Prediction of Stock Market using Stochastic Neural Networks

Authors

  • Bhanu Teja Reddy Financial Management, M.S Ramaiah University of Applied Sciences, Bengaluru, India, Author
  • Usha J C Financial Management, M.S Ramaiah University of Applied Sciences, Bengaluru, India Author

Keywords:

Artificial Neural Networks, Long Short -Term Memory (LSTM), Stochastic Neural Networks (SNN), Stock Market Prediction, Tokyo Stock Exchange (TSE)

Abstract

The primary objective of investors and  stockbrokers is to make profits by being able to predict  the financial markets. However, forecasting is a  complex task since the financial markets have a  complicated pattern. This study addresses the direction  of the stock price index for Japanese Nikkei 225. The  research compares two prediction models, i.e., the  Stochastic Neural Networks (SNN) and fusion of Long  -Short Term Memory and Stochastic Neural Networks (LSTM - SNN) for predicting the index. The input layer  includes computation of fifteen technical indicators  using stock market parameters (open, high, low, close  prices, and volume). Accuracy of each of the prediction  models was evaluated using price and trend  performance metrics. The evaluation was carried out  for historical data from 23rd January 2007 to 30th December 2013 of the Tokyo Stock Exchange (TSE).  The experimental outcomes recommend that for the  SNN, the model gave an accuracy of 85.37% and hybrid  of LSTM – SNN gave accuracy of 86.28%. The increase  in the accuracy of LSTM – SNN was due to the  introduction of LSTM layer. Experimental outcomes  also illustrate that the performance of both the  prediction models progress when these technical  indicators are added to the input layer of the proposed  models. 

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Published

2019-11-01

Issue

Vol. 7 No. 5 (2019): International Journal of Innovative Research in Computer Science & Technology (Sep) 2019

Section

Articles

How to Cite

Prediction of Stock Market using Stochastic Neural Networks . (2019). International Journal of Innovative Research in Computer Science & Technology, 7(5), 128–138. Retrieved from https://acspublisher.com/journals/index.php/ijircst/article/view/13216