Deep learning methods for the accurate modeling and forecasting of the Indian stock market (Record no. 19042)
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| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20230327093349.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 230327b xxu||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | AIKTC-KRRC |
| Transcribing agency | AIKTC-KRRC |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 20272 |
| Author | Saqware, Godfrey Joseph |
| 245 ## - TITLE STATEMENT | |
| Title | Deep learning methods for the accurate modeling and forecasting of the Indian stock market |
| 250 ## - EDITION STATEMENT | |
| Volume, Issue number | Vol.13(1), Oct |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | Chennai |
| Name of publisher, distributor, etc. | ICT Academy |
| Year | 2022 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Pagination | 2765-2774p. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | The stock markets are among the most volatile market worldwide. The<br/>future of these markets is daily affected by political instability and<br/>different enacted economic and government policies. Thus, the<br/>prediction and forecast of these markets are very important. The<br/>Bombay Stock Exchange (BSE) is the oldest stock market in Asia and<br/>India. This paper applied deep learning methods to predict the five<br/>companies closing prices under BSE. The selected companies based on<br/>market capitalization were Reliance Industries Ltd (RELI), TATA<br/>Consultancy Services (TCS), HDFC Bank Ltd (HDBK), Infosys Ltd<br/>(INFY), and ICICI Bank Ltd (ICBK). Based on Root Mean Square<br/>Error (RMSE), the traditional Bidirectional Long Short-Term Model<br/>(Bi-LSTM) model predicted well the HDBK closing prices. The<br/>Convolution Neural Networks (CNN) outperformed other models in<br/>predicting the ICBK, RELI, and INFY. The proposed Hybrid CNN-<br/>LSTM model with Bayesian hyperparameter tuning outperformed the<br/>CNN and Bi-LSTM models in predicting the TCS close price.<br/>Moreover, the hybrid model ranked second in predicting closing prices<br/>in all the selected companies. The next 100 days forecast shows high<br/>price volatility in the selected companies. In the closing prices<br/>forecasts, the hybrid CNN-LSTM model with Bayesian hyperparameter<br/>tuning has captured well the trend of the historical data. Additionally,<br/>Traders and financial analysts may easily understand the future market<br/>trend using the methods. Therefore, the powerful computer and more<br/>complex hybrid model may be applied to bring the best performance in<br/>terms of accuracy. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| 9 (RLIN) | 4622 |
| Topical term or geographic name entry element | Computer Engineering |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 20273 |
| Co-Author | Ismail, B. |
| 773 0# - HOST ITEM ENTRY | |
| Place, publisher, and date of publication | Chennai ICT Academy |
| Title | ICTACT Journal on Soft Computing (IJSC) |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| URL | https://ictactjournals.in/paper/IJSC_Vol_13_Iss_1_Paper_4_2765_2774.pdf |
| Link text | Click here |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Articles Abstract Database |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Home library | Current library | Shelving location | Date acquired | Total Checkouts | Barcode | Date last seen | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | School of Engineering & Technology | School of Engineering & Technology | Archieval Section | 27/03/2023 | 2023-0513 | 27/03/2023 | 27/03/2023 | Articles Abstract Database |