Correlation-predictability analysis for intraday predictions
Refereed conference paper presented and published in conference proceedings

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AbstractIntraday financial data can be interpreted based on high-frequency and low-frequency time-series modeling. Recent study has revealed the complexity of high-frequency dynamics using correlation analysis, in related with randomized matrices, eigen-decomposition, and hierarchical grouping of stocks. As an alternative approach, we present some ideas on low-frequency "news" modeling as applied to intraday data. A comparative eigen-analysis is described, showing the regularity of correlation matrix and the significance of variance-weighted principal components. It is also shown that low-frequency modeling is related with a receding-horizon intraday prediction problem, where improved predictability is conditional upon the available information up to the current time. Strong empirical evidence is obtained for a linear correlation-predictability relationship for intraday high, low and close prediction, which shows promises in applying to the NASDAQ composite index and other financial data. The relationship implies that a non-model based correlation measure can predict the performance of linear regression prediction model that uses intraday information.
All Author(s) ListMok P.Y., Lam K.P., Ng H.S.
Name of ConferenceProceedings of the Second IASTED International Conference on Financial Engineering and Applications
Start Date of Conference08/11/2004
End Date of Conference10/11/2004
Place of ConferenceCambridge, MA
Country/Region of ConferenceUnited States of America
Pages191 - 196
LanguagesEnglish-United Kingdom
KeywordsEigen-analysis, High frequency time series, Intraday analysis, Low frequency time series

Last updated on 2020-09-08 at 02:12