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Article: A Machine Learning View on Momentum and Reversal Trading

TitleA Machine Learning View on Momentum and Reversal Trading
Authors
Keywordsstock market
machine learning
momentum effect
momentum trading
reversal effect
Issue Date2018
PublisherMDPIAG. The Journal's web site is located at http://www.mdpi.com/journal/algorithms
Citation
Algorithms, 2018, v. 11 n. 11, p. article no. 170 How to Cite?
AbstractMomentum and reversal effects are important phenomena in stock markets. In academia, relevant studies have been conducted for years. Researchers have attempted to analyze these phenomena using statistical methods and to give some plausible explanations. However, those explanations are sometimes unconvincing. Furthermore, it is very difficult to transfer the findings of these studies to real-world investment trading strategies due to the lack of predictive ability. This paper represents the first attempt to adopt machine learning techniques for investigating the momentum and reversal effects occurring in any stock market. In the study, various machine learning techniques, including the Decision Tree (DT), Support Vector Machine (SVM), Multilayer Perceptron Neural Network (MLP), and Long Short-Term Memory Neural Network (LSTM) were explored and compared carefully. Several models built on these machine learning approaches were used to predict the momentum or reversal effect on the stock market of mainland China, thus allowing investors to build corresponding trading strategies. The experimental results demonstrated that these machine learning approaches, especially the SVM, are beneficial for capturing the relevant momentum and reversal effects, and possibly building profitable trading strategies. Moreover, we propose the corresponding trading strategies in terms of market states to acquire the best investment returns.
DescriptionA Special Issue on 'Algorithms in Computational Finance'
Persistent Identifierhttp://hdl.handle.net/10722/274994
ISSN
2015 SCImago Journal Rankings: 0.357

 

DC FieldValueLanguage
dc.contributor.authorLI, Z-
dc.contributor.authorTam, V-
dc.date.accessioned2019-09-10T02:33:11Z-
dc.date.available2019-09-10T02:33:11Z-
dc.date.issued2018-
dc.identifier.citationAlgorithms, 2018, v. 11 n. 11, p. article no. 170-
dc.identifier.issn1999-4893-
dc.identifier.urihttp://hdl.handle.net/10722/274994-
dc.descriptionA Special Issue on 'Algorithms in Computational Finance'-
dc.description.abstractMomentum and reversal effects are important phenomena in stock markets. In academia, relevant studies have been conducted for years. Researchers have attempted to analyze these phenomena using statistical methods and to give some plausible explanations. However, those explanations are sometimes unconvincing. Furthermore, it is very difficult to transfer the findings of these studies to real-world investment trading strategies due to the lack of predictive ability. This paper represents the first attempt to adopt machine learning techniques for investigating the momentum and reversal effects occurring in any stock market. In the study, various machine learning techniques, including the Decision Tree (DT), Support Vector Machine (SVM), Multilayer Perceptron Neural Network (MLP), and Long Short-Term Memory Neural Network (LSTM) were explored and compared carefully. Several models built on these machine learning approaches were used to predict the momentum or reversal effect on the stock market of mainland China, thus allowing investors to build corresponding trading strategies. The experimental results demonstrated that these machine learning approaches, especially the SVM, are beneficial for capturing the relevant momentum and reversal effects, and possibly building profitable trading strategies. Moreover, we propose the corresponding trading strategies in terms of market states to acquire the best investment returns.-
dc.languageeng-
dc.publisherMDPIAG. The Journal's web site is located at http://www.mdpi.com/journal/algorithms-
dc.relation.ispartofAlgorithms-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectstock market-
dc.subjectmachine learning-
dc.subjectmomentum effect-
dc.subjectmomentum trading-
dc.subjectreversal effect-
dc.titleA Machine Learning View on Momentum and Reversal Trading-
dc.typeArticle-
dc.identifier.emailTam, V: vtam@hkucc.hku.hk-
dc.identifier.authorityTam, V=rp00173-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.3390/a11110170-
dc.identifier.scopuseid_2-s2.0-85056891464-
dc.identifier.hkuros302434-
dc.identifier.volume11-
dc.identifier.issue11-
dc.identifier.spagearticle no. 170-
dc.identifier.epagearticle no. 170-
dc.publisher.placeSwitzerland-

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