A scalable multi-step hybrid model for stock index prediction

Dassanayake, Wajira

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Date
2017-10-05
Citation:
Dassanayake, W. (2017, October). A scalable multi-step hybrid model for stock index prediction. Paper presented at the Unitec Research Symposium, 2017, Unitec Institute of Technology, New Zealand.
Permanent link to Research Bank record:
https://hdl.handle.net/10652/4333
Abstract
My research intends to derive an intelligent multistep scalable hybrid model Integrating technical, fundamental and textual analyses. This dynamic multistep model would link the existing segregated models in the literature. The literature survey reveals that the existing models formulate marginally segregated subsets in the area of stock market price prediction. This research will be the first scientific exploration to combine the effects of historical factors, macroeconomic determinants and spontaneous events in a single multistep model to forecast the stock market prices.
Keywords:
stock markets, stock movement. prediction, correlation analysis, stock price analysis
ANZSRC Field of Research:
1502 Other Banking, Finance and Investment, 080109 Pattern Recognition and Data Mining
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