A Machine Learning View on Momentum and Reversal Trading
Momentum 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 unco...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2018-10-01
|
| Series: | Algorithms |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-4893/11/11/170 |
| _version_ | 1818134076283944960 |
|---|---|
| author | Zhixi Li Vincent Tam |
| author_facet | Zhixi Li Vincent Tam |
| author_sort | Zhixi Li |
| collection | DOAJ |
| description | Momentum 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. |
| first_indexed | 2024-12-11T09:02:52Z |
| format | Article |
| id | doaj.art-35e69d2f80d44aca852ee77d2f3a42e5 |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-12-11T09:02:52Z |
| publishDate | 2018-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-35e69d2f80d44aca852ee77d2f3a42e52022-12-22T01:13:43ZengMDPI AGAlgorithms1999-48932018-10-01111117010.3390/a11110170a11110170A Machine Learning View on Momentum and Reversal TradingZhixi Li0Vincent Tam1Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, ChinaDepartment of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, ChinaMomentum 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.https://www.mdpi.com/1999-4893/11/11/170stock marketmachine learningmomentum effectmomentum tradingreversal effectreversal trading |
| spellingShingle | Zhixi Li Vincent Tam A Machine Learning View on Momentum and Reversal Trading Algorithms stock market machine learning momentum effect momentum trading reversal effect reversal trading |
| title | A Machine Learning View on Momentum and Reversal Trading |
| title_full | A Machine Learning View on Momentum and Reversal Trading |
| title_fullStr | A Machine Learning View on Momentum and Reversal Trading |
| title_full_unstemmed | A Machine Learning View on Momentum and Reversal Trading |
| title_short | A Machine Learning View on Momentum and Reversal Trading |
| title_sort | machine learning view on momentum and reversal trading |
| topic | stock market machine learning momentum effect momentum trading reversal effect reversal trading |
| url | https://www.mdpi.com/1999-4893/11/11/170 |
| work_keys_str_mv | AT zhixili amachinelearningviewonmomentumandreversaltrading AT vincenttam amachinelearningviewonmomentumandreversaltrading AT zhixili machinelearningviewonmomentumandreversaltrading AT vincenttam machinelearningviewonmomentumandreversaltrading |