Stock price prediction using intraday and AHIPMI data

K. P. Lam, Pik Yin Mok

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

4 Citations (Scopus)


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 languageEnglish
Title of host publicationICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing
Subtitle of host publicationComputational Intelligence for the E-Age
Number of pages5
ISBN (Electronic)9789810475246, 9810475241
Publication statusPublished - 1 Jan 2002
Externally publishedYes
Event9th International Conference on Neural Information Processing, ICONIP 2002 - Orchid Country Club, Singapore, Singapore
Duration: 18 Nov 200222 Nov 2002


Conference9th International Conference on Neural Information Processing, ICONIP 2002

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

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