Models applied in stock market prediction : a literature survey
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Authors
Dassanayake, Wajira
Jayawardena, Chandimal
Ardekani, Iman
Sharifzadeh, Hamid
Jayawardena, Chandimal
Ardekani, Iman
Sharifzadeh, Hamid
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Date
2019-03-07
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Journal Article
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Keyword
stock markets
stock movement prediction
correlation analysis
stock price analysis
computer modelling
literature reviews
stock movement prediction
correlation analysis
stock price analysis
computer modelling
literature reviews
ANZSRC Field of Research Code (2020)
Citation
Dassanayake, W., Jayawardena, C., Ardekani. I., & Sharifzadeh, H. (2019). Models applied in stock market prediction: a literature survey, Occasional and discussion paper series, 2019(1) ISSN 2324-3635 Retrieved from http://www.unitec.ac.nz/epress
Abstract
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.
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Unitec ePress
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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.
Attribution-NonCommercial-NoDerivs 3.0 New Zealand
Attribution-NonCommercial-NoDerivs 3.0 New Zealand