NDLI: Stock price prediction using intraday and AHIPMI data

Content Provider	IEEE Xplore Digital Library
Author	Lam, K.P. Mok, P.Y.
Copyright Year	2002
Description	Author affiliation: Dept. of Syst. Eng. & Eng. Manage., Chinese Univ. of Hong Kong, Shatin, China (Lam, K.P.; Mok, P.Y.)
Abstract	Partitioned linear-nonlinear models are developed to improve in-sample precision and reduce sensitivity to out-sample modelling errors in stock price predictions. Such partitioned models are compared with linear regression models and nonlinear neural network models. The partitioned models demonstrate similar performance to the nonlinear models in both in-sample and out-sample predictions. Robust prediction schemes are then introduced to improve the predictabilities of partitioned models. Such partitioned models with robust schemes outperformed both linear regression models and nonlinear neural networks models in terms of prediction accuracy as well as model robustness. In addition, a linear relationship of non-model-based correlation and linear-regression-model-based predictability is found to exist between intraday (as well as AHI-PMI) data and stock price indexes of open, close, high and low.
Sponsorship	Asia-Pacific Neural Network Assembly Singapore Neuroscience Assoc. SEAL & FSKD Conference Steering Committees IEEE Neural Networks Soc. Int. Neural Network Soc. Eur. Neural Network Soc. SPIE
Starting Page	2167
Ending Page	2171
File Size	511296
Page Count	5
File Format	PDF
ISBN	9810475241
DOI	10.1109/ICONIP.2002.1201876
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-11-18
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Nanyang Technological University
Subject Keyword	Predictive models Robustness Linear regression Neural networks Stock markets Investments Time series analysis Information analysis Systems engineering and theory Research and development management
Content Type	Text
Resource Type	Article

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