Correlation-predictability analysis for intraday predictions

Pik Yin Mok, K. P. Lam, H. S. Ng

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

Abstract

Intraday 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.
Original languageEnglish
Title of host publicationProceedings of the Second IASTED International Conference On Financial Engineering and Applications
Pages191-196
Number of pages6
Publication statusPublished - 1 Dec 2004
Externally publishedYes
EventProceedings of the Second IASTED International Conference on Financial Engineering and Applications - Cambridge, MA, United States
Duration: 8 Nov 200410 Nov 2004

Conference

ConferenceProceedings of the Second IASTED International Conference on Financial Engineering and Applications
CountryUnited States
CityCambridge, MA
Period8/11/0410/11/04

Keywords

  • Eigen-analysis
  • High frequency time series
  • Intraday analysis
  • Low frequency time series

ASJC Scopus subject areas

  • Engineering(all)

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