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 language | English |
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| Title of host publication | Proceedings of the Second IASTED International Conference On Financial Engineering and Applications |
| Pages | 191-196 |
| Number of pages | 6 |
| Publication status | Published - 1 Dec 2004 |
| Externally published | Yes |
| Event | Proceedings of the Second IASTED International Conference on Financial Engineering and Applications - Cambridge, MA, United States Duration: 8 Nov 2004 → 10 Nov 2004 |
Conference
| Conference | Proceedings of the Second IASTED International Conference on Financial Engineering and Applications |
|---|---|
| Country/Territory | United States |
| City | Cambridge, MA |
| Period | 8/11/04 → 10/11/04 |
Keywords
- Eigen-analysis
- High frequency time series
- Intraday analysis
- Low frequency time series
ASJC Scopus subject areas
- General Engineering