A Machine Learning View on Momentum and Reversal Trading

Abstract

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.

The Stock Exchange Prediction using Machine Learning Techniques: A Comprehensive and Systematic Literature Review

“…There is no strong consensus as to which method is best when each is looked at individually. The SVM using a technical dataset type indicator in [40] and [41] has reported that it works very well by producing an accuracy rate of 71.5% and a return of 13.9 % per week compared to RF with an accuracy rate of 66.2% and a return of 10.8% per week and LR with an accuracy rate of 65.8% and a return of 11.1%. RF is the best performing method in [3] combining types of technical data sets, indicators and company fundamentals.…”

“…While many studies on predicting stock price movements individually report on measuring the performance of the proposed modeling methods, there is no strong consensus as to which method is best when each is looked at individually. Lee et al [40] and Li et al [41] concluded performance the SVM performs very well compared to other machine learning algorithms such as DT and Neural Network-based (MLP & LSTMNN) by producing a higher level of accuracy and return. high.…”

Stock Ranking Prediction Using List-Wise Approach and Node Embedding Technique

“…DL based models are used for predicting stock ranking [11] or one-day ahead close price [12]. They are also capable of producing superior performance while predicting momentum and reversal effects in the stock market [6] or forecasting the actual value of a stock index [13]. They tend to outperform ANN or SVM in the case of one step ahead stock movement prediction [25].…”

“…The stock price can be influenced by numerous factors such as macro-economic factors [4], the past value of technical and fundamental indicators [5], [6], or news and online search data [7], [8], [9]. It is possible to predict the movement of a single stock or the market as a whole [5].…”

Forecasting Stock Prices Using a Hybrid Deep Learning Model Integrating Attention Mechanism, Multi-Layer Perceptron, and Bidirectional Long-Short Term Memory Neural Network

“…Pai and Lin [15] proposed a hybrid methodology that combined ARIMA with SVM model for forecasting stock prices and obtained improved results compared with any single models. Li and Tam [16] used various machine learning techniques including DT to investigate the momentum and reversal effects occurring in the stock market. Chen et al [17] compared various machine learning models with a sample dimension engineering method for Bitcoin price prediction, and proved the superiority of machine learning models.…”