Abstract

Building predictive models for robust and accurate prediction of stock prices and stock price movement is a challenging research problem to solve. The well-known efficient market hypothesis believes in the impossibility of accurate prediction of future stock prices in an efficient stock market as the stock prices are assumed to be purely stochastic. However, numerous works proposed by researchers have demonstrated that it is possible to predict future stock prices with a high level of precision using sophisticated algorithms, model architectures, and the selection of appropriate variables in the models. This chapter proposes a collection of predictive regression models built on deep learning architecture for robust and precise prediction of the future prices of a stock listed in the diversified sectors in the National Stock Exchange (NSE) of India. The Metastock tool is used to download the historical stock prices over a period of two years (2013–2014) at 5 minutes intervals. While the records for the first year are used to train the models, the testing is carried out using the remaining records. The design approaches of all the models and their performance results are presented in detail. The models are also compared based on their execution time and accuracy of prediction.

Highlights

  • Building predictive models for robust and accurate prediction of stock prices and stock price movement is a very challenging research problem

  • We present a gamut of deep learning models built on Convolutional Neural Network (CNN) and Long-and-Short-Term Memory (LSTM) architectures and illustrate their efficacy and effectiveness in solving the same problem

  • Prediction of future stock prices and price movement patterns is a challenging task if the stock price time series has a large amount of volatility

Read more

Summary

Introduction

Building predictive models for robust and accurate prediction of stock prices and stock price movement is a very challenging research problem. Numerous works in the finance literature have shown that robust and precise prediction of future stock prices is using sophisticated machine learning and deep learning algorithms, model architectures, and selection of appropriate variables in the models. Machine Learning - Algorithms, Models and Applications work on searching and finding some pre-identified patterns and sequences in the time series of stock prices. Prior detection of such patterns can be useful for the investors in the stock market in formulating their investment strategies in the market to maximize their profit.

Related work
Methodology
The CNN_UNIV_5 model
The CNN_UNIV_10 model
The CNN_MULTV_10 model
The CNN_MULTH_10 model
The LSTM_UNIV_5 model
The LSTM_UNIV_10 model
The LSTM_UNIV_ED_10 model
The LSTM_MULTV_ED_10 model
The LSTM_UNIV_CNN_10 model
3.10 The LSTM_UNIV_CONV_10 model
Performance results
Findings
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call