Does intraday technical analysis in the U.S. equity market have value?

Marshall, Ben R.; Cahan, Rochester H.; Cahan, Jared M.

doi:10.1016/j.jempfin.2006.05.003

Cited by 78 publications

(43 citation statements)

References 35 publications

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“…The results confirm the positive performance of the flag pattern over the intraday data of the DJIA for a time horizon of more than 13 years, opposite to Marshall et al (2008) who did not find any positive empirical evidence for the US equity market during a period of only 2 years.…”

Section: Introductionsupporting

confidence: 69%

Stock market trading rule based on pattern recognition and technical analysis: Forecasting the DJIA index with intraday data

Royo

Guijarro

Michniuk

2015

Expert Systems with Applications

157

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ElsevierCervelló Royo, RE.; Guijarro Martínez, F.; Michniuk, K. (2015). AbstractThis study introduces a new approximation of the flag price pattern recognition. We develop a trading rule which provides positive risk-adjusted returns for intraday data of the Dow Jones Industrial Average Index. In order to mitigate the data snooping problem we use a data set of more than 90,000 observations, results are reported over 96 different configurations of the trading rule parameters. Furthermore, results are examined over 3 non-overlapping sub-periods. The trading rule provides positive results for all the configurations.

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Section: Introductionsupporting

confidence: 69%

Stock market trading rule based on pattern recognition and technical analysis: Forecasting the DJIA index with intraday data

Royo

Guijarro

Michniuk

2015

Expert Systems with Applications

157

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“…An entropy equation is written for the regions [x 1 , x] and [x, x 2 ], with the first region denoted p and the second region denoted q. An entropy [14,16] with each value of x is expressed by following equations (1) through (3):…”

Section: The Minimize Entropy Principle Approach (Mepa)mentioning

confidence: 99%

“…Therefore, since the first stock market opened, many analytical methods and forecasting models have been advanced in an attempt to land the big fish in the stock market sea. Two major stock market analysis approaches, fundamental and technical analysis [1][2][3][4], are commonly used by both stock analysts and artificial intelligence (AI) methods proposed by the researchers who are interested in stock markets [5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Forecasting the Stock Market with Linguistic Rules Generated from the Minimize Entropy Principle and the Cumulative Probability Distribution Approaches

Chen

Cheng

et al. 2010

Entropy

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Abstract:To forecast a complex and non-linear system, such as a stock market, advanced artificial intelligence algorithms, like neural networks (NNs) and genetic algorithms (GAs) have been proposed as new approaches. However, for the average stock investor, two major disadvantages are argued against these advanced algorithms: (1) the rules generated by NNs and GAs are difficult to apply in investment decisions; and (2) the time complexity of the algorithms to produce forecasting outcomes is very high. Therefore, to provide understandable rules for investors and to reduce the time complexity of forecasting algorithms, this paper proposes a novel model for the forecasting process, which combines two granulating methods (the minimize entropy principle approach and the cumulative probability distribution approach) and a rough set algorithm. The model verification demonstrates that the proposed model surpasses the three listed conventional fuzzy time-series models and a multiple regression model (MLR) in forecast accuracy.

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“…This definition is close to Yamamoto's definition. Marshall et al (2008) follow the above direction in defining technical analysis describing technical analysis as "the use of past price movements to predict future price movements" (Marshall et al, 2008). A similar definition is offered by Leight and Purvis (2008) stating that "technical analysis uses the past dynamics of price and volume to predict future stock price" .…”

Section: Definition Of Technical Analysismentioning

confidence: 99%

“…As shown by Marshall et al (2008) the profitability of technical trading rules decreases when data snooping problems and transaction costs are taken into consideration. It is important that these aspects are incorporated into studies since they reflect vital aspects of analyses.…”

Section: Model-based Bootstrap Studiesmentioning

confidence: 99%

Pattern Recognition Applied to Chart Analysis. Evidence From Intraday International Stock Markets

Michniuk¹

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Does intraday technical analysis in the U.S. equity market have value?

Cited by 78 publications

References 35 publications

Stock market trading rule based on pattern recognition and technical analysis: Forecasting the DJIA index with intraday data

Stock market trading rule based on pattern recognition and technical analysis: Forecasting the DJIA index with intraday data

Forecasting the Stock Market with Linguistic Rules Generated from the Minimize Entropy Principle and the Cumulative Probability Distribution Approaches

Pattern Recognition Applied to Chart Analysis. Evidence From Intraday International Stock Markets

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