Comparative Analysis of Recurrent Neural Networks in Stock Price Prediction for Different Frequency Domains

Polash Dey,Md Ishtiaque Hossain,Md Shariful Alam,Emam Hossain,Mohammad Shahadat Hossain,Mohammed Armanuzzaman Chowdhury,Karl Andersson

doi:10.3390/a14080251

Polash Dey, Md Ishtiaque Hossain + Show 5 more

Open Access

https://doi.org/10.3390/a14080251

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Abstract

Investors in the stock market have always been in search of novel and unique techniques so that they can successfully predict stock price movement and make a big profit. However, investors continue to look for improved and new techniques to beat the market instead of old and traditional ones. Therefore, researchers are continuously working to build novel techniques to supply the demand of investors. Different types of recurrent neural networks (RNN) are used in time series analyses, especially in stock price prediction. However, since not all stocks’ prices follow the same trend, a single model cannot be used to predict the movement of all types of stock’s price. Therefore, in this research we conducted a comparative analysis of three commonly used RNNs—simple RNN, Long Short Term Memory (LSTM), and Gated Recurrent Unit (GRU)—and analyzed their efficiency for stocks having different stock trends and various price ranges and for different time frequencies. We considered three companies’ datasets from 30 June 2000 to 21 July 2020. The stocks follow different trends of price movements, with price ranges of $30, $50, and $290 during this period. We also analyzed the performance for one-day, three-day, and five-day time intervals. We compared the performance of RNN, LSTM, and GRU in terms of R2 value, MAE, MAPE, and RMSE metrics. The results show that simple RNN is outperformed by LSTM and GRU because RNN is susceptible to vanishing gradient problems, while the other two models are not. Moreover, GRU produces lesser errors comparing to LSTM. It is also evident from the results that as the time intervals get smaller, the models produce lower errors and higher reliability.

Highlights

A stock market is a place where companies issue their stocks to enlarge their business and investors can buy/sell the stocks to each other at specific prices
We have analyzed the effectiveness of Recurrent Neural Networks (Simple recurrent neural networks (RNN), Long Short Term Memory (LSTM), and Gated Recurrent Unit (GRU)) while predicting different types of stocks’ price movements
We discuss the performance of RNN, LSTM, and GRU on the three datasets for the three different time-intervals

Summary

Introduction

A stock market is a place where companies issue their stocks to enlarge their business and investors can buy/sell the stocks to each other at specific prices. Investors around the world can buy a company’s stock and enjoy yearly dividends for their shares. They can sell their stocks at any time and can make a profit by selling at a price higher than their buying. Stock market investment seems lucrative, predicting stock movements in competitive financial markets is a challenge, even for experienced traders and stock experts. Many experts and economists have been continuously trying to make stock predictions using a variety of methods for the past few decades. Manually predicting stock price trends from stock data is a tedious task. With the advent of artificial intelligence, the automated method of predicting the stock market through big data and enhanced computing capabilities has become possible. This work can be categorized as big data research, as we have considered stock data of the timeframe 2000–2020 [10]

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