Models applied in stock market prediction : a literature survey
Dassanayake, Wajira; Jayawardena, Chandimal; Ardekani, Iman; Sharifzadeh, Hamid
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Citation:Dassanayake, W., Jayawardena, C., Ardekani. I., & Sharifzadeh, H. (2019). Models Applied in Stock Market Prediction: A Literature Survey. (Unitec ePress Occasional and Discussion Paper Series 2019/01). Unitec ePress. ISSN 2324-3635 Retrieved from http://www.unitec.ac.nz/epress
Permanent link to Research Bank record:https://hdl.handle.net/10652/4549
Stock market prices are intrinsically dynamic, volatile, highly sensitive, nonparametric, nonlinear and chaotic in nature, as they are influenced by a myriad of interrelated factors. As such, stock market time series prediction is complex and challenging. Many researchers have been attempting to predict stock market price movements using various techniques and different methodological approaches. Recent literature confirms that hybrid models, integrating linear and non-linear functions or statistical and learning models, are better suited for training, prediction and generalisation performance of stock market prices. The purpose of this review is to investigate different techniques applied in stock market price prediction with special emphasis on hybrid models.
Keywords:stock markets, stock movement, prediction, correlation analysis, stock price analysis, computer modeling, literature reviews
ANZSRC Field of Research:150299 Banking, Finance and Investment not elsewhere classified, 0802 Computation Theory and Mathematics
Copyright Notice:Models Applied in Stock Market Prediction: A Literature Survey by Wajira Dassanayake, Chandimal Jayawardena, Iman Ardekani and Hamid Sharifzadeh is licensed under a Creative Commons AttributionNonCommercial 4.0 International License.
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