A computational implementation of stock charting: abrupt volume increase as signal for movement in New York Stock Exchange Composite Index

Leigh, William; Modani, Naval K.; Hightower, Ross

doi:10.1016/s0167-9236(03)00084-8

Cited by 58 publications

(45 citation statements)

References 19 publications

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“…The non normality of the trading rule returns impedes the application of the statistical (Leigh et al, 2004) and the estimation of the mean return intervals. In the present study, the non normality of the returns is even higher than in other works, due to the levels of stop loss and take profit we have chosen.…”

Section: Resultsmentioning

confidence: 99%

“…∈ 10, 20, 40, 80, in Leigh, Paz et al (2002), ∈ 20, 40, 60, 80, 100 in Leigh et al (2004) and in Chan (2007), and∈ 20, 40, 60, 80, 100, 120, 160, 200, 240 in Wang andChan (2009).…”

Section: Trading Rule Specificationmentioning

confidence: 99%

“…Leigh, Paz et al (2002), , Leigh et al (2004) and Chan (2007, 2009) have reported positive performance of trading rules based on the flag pattern by employing different stock market indexes and for a relatively wide period of time. The profitability of this trading rule has been greater than the index selected as a benchmark, even after including the transaction costs.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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: Resultsmentioning

confidence: 99%

“…∈ 10, 20, 40, 80, in Leigh, Paz et al (2002), ∈ 20, 40, 60, 80, 100 in Leigh et al (2004) and in Chan (2007), and∈ 20, 40, 60, 80, 100, 120, 160, 200, 240 in Wang andChan (2009).…”

Section: Trading Rule Specificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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show abstract

“…8, SOM clustering enables the resemblance among prototype vectors within the same neighborhood. For example, prototype vectors, with the position of (2, 7), (2,8), (3,7) and (3,8) in Fig. 8, exhibit almost the same shape, which indicates that some clusters can be merged.…”

Section: Clustering Analysis Based On Sommentioning

confidence: 93%

“…[1] indicate the efficiency of one specific technical pattern, namely the head and shoulders, for forecasting the foreign exchange rate. [2] and [3] demonstrate that the bull flag technical pattern, exhibited in both the stock price and the trading volume time series, is able to generate trading rules that imply higher profits compared to stochastic trading strategies. Besides, it is claimed in [4] that some technical patterns can provide additional information to forecast stock prices.…”

Section: Introductionmentioning

confidence: 99%

Automatically Recognizing Stock Patterns Using RPCL Neural Networks

Guo¹

2007

Proceedings on Intelligent Systems and Knowledge Engineering (ISKE2007)

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Stock patterns are those that occur frequently in stock time series, containing valuable forecasting information. In this paper, an approach to extract patterns and features from stock price time series is introduced. Thereafter, we employ two ANN-based methods to conduct clustering analyses upon the extracted samples, which are the self-organizing map (SOM) and the competitive learning. Besides, and we introduce an improved version of the rival penalized competitive learning (RPCL), and furthermore conduct a comparative study between the clustering performances of the improved RPCL and the SOM. Experimental results show that a better clustering performance can be achieved by the former.

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Trading rule discovery using technical analysis and a template matching technique for pattern recognition: Evidence from the Chinese stock market

Lobão,

Pacheco,

Fernandes

2023

International Studies of Economics

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This paper examines the potential profit of bull flag trading rules in the Shangai Stock Exchange Composite Index (SSE) using a template matching technique based on price pattern recognition. This paper fills a gap in the literature by applying a template matching technique for the recognition of bull flag patterns in the Shangai Stock Exchange Composite Index (SSE) during the period of 1991‐2021. To the best of our knowledge, no previous study has applied bull flag trading rules to the Chinese stock market. Our results indicate that bull flag trading rules can correctly predict the price movement direction of the index most of the time, achieving significantly positive excess profits. Moreover, shorter fitting windows and better quality of price fit values for lower holding periods are associated with better performance. This research may have relevant practical implications for investors who opt for this indicator in their asset allocation decisions.This article is protected by copyright. All rights reserved.

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A computational implementation of stock charting: abrupt volume increase as signal for movement in New York Stock Exchange Composite Index

Cited by 58 publications

References 19 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

Automatically Recognizing Stock Patterns Using RPCL Neural Networks

Trading rule discovery using technical analysis and a template matching technique for pattern recognition: Evidence from the Chinese stock market

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