ICTACT Journal on Soft Computing

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Deep Learning Methods for the Accurate Modeling and Forecasting of the Indian Stock Market


Affiliations
1 Department of Statistics, University of Dar Es Salaam, Tanzania, United Republic of
2 Department of Statistics, Yenepoya University, India
     

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The stock markets are among the most volatile market worldwide. The future of these markets is daily affected by political instability and different enacted economic and government policies. Thus, the prediction and forecast of these markets are very important. The Bombay Stock Exchange (BSE) is the oldest stock market in Asia and India. This paper applied deep learning methods to predict the five companies closing prices under BSE. The selected companies based on market capitalization were Reliance Industries Ltd (RELI), TATA Consultancy Services (TCS), HDFC Bank Ltd (HDBK), Infosys Ltd (INFY), and ICICI Bank Ltd (ICBK). Based on Root Mean Square Error (RMSE), the traditional Bidirectional Long Short-Term Model (Bi-LSTM) model predicted well the HDBK closing prices. The Convolution Neural Networks (CNN) outperformed other models in predicting the ICBK, RELI, and INFY. The proposed Hybrid CNN-LSTM model with Bayesian hyperparameter tuning outperformed the CNN and Bi-LSTM models in predicting the TCS close price. Moreover, the hybrid model ranked second in predicting closing prices in all the selected companies. The next 100 days forecast shows high price volatility in the selected companies. In the closing prices forecasts, the hybrid CNN-LSTM model with Bayesian hyperparameter tuning has captured well the trend of the historical data. Additionally, Traders and financial analysts may easily understand the future market trend using the methods. Therefore, the powerful computer and more complex hybrid model may be applied to bring the best performance in terms of accuracy.

Keywords

Bayesian hyperparameter tuning, Bi-LSTM, Bombay Stock Exchange, CNN, and CNN-LSTM
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  • Deep Learning Methods for the Accurate Modeling and Forecasting of the Indian Stock Market

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Authors

Godfrey Joseph Saqware
Department of Statistics, University of Dar Es Salaam, Tanzania, United Republic of
B. Ismail
Department of Statistics, Yenepoya University, India

Abstract


The stock markets are among the most volatile market worldwide. The future of these markets is daily affected by political instability and different enacted economic and government policies. Thus, the prediction and forecast of these markets are very important. The Bombay Stock Exchange (BSE) is the oldest stock market in Asia and India. This paper applied deep learning methods to predict the five companies closing prices under BSE. The selected companies based on market capitalization were Reliance Industries Ltd (RELI), TATA Consultancy Services (TCS), HDFC Bank Ltd (HDBK), Infosys Ltd (INFY), and ICICI Bank Ltd (ICBK). Based on Root Mean Square Error (RMSE), the traditional Bidirectional Long Short-Term Model (Bi-LSTM) model predicted well the HDBK closing prices. The Convolution Neural Networks (CNN) outperformed other models in predicting the ICBK, RELI, and INFY. The proposed Hybrid CNN-LSTM model with Bayesian hyperparameter tuning outperformed the CNN and Bi-LSTM models in predicting the TCS close price. Moreover, the hybrid model ranked second in predicting closing prices in all the selected companies. The next 100 days forecast shows high price volatility in the selected companies. In the closing prices forecasts, the hybrid CNN-LSTM model with Bayesian hyperparameter tuning has captured well the trend of the historical data. Additionally, Traders and financial analysts may easily understand the future market trend using the methods. Therefore, the powerful computer and more complex hybrid model may be applied to bring the best performance in terms of accuracy.

Keywords


Bayesian hyperparameter tuning, Bi-LSTM, Bombay Stock Exchange, CNN, and CNN-LSTM

References