Deep learning methods for the accurate modeling and forecasting of the Indian stock market
By: Saqware, Godfrey Joseph
.
Contributor(s): Ismail, B.
Publisher: Chennai ICT Academy 2022Edition: Vol.13(1), Oct.Description: 2765-2774p.Subject(s): Computer EngineeringOnline resources: Click here
In:
ICTACT Journal on Soft Computing (IJSC)Summary: 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.
| Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|---|
Articles Abstract Database
|
School of Engineering & Technology Archieval Section | Not for loan | 2023-0513 |
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.
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