| 000 -LEADER |
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| fixed length control field |
02468nam a22001577a 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
220509b ||||| |||| 00| 0 eng d |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Prasad, Akhilesh and Seetharama, Arumugam |
| 245 ## - TITLE STATEMENT |
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| Title |
Importance of machine learning in making investment decision in stock market |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Place of publication, distribution, etc |
Vikalpa: The Journal for Decision Makers |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
46(4), Oct-Dec, 2021: p.209-222 |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc |
Predicting stock trends in the financial market is always demanding but satisfying as well. With the growing power of computing and the recent development of graphics processing unit and tensor processing unit, analysts and researchers are applying advanced techniques such as machine learning techniques more and more to predict stock price trends. In recent years, researchers have developed several algorithms to predict stock trends. To assist investors interested in investing in the stock market, preferably for a short period, it has become necessary to review research papers dealing on machine learning and analyse the importance of their findings in the context of how stock price trends generate trading signals. In this article, to achieve the stated task, authors scrutinized more than 50 research papers focusing on various machine learning algorithms with varied levels of input variables and found that though the performance of models measured by root-mean-square error (RMSE) for regression and accuracy score for classification models varied greatly, long short-term memory (LSTM) model displayed higher accuracy amongst the machine and deep learning models reviewed. However, reinforcement learning algorithm performance measured by profitability and Sharpe ratio outperformed all. In general, traders can maximize their profits by using machine learning instead of using technical analysis. Technical analysis is very easy to implement, but the profit based on it can vanish too soon or making a profit using technical analysis is almost difficult because of its simplicity. Hence, studying machine, deep and reinforcement learning algorithms is vital for traders and investors. These findings were based on the literature review consolidated in the result section. – Reproduced |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Machine learning, Deep learning, Reinforcement learning, Stock price, Trading volume, Technical indicators, News’ sentiments, News titles |
| 9 (RLIN) |
30996 |
| 773 ## - HOST ITEM ENTRY |
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| Main entry heading |
Vikalpa: The Journal for Decision Makers |
| 906 ## - LOCAL DATA ELEMENT F, LDF (RLIN) |
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| Subject DIP |
MACHINE LEARNING |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Item type |
Articles |
