Robust Analysis of Stock Price Time Series Using CNN and LSTM-Based Deep Learning Models

TL;DR

This paper develops and compares CNN and LSTM-based deep learning models to predict stock prices with high accuracy using granular historical data, challenging the notion that stock prediction is impossible.

Contribution

It introduces a suite of CNN and LSTM models for stock price prediction and evaluates their performance on high-frequency data, demonstrating high accuracy.

Findings

CNN and LSTM models achieve low RMSE in stock prediction.

Models show competitive forecasting accuracy with detailed performance analysis.

Deep learning models outperform traditional methods in this context.

Abstract

Prediction of stock price and stock price movement patterns has always been a critical area of research. While the well-known efficient market hypothesis rules out any possibility of accurate prediction of stock prices, there are formal propositions in the literature demonstrating accurate modeling of the predictive systems that can enable us to predict stock prices with a very high level of accuracy. In this paper, we present a suite of deep learning-based regression models that yields a very high level of accuracy in stock price prediction. To build our predictive models, we use the historical stock price data of a well-known company listed in the National Stock Exchange (NSE) of India during the period December 31, 2012 to January 9, 2015. The stock prices are recorded at five minutes intervals of time during each working day in a week. Using these extremely granular stock price data, we build four convolutional neural network (CNN) and five long- and short-term memory (LSTM)-based deep learning models for accurate forecasting of the future stock prices. We provide detailed results on the forecasting accuracies of all our proposed models based on their execution time and their root mean square error (RMSE) values.