Improving stock trading decisions based on pattern recognition using machine learning technology.

PRML, a novel candlestick pattern recognition model using machine learning methods, is proposed to improve stock trading decisions. Four popular machine learning methods and 11 different features types are applied to all possible combinations of daily patterns to start the pattern recognition schedu...

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Autores principales:	Yaohu Lin, Shancun Liu, Haijun Yang, Harris Wu, Bingbing Jiang
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a32e0deb05ed4cf2921442240455341a
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spelling	oai:doaj.org-article:a32e0deb05ed4cf2921442240455341a2021-12-02T20:18:33ZImproving stock trading decisions based on pattern recognition using machine learning technology.1932-620310.1371/journal.pone.0255558https://doaj.org/article/a32e0deb05ed4cf2921442240455341a2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0255558https://doaj.org/toc/1932-6203PRML, a novel candlestick pattern recognition model using machine learning methods, is proposed to improve stock trading decisions. Four popular machine learning methods and 11 different features types are applied to all possible combinations of daily patterns to start the pattern recognition schedule. Different time windows from one to ten days are used to detect the prediction effect at different periods. An investment strategy is constructed according to the identified candlestick patterns and suitable time window. We deploy PRML for the forecast of all Chinese market stocks from Jan 1, 2000 until Oct 30, 2020. Among them, the data from Jan 1, 2000 to Dec 31, 2014 is used as the training data set, and the data set from Jan 1, 2015 to Oct 30, 2020 is used to verify the forecasting effect. Empirical results show that the two-day candlestick patterns after filtering have the best prediction effect when forecasting one day ahead; these patterns obtain an average annual return, an annual Sharpe ratio, and an information ratio as high as 36.73%, 0.81, and 2.37, respectively. After screening, three-day candlestick patterns also present a beneficial effect when forecasting one day ahead in that these patterns show stable characteristics. Two other popular machine learning methods, multilayer perceptron network and long short-term memory neural networks, are applied to the pattern recognition framework to evaluate the dependency of the prediction model. A transaction cost of 0.2% is considered on the two-day patterns predicting one day ahead, thus confirming the profitability. Empirical results show that applying different machine learning methods to two-day and three-day patterns for one-day-ahead forecasts can be profitable.Yaohu LinShancun LiuHaijun YangHarris WuBingbing JiangPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 8, p e0255558 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yaohu Lin Shancun Liu Haijun Yang Harris Wu Bingbing Jiang Improving stock trading decisions based on pattern recognition using machine learning technology.
description	PRML, a novel candlestick pattern recognition model using machine learning methods, is proposed to improve stock trading decisions. Four popular machine learning methods and 11 different features types are applied to all possible combinations of daily patterns to start the pattern recognition schedule. Different time windows from one to ten days are used to detect the prediction effect at different periods. An investment strategy is constructed according to the identified candlestick patterns and suitable time window. We deploy PRML for the forecast of all Chinese market stocks from Jan 1, 2000 until Oct 30, 2020. Among them, the data from Jan 1, 2000 to Dec 31, 2014 is used as the training data set, and the data set from Jan 1, 2015 to Oct 30, 2020 is used to verify the forecasting effect. Empirical results show that the two-day candlestick patterns after filtering have the best prediction effect when forecasting one day ahead; these patterns obtain an average annual return, an annual Sharpe ratio, and an information ratio as high as 36.73%, 0.81, and 2.37, respectively. After screening, three-day candlestick patterns also present a beneficial effect when forecasting one day ahead in that these patterns show stable characteristics. Two other popular machine learning methods, multilayer perceptron network and long short-term memory neural networks, are applied to the pattern recognition framework to evaluate the dependency of the prediction model. A transaction cost of 0.2% is considered on the two-day patterns predicting one day ahead, thus confirming the profitability. Empirical results show that applying different machine learning methods to two-day and three-day patterns for one-day-ahead forecasts can be profitable.
format	article
author	Yaohu Lin Shancun Liu Haijun Yang Harris Wu Bingbing Jiang
author_facet	Yaohu Lin Shancun Liu Haijun Yang Harris Wu Bingbing Jiang
author_sort	Yaohu Lin
title	Improving stock trading decisions based on pattern recognition using machine learning technology.
title_short	Improving stock trading decisions based on pattern recognition using machine learning technology.
title_full	Improving stock trading decisions based on pattern recognition using machine learning technology.
title_fullStr	Improving stock trading decisions based on pattern recognition using machine learning technology.
title_full_unstemmed	Improving stock trading decisions based on pattern recognition using machine learning technology.
title_sort	improving stock trading decisions based on pattern recognition using machine learning technology.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/a32e0deb05ed4cf2921442240455341a
work_keys_str_mv	AT yaohulin improvingstocktradingdecisionsbasedonpatternrecognitionusingmachinelearningtechnology AT shancunliu improvingstocktradingdecisionsbasedonpatternrecognitionusingmachinelearningtechnology AT haijunyang improvingstocktradingdecisionsbasedonpatternrecognitionusingmachinelearningtechnology AT harriswu improvingstocktradingdecisionsbasedonpatternrecognitionusingmachinelearningtechnology AT bingbingjiang improvingstocktradingdecisionsbasedonpatternrecognitionusingmachinelearningtechnology
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Improving stock trading decisions based on pattern recognition using machine learning technology.

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