The propensity of applying neural networks has been proliferated in multiple disciplines for research activities since the past recent decades because of its powerful control with regulatory parameters for pattern recognition and classification. It is also being widely applied for forecasting in the numerous divisions. Since financial data have been readily available due to the involvement of computers and computing systems in the stock market premises throughout the world, researchers have also developed numerous techniques and algorithms to analyze the data from this sector. In this paper we have applied neural network with backpropagation technique to find the data pattern from finance section and prediction for stock values as well. © Published under licence by IOP Publishing Ltd.

Raja Das

Department of Mathematics

School of Advanced Sciences

Vellore Campus

Jaiswal J.K

Fulltext

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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Application of artificial neural networks with backpropagation technique in the financial data

IOP Conference Series: Materials Science and Engineering

The application of artificial neural network (ANN) has become quite ubiquitous in numerous disciplines with different motivations and approaches. One of the most contemporary implementations accounts it for stock price behavior analysis and forecasting. The stochastic behavior of stock market follows numerous factors to determine the price vicissitudes such as GDP, supply and demand, political influences, finance, and many more. In this paper, we have considered two ANN techniques, viz., backpropagation-based neural network (BPNN) and radial basis function network (RBFN), first, without principal component analysis (PCA), and further modified the model with PCA, to execute financial time series forecasting for the next 5 days (which can also be extended for some other number of days) by accepting the input as historical data on the sliding window basis. Moreover, the empirical research is conducted to verify the forecasting impact on the stock prices for oil and natural gas sector in India with the developed model, and subsequently a comparison study has also been performed for the effectiveness of the two models without and with PCA, on the basis of mean square percentage error. © 2018, Springer Nature Switzerland AG.

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Journal	Data powered by TypesetIOP Conference Series: Materials Science and Engineering
Publisher	Data powered by TypesetIOP Publishing
ISSN	1757-8981
Open Access	Yes