A Stock Prediction Model Based on DCNN

TL;DR

This paper introduces a deep CNN-based stock prediction model using candle charts, achieving 92.2% accuracy for 20-day forecasts and analyzing different classification methods for improved market trend prediction.

Contribution

The paper presents a novel deep CNN approach utilizing candle charts and multiple classification strategies for stock trend prediction, with enhanced accuracy over existing methods.

Findings

Best performance at 20-day forecast interval

Inclusion of Moving Average Convergence Divergence improves accuracy

Achieved 92.2% prediction accuracy on US NDAQ stock data

Abstract

The prediction of a stock price has always been a challenging issue, as its volatility can be affected by many factors such as national policies, company financial reports, industry performance, and investor sentiment etc.. In this paper, we present a prediction model based on deep CNN and the candle charts, the continuous time stock information is processed. According to different information richness, prediction time interval and classification method, the original data is divided into multiple categories as the training set of CNN. In addition, the convolutional neural network is used to predict the stock market and analyze the difference in accuracy under different classification methods. The results show that the method has the best performance when the forecast time interval is 20 days. Moreover, the Moving Average Convergence Divergence and three kinds of moving average are added as input. This method can accurately predict the stock trend of the US NDAQ exchange for 92.2%. Meanwhile, this article distinguishes three conventional classification methods to provide guidance for future research.