![]() ![]() After examining data for S&P 500 and Russell 2000 from 1990 to 1999, they conclude that the pattern has negligible predictive power when used as a stand-alone trading strategy, but has power to predict risk-adjusted excess returns over market portfolio returns. (2000) to test if head-and-shoulders pattern has predictive power. (2007) use pattern recognition method presented by Lo et al. (1978), Sweeney (1986), and Levich and Thomas (1993) also report statistically significant profits using technical analysis. Rejection of null model could either imply inefficiency of those exchange markets or the existence of a different null model compatible with efficient market hypothesis (for example, time varying mean). They use a bootstrap method using random walk null model of efficient market hypothesis to conclude that returns from head and shoulders trading strategy are incompatible with the null hypothesis for German Mark and Yen exchange markets. They find the strategy yields economically significant profit in German Mark and Yen markets but not in other exchange rate markets studied in the work. Osler and Chang (1995) test the profitability of head and shoulders pattern in foreign-exchange markets. ![]() They observe further that volatility of returns following a buy signal is lower than volatility of returns following sell signal, thereby refuting the notion that higher returns for these strategies compensate higher inherent risks. They find statistically significant profits that cannot be explained using three null models of efficient market hypothesis – random walk, AR(1) and GARCH-M models. (1992) test the profitability of moving average rule (buying when shorter period moving average rises above longer period moving average, and selling when it falls below longer period moving average) and trading-range break out (buy when price rises above the observed local maximum and sell when it falls below the observed local minimum). Recent works dealing with profitability of technical analysis based trading strategies have given some credence to the assertion that technical analysis may not be a complete farce. Academic professionals and fundamental analysts typically scoff at technical analysis because of its paucity of quantitative justification. Technical analysis is the art of identifying geometric patterns in historical prices – often supplemented with volume-based signals – with the belief that occurrence of patterns are reliable predictors of price movement in the immediate future. These observations can be explained by the Adaptive Market Hypothesis with certain patterns becoming more accurate predictors in specific market environments. Bullish (bearish) patterns are more reliable predictors in bullish (bearish) market environments. The study reveals that no pattern produces statistically and economically significant profits for a cross-section of stocks and indices analyzed, though a few patterns are more successful predictors. A range of holding periods from 10 to 50 trading days is considered and a simple model of transaction costs is added. In order to test the possibility that technical patterns are more predictable in certain market environments, the period under study (1990 – 2015) is partitioned into bull and bear markets and the statistical significance of profits earned by identified patterns observed in each environment is analyzed. ![]() The thirty components of the Dow Jones Industrial Average and a set of ten indices are considered. This work presents a robust framework for pattern identification using probabilistic neural networks (PNN). An empirical evaluation of the effectiveness of technical analysis is confounded by the subjectivity involved in identifying patterns. Technical analysis is the art of identifying patterns in historical data with the belief that certain patterns foretell future price movements. ![]()
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