Stock Market Analysis: A Review and Taxonomy of Prediction Techniques
By Dev Shah, Haruna Isah, F. Zulkernine. International Journal of Financial Studies, 2019
390 citations recorded by the source corpus.
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.
Subfields: Finance, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/d81bd5721dd2ed72ba9cb566102ee883a074428a
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Finance
In Management Science and Operations Research
Citations in the corpus
- Stock price prediction with optimized deep LSTM network with artificial rabbits optimization algorithm (2023, background): “…prices of stocks are determined by the law of supply and demand, where buyers are willing to pay more for a stock if they think it will go up in value, and sellers are willing to accept less if they think it will go down in value (Bosworth et al., 1975; Shah et al., 2019; Teweles & Bradley, 1998).”
- Applications of deep learning in stock market prediction: recent progress (2020, background): “Some latest reviews are trying to cover a wider range, e.g., Shah et al. (2019) covers machine learning techniques applied to the prediction of financial market prices, and Sezer et al. (2019) covers more financial instruments.”