Short Term Stock Price Prediction in Indian Market: A Neural Network Perspective

Soham Banerjee and Diganta Mukherjee

Studies in Microeconomics, 2022, vol. 10, issue 1, 23-49

Abstract: In recent times there has been an increasing level of debate whether patterns do exist in equity market movements and whether they can be predicted. In order to overcome the shortcomings of traditional time series models, we have focused our study on the application of non-parametric paradigms like stacked multi-layer perceptrons (MLP), long short term memory (LSTM), gated recurrent unit (GRU), bidirectional long short term memory (BLSTM) and gated bidirectional recurrent unit (BGRU) on three NSE listed banks to predict short term stock price, and compared their performance with a shallow neural network benchmark. We have predicted equity ‘Close Prices’ five minutes into the future, using a sliding window approach and have observed that average error in predictions of MLP, LSTM, GRU, BLSTM and BGRU models, varied between 0.09% and 0.1%, indicating their superior performance with regard to benchmark baseline of 0.88%. We have used the aforementioned predictions to determine price trends, which successfully outperformed the random walk baseline accuracy of 50%. JEL Classification: C45, C58, G11, G14

Keywords: Stock market; MLP; LSTM; GRU; BLSTM; BGRU (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:sae:miceco:v:10:y:2022:i:1:p:23-49

DOI: 10.1177/2321022220980537

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