Algorithms comparison on intraday index return prediction：evidence from China

Xiang Li, Xianghui Yuan, Jin Yuan and Hailun Xu

Applied Economics Letters, 2021, vol. 28, issue 12, 995-999

Abstract: We introduce the fading memory recursive least squares (FM-RLS) and rolling window ordinary least squares (RW-OLS) methods to predict CSI 300 intraday index return in Chinese stock market. Empirical results show that the performances are better than that of same sign method. The additional profit is mainly from two conflict signals, with one amplitude far greater than the other.

Date: 2021
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1080/13504851.2020.1791793 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:apeclt:v:28:y:2021:i:12:p:995-999

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RAEL20

DOI: 10.1080/13504851.2020.1791793

Access Statistics for this article

Applied Economics Letters is currently edited by Anita Phillips

More articles in Applied Economics Letters from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().