Intraday stock price analysis and prediction
Refereed conference paper presented and published in conference proceedings

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AbstractThe close relationship between daily and intraday stock data is established using theoretical interpretation and variance estimation by neural network. Based on this, conventional time-series and neural networks are used to analyze the more informative intraday data for stock price prediction. Each method is tried with different set of parameters, in order to obtain an objective and thorough evaluation. The evaluation results show that Widrow-Hofrs LMS should be used given adequate computing resources and time. Back Propagation is optimal if the input parameters of the series are precisely known. ARMAX is a simple and parameter insensitive method. In general, it is a bad choice to use the trading volume as an exogenous input. Contradicting to intuition, simple models give better predictions than complex ones, and lightly trained is better than heavily trained.
All Author(s) ListCheung WS, Ng HS, Lam KP
Name of ConferenceIEEE International Conference on Management of Innovation and Technology (ICMIT 2000)
Start Date of Conference12/11/2000
End Date of Conference15/11/2000
Place of ConferenceSINGAPORE
Country/Region of ConferenceSingapore
Detailed description(Management in the 21st Century)
Year2000
Month1
Day1
PublisherIEEE
Pages47 - 52
ISBN0-7803-6652-2
LanguagesEnglish-United Kingdom
Keywordsintraday stock prices; neural network modeling; time-series prediction
Web of Science Subject CategoriesComputer Science; Computer Science, Information Systems; Engineering; Engineering, Industrial; Engineering, Manufacturing

Last updated on 2020-21-11 at 01:04