Short-term stock market price trend prediction using a comprehensive deep learning system

Jingyi Shen,M Omair Shafiq

doi:10.1186/s40537-020-00333-6

Abstract

In the era of big data, deep learning for predicting stock market prices and trends has become even more popular than before. We collected 2 years of data from Chinese stock market and proposed a comprehensive customization of feature engineering and deep learning-based model for predicting price trend of stock markets. The proposed solution is comprehensive as it includes pre-processing of the stock market dataset, utilization of multiple feature engineering techniques, combined with a customized deep learning based system for stock market price trend prediction. We conducted comprehensive evaluations on frequently used machine learning models and conclude that our proposed solution outperforms due to the comprehensive feature engineering that we built. The system achieves overall high accuracy for stock market trend prediction. With the detailed design and evaluation of prediction term lengths, feature engineering, and data pre-processing methods, this work contributes to the stock analysis research community both in the financial and technical domains.

Highlights

Stock market is one of the major fields that investors are dedicated to, stock market price trend prediction is always a hot topic for researchers from both financial and technical domains
We found most of the previous works in the technical domain were analyzing all the stocks, while in the financial domain, researchers prefer to analyze the specific scenario of investment, to fill the gap between the two domains, we decide to apply a feature extension based on the findings we gathered from the financial domain before we start the Recursive Feature Elimination (RFE) procedure
Test procedure included two parts, one testing dataset is for feature selection, and another one is for model testing

Summary

Introduction

Stock market is one of the major fields that investors are dedicated to, stock market price trend prediction is always a hot topic for researchers from both financial and technical domains. Back in 2003, Wang et al in [44] already applied artificial neural networks on stock market price prediction and focused on volume, as a specific feature of stock market. Ince and Trafalis in [15] targeted short-term forecasting and applied support vector machine (SVM) model on the stock price prediction. Their main contribution is performing a comparison between multi-layer perceptron (MLP) and SVM found that most of the scenarios SVM outperformed MLP, while

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