The rise and fall of technical trading rule success (original) (raw)
Related papers
The Quarterly Review of Economics and Finance, 2002
This study extends the work of Brock et al.'s (1992) empirical analysis on technical trading rules (price and momentum) by including trading volume moving averages; broader indices (New York Stock Exchange (NYSE) and National Association of Security Dealers Automatic Quotations (NASDAQ)) covering both large-cap and small-cap firms using market weightings; and focusing on a time period that includes great innovations in trading and disseminating data to the market. Similar to their study, we base our conclusions on nonparametric analysis. By extending the t-test analysis through a residual bootstrap methodology utilizing a random walk, a generalized autoregressive conditional heteroskedasticity in mean (GARCH-M), and a GARCH-M with instrument variables, criticisms of earlier technical analysis are mitigated. Overall, the results support Brock et al.'s (1992) price-weighted index (Dow Jones Industrial Average (DJIA)) analysis by showing that the technical trading rules add value by capturing profit opportunities when compared to a buy-and-hold strategy. When the analysis of the trading rules are applied to different time periods, the results reveal a weakening in profit potential over time. This may imply that the market is becoming more efficient in disseminating information to a wider range of investors.
A momentum trading approach to technical analysis of Dow Jones industrials
Physica A: Statistical Mechanics and its Applications, 2004
A momentum trading approach is presented to examine the Dow Jones industrial components for a period of about past 10 years (1992-2002). An analogy between the classical dynamics in physics and the stock trade dynamics is used with the momentum, P = mv, where the velocity (v) is a relative price change in a period () and the inertial mass (m) is a normalized trade volume. Extrema in the momentum time series, i.e., the singularities in the driving force provide the signals for executing trades, minima with negative momentum to buy and maxima with positive momentum to sell. Trades are implemented using a momentum threshold (Pc). A range of periodic cycles (=5-240 days) in time series and trading momentum thresholds (|Pc|=0:01-0.5) are considered and returns (maximum, minimum, accumulative, and average) are examined in detail on the historical DJI data for about a decade (1992-2002). Frequency of trade is generally higher with smaller periods with the high probability of higher returns at |Pc| = 0:02-0.1 for nearly all stocks in DJI.
The Profitability of Technical Stock Trading has Moved from Daily to Intraday Data
SSRN Electronic Journal, 2000
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.
Asia-pacific Financial Markets, 2002
Numerous studies in the finance literature have investigated technical analysis to determine its validity as an investment tool. This study is an attempt to explore whether some forms of technical analysis can predict stock price movement and make excess profits based on certain trading rules in markets with different efficiency level. To avoid using arbitrarily selected 26 trading rules as did by Brock, Lakonishok and LeBaron (1992) and later by Bessembinder and Chan (1998), this paper examines predictive power and profitability of simple trading rules by expanding their universe of 26 rules to 412 rules. In order to find out the relationship between market efficiency and excess return by applying trading rules, we examine excess return over periods in U.S. markets and also compare the excess returns between U.S. market and Chinese markets. Our results found that there is no evidence at all supporting technical forecast power by these trading rules in U.S. equity index after 1975. During the 1990s break-even costs turned to be negative, –0.06%, even failing to beat a buy-holding strategyin U.S. equity market. In comparison, our results provide support for the technical strategies even in the presence of trading cost in Chinese stock markets.
Technical Trading Rules and Market Efficiency: Evidence from the Australian Stock Exchange 1980-2002
2004
This paper examines the validity of two classes of simple technical trading rulesmoving averages and trading range breaks-in the Australian Stock Exchange. We conduct our empirical analysis in two stages. In the first stage, standard t-tests are used to compare returns generate by the technical trading rules against those generated by the buy-and-hold equivalent. In the second stage, we employ bootstrap methods to generate empirical distributions of trading returns simulated under four models of stock pricesthe random walk with drift, AR(1), GARCH(1,1) and EGARCH(1,1) models. Using daily data from 1 January 1980 to 31 December 2002, we find that technical rules possess some predictive power over the full sample period. However, tests on four nonoverlapping sub-samples reveal that the technical rules generate returns in excess of the buy-and-hold equivalent only in the pre-1991 sample period, but then begin to generate negative returns in the sample period post 1991. Results from the second stage tests also indicate that it is possible to reverse the standard test outcome of predictability by addressing non-normality, time-dependence and conditional heteroskedasticity in the data. Overall, our results finds the ASX informationally efficient and that the period post-1991 marks a significant rise in the stock market's efficiency.
Profitability of technical stock trading: Has it moved from daily to intraday data?
Review of Financial Economics, 2009
This paper investigates how technical trading systems exploit the momentum and reversal effects in the S&P 500 spot and futures market. When based on daily data, the profitability of 2,580 technical models has steadily declined since 1960, and has been unprofitable since the early 1990s. However, when based on 30-minutes data the same models produce an average gross return of 7.2 percent per year between 1983 and 2007. These results do not change substantially when trading is tested over eight subperiods. In particular, there is no clear trend of a declining profitability of technical stock trading based on 30-minutes data. Those 25 models which performed best over the most recent subperiod produce a significantly higher gross return over the subsequent subperiod than all models. Between 2001 and 2007 the 2,580 models perform worse than over the 1980s and 1990s. 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.
Optimization of technical trading strategies and the profitability in security markets
Economics Letters, 1998
The ultimate goal of any testing strategy is to measure profitability. This paper measures the profitability of simple technical trading rules based on nonparametric models which maximize the total returns of an investment strategy. The profitability of an investment strategy is evaluated against a simple buy-and-hold strategy on the security and its distance from the ideal net profit. The predictive performance is evaluated by the market timing tests of Henriksson-Merton and Pesaran-Timmermann to measure whether forecasts have economic value in practice. The results of an illustrative example indicate that nonparametric models with technical strategies provide significant profits when tested against buy-and-hold strategies. In addition, the sign predictions of these models are statistically significant.
Performance of technical trading rules: evidence from Southeast Asian stock markets
SpringerPlus
Background Technical analysis involves making investment decisions based on past trading data. It aims to establish buying and selling rules that maximize profits and still control risks of loss. Unfortunately, according to the efficient market hypothesis (EMH), this endeavor is ultimately futile. The EMH states that all available and relevant information are already incorporated in security prices. As technical analysis uses only current and past trading data, it is not possible to obtain abnormal positive returns by applying these technical trading rules. If investors could make money from applying these trading rules, this would indicate that the market is inefficient. Therefore, the question of whether