Articlehybrid2019

An Empirical Study of Machine Learning Algorithms for Stock Daily Trading Strategy

66 citations0/0 files storedMathematical Problems in Engineering

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Stock Market Forecasting Methods Financial Markets and Investment Strategies Financial Analysis and Corporate Governance

Abstract

According to the forecast of stock price trends, investors trade stocks. In recent years, many researchers focus on adopting machine learning (ML) algorithms to predict stock price trends. However, their studies were carried out on small stock datasets with limited features, short backtesting period, and no consideration of transaction cost. And their experimental results lack statistical significance test. In this paper, on large‐scale stock datasets, we synthetically evaluate various ML algorithms and observe the daily trading performance of stocks under transaction cost and no transaction cost. Particularly, we use two large datasets of 424 S&P 500 index component stocks (SPICS) and 185 CSI 300 index component stocks (CSICS) from 2010 to 2017 and compare six traditional ML algorithms and six advanced deep neural network (DNN) models on these two datasets, respectively. The experimental results demonstrate that traditional ML algorithms have a better performance in most of the directional evaluation indicators. Unexpectedly, the performance of some traditional ML algorithms is not much worse than that of the best DNN models without considering the transaction cost. Moreover, the trading performance of all ML algorithms is sensitive to the changes of transaction cost. Compared with the traditional ML algorithms, DNN models have better performance considering transaction cost. Meanwhile, the impact of transparent transaction cost and implicit transaction cost on trading performance are different. Our conclusions are significant to choose the best algorithm for stock trading in different markets.

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References

2017Deep learning with long short-term memory networks for financial market predictions 2014Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques 2017A deep learning framework for financial time series using stacked autoencoders and long-short term memory 2004Forecasting stock market movement direction with support vector machine 2017Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies 2018Forecasting the volatility of stock price index: A hybrid model integrating LSTM with multiple GARCH-type models 2016Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500 2016Computational Intelligence and Financial Markets: A Survey and Future Directions

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