A performance comparison of machine learning models for stock market prediction with novel investment strategy

Azaz Hassan Khan · Rabia Shahid · Zaka Ullah Zahid · Malik Umar Sharif · Tariqullah Jan · Mohammad Haseeb Zafar

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Stock Market Forecasting Methods Financial Markets and Investment Strategies Forecasting Techniques and Applications

Abstract

Stock market forecasting is one of the most challenging problems in today's financial markets. According to the efficient market hypothesis, it is almost impossible to predict the stock market with 100% accuracy. However, Machine Learning (ML) methods can improve stock market predictions to some extent. In this paper, a novel strategy is proposed to improve the prediction efficiency of ML models for financial markets. Nine ML models are used to predict the direction of the stock market. First, these models are trained and validated using the traditional methodology on a historic data captured over a 1-day time frame. Then, the models are trained using the proposed methodology. Following the traditional methodology, Logistic Regression achieved the highest accuracy of 85.51% followed by XG Boost and Random Forest. With the proposed strategy, the Random Forest model achieved the highest accuracy of 91.27% followed by XG Boost, ADA Boost and ANN. In the later part of the paper, it is shown that only classification report is not sufficient to validate the performance of ML model for stock market prediction. A simulation model of the financial market is used in order to evaluate the risk, maximum draw down and returns associate with each ML model. The overall results demonstrated that the proposed strategy not only improves the stock market returns but also reduces the risks associated with each ML model.

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References

2017Deep learning with long short-term memory networks for financial market predictions 2003Financial time series forecasting using support vector machines 2003Support vector machine with adaptive parameters in financial time series forecasting 2011Using artificial neural network models in stock market index prediction 2020Stock Closing Price Prediction using Machine Learning Techniques 2020Predicting Stock Market Trends Using Machine Learning and Deep Learning Algorithms Via Continuous and Binary Data; a Comparative Analysis 2019Stock Market Analysis: A Review and Taxonomy of Prediction Techniques 2017A feature weighted support vector machine and K-nearest neighbor algorithm for stock market indices prediction

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