Stock price prediction using DEEP learning algorithm and its comparison with machine learning algorithms

TLDR

Security indices are key tools for assessing financial markets, and because stock market investment constitutes a major part of national economies, accurate prediction of market trends could enhance investor returns, though the series’ nonlinearity and nonstationarity make such prediction challenging. This study evaluates the predictive performance of machine‑learning models for stock market forecasting. Using daily close prices of the iShares MSCI United Kingdom ETF from January 2015 to June 2018, the authors applied four machine‑learning algorithms to forecast future prices. The deep‑learning model achieved the lowest prediction error, outperforming support‑vector regression, neural networks, and random forests, which exhibited progressively higher errors.

Abstract

Summary Security indices are the main tools for evaluation of the status of financial markets. Moreover, a main part of the economy of any country is constituted of investment in stock markets. Therefore, investors could maximize the return of investment if it becomes possible to predict the future trend of stock market with appropriate methods. The nonlinearity and nonstationarity of financial series make their prediction complicated. This study seeks to evaluate the prediction power of machine‐learning models in a stock market. The data used in this study include the daily close price data of iShares MSCI United Kingdom exchange‐traded fund from January 2015 to June 2018. The prediction process is done through four models of machine‐learning algorithms. The results indicate that the deep learning method is better in prediction than the other methods, and the support vector regression method is in the next rank with respect to neural network and random forest methods with less error.