000			02468nam a22001577a 4500
999			_c519797 _d519797
008			220509b ||||| |||| 00| 0 eng d
100			_aPrasad, Akhilesh and Seetharama, Arumugam _933065
245			_aImportance of machine learning in making investment decision in stock market
260			_aVikalpa: The Journal for Decision Makers
300			_a46(4), Oct-Dec, 2021: p.209-222
520			_aPredicting 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			_aMachine learning, Deep learning, Reinforcement learning, Stock price, Trading volume, Technical indicators, News’ sentiments, News titles _930996
773			_aVikalpa: The Journal for Decision Makers
906			_aMACHINE LEARNING
942			_cAR