Analysis on Indian Stock Market Prediction Using Deep Learning Models

Analysis on Indian Stock Market Prediction Using Deep Learning Models

Kalaivani Karuppiah, Umamaheswari N., Venkatesh R.
Copyright: © 2021 |Pages: 11
DOI: 10.4018/978-1-7998-2566-1.ch004
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Abstract

The neural network is one of the best data mining techniques that have been used by researchers in different areas for the past 10 years. Analysis on Indian stock market prediction using deep learning models plays a very important role in today's economy. In this chapter, various deep learning architectures such as multilayer perceptron, recurrent neural networks, long short -term memory, and convolutional neural network help to predict the stock market prediction. There are two different stock market price companies, namely National Stock Exchange and New York Stock Exchange, are used for analyzing the day-wise closing price used for comparing different techniques such as neural network, multilayer perceptron, and so on. Both the NSE and NYSE share their common details, and they are compared with various existing models. When compared with the previous existing models, neural networks obtain higher accuracy, and their experimental result is shown in betterment compared with existing techniques.
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Artificial Neural Network

ANN (Wang et al., 2011) is a computational shape which performs alongside these lines to that of organic neurons (Moghaddam et al., 2016). Its miles intended to differentiate a hidden sample from information and to sum up from it. ANN's are taken into consideration as non-direct real records equipment (Rather et al., 2015). The complex connection amongst yields and records resources may be proven utilizing ANN. The primary favorable role of ANN is its potential to take within the hidden examples from the statistics, where the more part of the ordinary techniques comes up brief (Zhang et al., 1998). Normally, ANN incorporates of three layers especially enter layer, shrouded layer and yield layer. Non-direct enactment capacities are applied in every one of the hubs in protected up just as yield layers barring enter layer. Every hub inside the records layer is associated with every neuron in the succeeding concealed layer pursued by using yield layer.

Figure 1.

Artificial neuron

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