Prediction of Stock Market using Stochastic Neural Networks
Journal Title: International Journal of Innovative Research in Computer Science and Technology - Year 2019, Vol 7, Issue 5
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.
Authors and Affiliations
Bhanu Teja Reddy, Usha J. C
Keywords
Related Articles
- EP ID EP748077
- DOI 10.21276/ijircst.2019.7.5.1
- Views 46
- Downloads 0
A Review of Video Detection and Tracking Methods for Moving Objects
Object detection and tracking are widely utilized in today's society, especially for motion detection of different objects. The initial stage in object detection is to recognize objects in the video stream and cluster th...
Analysis of Customer Churn Prediction in Telecom Industry Using Logistic Regression
Customers plays an import role in industry to run industry. Churn of the customer may lead many consequences. Customer churn prediction must the important aspect of any company. This helps in the detection of customers w...
Practicing LSB Steganography in PCA Transform Field
PCA conversion defines one main stream and two sub-streams for the color of homogenous areas. This subject has led to design some effective algorithms for the processing of colored images such as coloring, changing the c...
Big Data Privacy in Biomedical Research
The examination of patient data, which may contain personally identifiable information, is a common part of biomedical research. If these data are misused, it could result in the disclosure of private patient information...
Single Image Super Resolution with Wavelet Domain Transformation and Sparse Representation
In this paper, we have proposed a new image resolution enhancement algorithm based on discrete wavelet transform (DWT), lifting wavelet transform (LWT) and sparse recovery of the input image. A single low resolution (LR)...