Analysis and Design of an Efficient Temporal Data Mining Model for the Indian Stock Market

Abraham, Cerene Mariam; Elayidom, M. Sudheep; Santhanakrishnan, T.

doi:10.1007/978-981-13-1498-8_54

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…Several machine learning techniques have been applied to predict stock price movement [11][12][13]. In order to obtain appropriate predictions, this materialistic stock data have been merged with computational intellectual-based procedures [14][15][16] and different econometrics-based factual strategies [17][18][19]. The numerical techniques are probably going to be subject to the underlying presumptions; on the contrary, the AI approaches experience controlled interoperability, performing based on manually selected features, and over-fitting issues; this supports a mix of neural network (NN)-based deep learning strategies to upgrade the predictions of the stock market [20][21][22].…”

Section: Related Workmentioning

confidence: 99%

Comparative Analysis of Recurrent Neural Networks in Stock Price Prediction for Different Frequency Domains

Dey¹,

Hossain²,

Hossain

et al. 2021

Algorithms

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Investors in the stock market have always been in search of novel and unique techniques so that they can successfully predict stock price movement and make a big profit. However, investors continue to look for improved and new techniques to beat the market instead of old and traditional ones. Therefore, researchers are continuously working to build novel techniques to supply the demand of investors. Different types of recurrent neural networks (RNN) are used in time series analyses, especially in stock price prediction. However, since not all stocks’ prices follow the same trend, a single model cannot be used to predict the movement of all types of stock’s price. Therefore, in this research we conducted a comparative analysis of three commonly used RNNs—simple RNN, Long Short Term Memory (LSTM), and Gated Recurrent Unit (GRU)—and analyzed their efficiency for stocks having different stock trends and various price ranges and for different time frequencies. We considered three companies’ datasets from 30 June 2000 to 21 July 2020. The stocks follow different trends of price movements, with price ranges of $30, $50, and $290 during this period. We also analyzed the performance for one-day, three-day, and five-day time intervals. We compared the performance of RNN, LSTM, and GRU in terms of R2 value, MAE, MAPE, and RMSE metrics. The results show that simple RNN is outperformed by LSTM and GRU because RNN is susceptible to vanishing gradient problems, while the other two models are not. Moreover, GRU produces lesser errors comparing to LSTM. It is also evident from the results that as the time intervals get smaller, the models produce lower errors and higher reliability.

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