Stock price prediction using intraday and AHIPMI data

K. P. Lam; Pik Yin Mok

doi:10.1109/ICONIP.2002.1201876

Stock price prediction using intraday and AHIPMI data

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

6 Citations (Scopus)

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.

Original language	English
Title of host publication	ICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing
Subtitle of host publication	Computational Intelligence for the E-Age
Publisher	IEEE
Pages	2167-2171
Number of pages	5
Volume	5
ISBN (Electronic)	9789810475246, 9810475241
DOIs	https://doi.org/10.1109/ICONIP.2002.1201876
Publication status	Published - 1 Jan 2002
Externally published	Yes
Event	9th International Conference on Neural Information Processing, ICONIP 2002 - Orchid Country Club, Singapore, Singapore Duration: 18 Nov 2002 → 22 Nov 2002

Conference

Conference	9th International Conference on Neural Information Processing, ICONIP 2002
Country/Territory	Singapore
City	Singapore
Period	18/11/02 → 22/11/02

ASJC Scopus subject areas

Computer Networks and Communications
Information Systems
Signal Processing

More information

10.1109/ICONIP.2002.1201876

Cite this

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title = "Stock price prediction using intraday and AHIPMI data",

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.",

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Lam, KP & Mok, PY 2002, Stock price prediction using intraday and AHIPMI data. in ICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing: Computational Intelligence for the E-Age. vol. 5, 1201876, IEEE, pp. 2167-2171, 9th International Conference on Neural Information Processing, ICONIP 2002, Singapore, Singapore, 18/11/02. https://doi.org/10.1109/ICONIP.2002.1201876

Stock price prediction using intraday and AHIPMI data. / Lam, K. P.; Mok, Pik Yin.
ICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing: Computational Intelligence for the E-Age. Vol. 5 IEEE, 2002. p. 2167-2171 1201876.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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AU - Lam, K. P.

AU - Mok, Pik Yin

PY - 2002/1/1

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N2 - 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.

AB - 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.

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ER -

Stock price prediction using intraday and AHIPMI data

Abstract

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ASJC Scopus subject areas

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