Empirical estimation of multilayer perceptron for stock market indexes

Title

Empirical estimation of multilayer perceptron for stock market indexes

Creator

Chandra J.; Nachamai M.; Pillai A.S.

Description

The return on investment of stock market index is used to estimate the effectiveness of an investment in different savings schemes. To calculate Return on Investment, profit of an investment is divided by the cost of investment. The purpose of the paper is to perform empirical evaluation of various multilayer perceptron neural networks that are used for obtaining high quality prediction for Return on Investment based on stock market indexes. Many researchers have already implemented different methods to forecast stock prices, but accuracy of the stock prices are a major concern. The multilayer perceptron feed forward neural network model is implemented and compared against multilayer perceptron back propagation neural network models on various stock market indexes. The estimated values are checked against the original values of next business day to measure the actual accuracy. The uniqueness of the research is to achieve maximum accuracy in the Indian stock market indexes. The comparative analysis is done with the help of data set NSEindia historical data for Indian share market. Based on the comparative analysis, the multilayer perceptron feed forward neural network performs better prediction with higher accuracy than multilayer perceptron back propagation. A number of variations have been found by this comparative experiment to analyze the future values of the stock prices. With the experimental comparison, the multilayer perceptron feed forward neural network is able to forecast quality decision on return on investment on stock indexes with average accuracy rate as 95 % which is higher than back propagation neural network. So the results obtained by the multilayer perceptron feed forward neural networks are more satisfactory when compared to multilayer perceptron back propagation neural network. Springer International Publishing Switzerland 2016.

Source

Lecture Notes in Electrical Engineering, Vol-362, pp. 747-758.

Date

2016-01-01

Publisher

Springer Verlag

Subject

Adaptive linear neuron (ADALINE); Back propagation (BP); Feed forward (FF); Mean of magnitude relative error (MMRE); Multi layer perceptron (MLP); Neural network (NN); Return on investment (ROI)

Coverage

Chandra J., Christ University, Bangalore, India; Nachamai M., Christ University, Bangalore, India; Pillai A.S., Hindustan University, Chennai, India

Rights

Restricted Access

Relation

ISSN: 18761100; ISBN: 978-331924582-9

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-3-319-24584-3_63

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