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Stock Market Analysis: A Review and Taxonomy of Prediction Techniques

School of Computing, Queen’s University, Kingston, ON K7L 2N8, Canada
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Int. J. Financial Stud. 2019, 7(2), 26; https://doi.org/10.3390/ijfs7020026
Received: 4 March 2019 / Revised: 29 April 2019 / Accepted: 15 May 2019 / Published: 27 May 2019
(This article belongs to the Special Issue New Trends in Algorithmic Trading)
Stock market prediction has always caught the attention of many analysts and researchers. Popular theories suggest that stock markets are essentially a random walk and it is a fool’s game to try and predict them. Predicting stock prices is a challenging problem in itself because of the number of variables which are involved. In the short term, the market behaves like a voting machine but in the longer term, it acts like a weighing machine and hence there is scope for predicting the market movements for a longer timeframe. Application of machine learning techniques and other algorithms for stock price analysis and forecasting is an area that shows great promise. In this paper, we first provide a concise review of stock markets and taxonomy of stock market prediction methods. We then focus on some of the research achievements in stock analysis and prediction. We discuss technical, fundamental, short- and long-term approaches used for stock analysis. Finally, we present some challenges and research opportunities in this field. View Full-Text
Keywords: stock exchanges; stock markets; analysis; prediction; statistics; machine learning; pattern recognition; sentiment analysis stock exchanges; stock markets; analysis; prediction; statistics; machine learning; pattern recognition; sentiment analysis
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Shah, D.; Isah, H.; Zulkernine, F. Stock Market Analysis: A Review and Taxonomy of Prediction Techniques. Int. J. Financial Stud. 2019, 7, 26.

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