; Chan-Jegadeesh-Lakonishok, 1996; Goetzmann-Massa, 2000); the second refers to reversals in stock price trends that can be profitably exploited following "contrarian strategies” (DeBondt-Thaler, 1985 and 1987; Fama-French, 1989; Jegadeesh, 1990; Lo-MacKinlay, 1990; Lehman, 1990).
All these studies investigate the profitability of hypothetical trading strategies that are not actually used by market participants. However, market participants use a great variety of trading techniques to exploit asset price trends and their reversals, i. e., the trend-following and contrarian models of technical analysis.
Technical analysis is omnipresent in financial markets. In the foreign exchange market, e. g., technical analysis is the most widely used trading technique (for recent survey studies see Taylor-Allen, 1992; Cheung-Wong, 2000; Cheung-Chinn, 2001; Oberlechner, 2001; Cheung-Chinn-Marsh, 2004; Gehrig-Menkhoff; 2004, 2005 and 2006; Menkhoff-Taylor, 2007). It seems highly plausible that technical analysis plays a similar role in stock markets, particularly in short-term trading in stock futures (Irwin-Holt, 2004, provide evidence about the popularity of technical analysis in futures markets).
The omnipresence of technical analysis in financial markets presents a dilemma for conventional asset market theory. If technical trading is not profitable, then the assumption of market participants’ rationality is in doubt, whereas, if technical analysis is actually profitable, then the assumption of (weak-form) market efficiency is in doubt.
Many empirical studies of the performance of technical trading systems in the stock and foreign exchange markets report that these trading techniques would have been abnormally profitable.1) The results of these studies have not, on the whole, been taken seriously by the economists’ profession. There might be several reasons for that. First, if one accepted the excessive profitability of technical analysis as a feature of asset markets then fundamental concepts like market efficiency or rational expectations would have to be seriously reconsidered. Second, recent studies – all based on daily data - find that the profitability of technical analysis has strongly declined or even ceased to exist in the stock market (Sullivan-Timmermann-White, 1999), in the foreign exchange market (Ohlson, 2004; Schulmeister, 2007A and 2007B) as well as in many futures markets (Park-Irwin, 2005). This could be viewed as confirmation that their excessive returns were only a temporary phenomenon. Finally, most of the extant studies report the profitability of only a relatively small number of trading rules and this gave rise to the suspicion of "data mining"; researchers might have been biased in favor of finding ex post profitable trading rules which a trader in practice would not know about ex ante.
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 (1960/2000) and in the stock index futures market (1983/2000). These models comprise trend-following as well as contrarian trading systems. My analysis is based on daily and 30-minute data.2) 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 technical 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 and 2000. The profitability of these models has steadily declined from 8.6% per year (1960/71) to 2.0% (1972/82), -0.0% (1983/91) to –5.1% (1992/2000).
• The picture is very different for stock futures trading based on 30-minutes-data. The 2580 models produce an average gross return of 8.8% per year between 1983 and 2000. The contrarian models perform much better (10.9%) than the trend-following models (6.4%). 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 (ex post) produce a significantly higher gross return over the subsequent subperiod (ex ante) than all models in sample (18.7% and 9.6%, respectively).
When testing the same 2580 trading systems over the out-of-sample-period 2001-2006 (based on 30-minutes-data) it turns out that the models would have performed much worse than between 1983 and 2000. This result could be due to stock markets becoming more efficient or to stock price trends shifting from 30-minutes-prices to prices of higher frequencies.
In the recent debates over the informational (in)efficiency of the stock market, particular attention has been paid to two "anomalies,” the momentum and reversal effects. The first effect refers to the phenomenon of stock price trends that can be profitably exploited by following "momentum strategies” (Fama-French, 1989; Jegadeesh-Titman, 1993; Chan-Jegadeesh-Lakonishok, 1996; Goetzmann-Massa, 2000); the second refers to reversals in stock price trends that can be profitably exploited following "contrarian strategies” (DeBondt-Thaler, 1985 and 1987; Fama-French, 1989; Jegadeesh, 1990; Lo-MacKinlay, 1990; Lehman, 1990).
All these studies investigate the profitability of hypothetical trading strategies that are not actually used by market participants. However, market participants use a great variety of trading techniques to exploit asset price trends and their reversals, i. e., the trend-following and contrarian models of technical analysis.
Technical analysis is omnipresent in financial markets. In the foreign exchange market, e. g., technical analysis is the most widely used trading technique (for recent survey studies see Taylor-Allen, 1992; Cheung-Wong, 2000; Cheung-Chinn, 2001; Oberlechner, 2001; Cheung-Chinn-Marsh, 2004; Gehrig-Menkhoff; 2004, 2005 and 2006; Menkhoff-Taylor, 2007). It seems highly plausible that technical analysis plays a similar role in stock markets, particularly in short-term trading in stock futures (Irwin-Holt, 2004, provide evidence about the popularity of technical analysis in futures markets).
The omnipresence of technical analysis in financial markets presents a dilemma for conventional asset market theory. If technical trading is not profitable, then the assumption of market participants’ rationality is in doubt, whereas, if technical analysis is actually profitable, then the assumption of (weak-form) market efficiency is in doubt.
Many empirical studies of the performance of technical trading systems in the stock and foreign exchange markets report that these trading techniques would have been abnormally profitable.1) The results of these studies have not, on the whole, been taken seriously by the economists’ profession. There might be several reasons for that. First, if one accepted the excessive profitability of technical analysis as a feature of asset markets then fundamental concepts like market efficiency or rational expectations would have to be seriously reconsidered. Second, recent studies – all based on daily data - find that the profitability of technical analysis has strongly declined or even ceased to exist in the stock market (Sullivan-Timmermann-White, 1999), in the foreign exchange market (Ohlson, 2004; Schulmeister, 2007A and 2007B) as well as in many futures markets (Park-Irwin, 2005). This could be viewed as confirmation that their excessive returns were only a temporary phenomenon. Finally, most of the extant studies report the profitability of only a relatively small number of trading rules and this gave rise to the suspicion of "data mining"; researchers might have been biased in favor of finding ex post profitable trading rules which a trader in practice would not know about ex ante.
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 (1960/2000) and in the stock index futures market (1983/2000). These models comprise trend-following as well as contrarian trading systems. My analysis is based on daily and 30-minute data.2) 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 technical 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 and 2000. The profitability of these models has steadily declined from 8.6% per year (1960/71) to 2.0% (1972/82), -0.0% (1983/91) to –5.1% (1992/2000).
• The picture is very different for stock futures trading based on 30-minutes-data. The 2580 models produce an average gross return of 8.8% per year between 1983 and 2000. The contrarian models perform much better (10.9%) than the trend-following models (6.4%). 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 (ex post) produce a significantly higher gross return over the subsequent subperiod (ex ante) than all models in sample (18.7% and 9.6%, respectively).
When testing the same 2580 trading systems over the out-of-sample-period 2001-2006 (based on 30-minutes-data) it turns out that the models would have performed much worse than between 1983 and 2000. This result could be due to stock markets becoming more efficient or to stock price trends shifting from 30-minutes-prices to prices of higher frequencies.