Optimal filter rules for selling stocks in the emerging stock markets

Sabri Boubaker, Xuyuan Han, Zhenya Liu and Yaosong Zhan
Additional contact information
Zhenya Liu: CERGAM - Centre d'Études et de Recherche en Gestion d'Aix-Marseille - AMU - Aix Marseille Université - UTLN - Université de Toulon, Renmin University of China = Université Renmin de Chine
Yaosong Zhan: Renmin University of China = Université Renmin de Chine

Post-Print from HAL

Abstract: With the application of the optimal stopping techniques, this paper proposes a filter rule for investors in emerging stock markets. In a bull market, once the stock price falls down to the optimal filter size, investors should sell the stock to avoid massive losses. We show that the optimal filter size is a function of the historical highest price, the weights of the future returns and the current drawdown in the investor's utility function, the characteristics of the underlying stochastic price process, and the discount rate. Out-of-sample tests verify that this filter rule is valid, and the selling signals generated by the filter rule are at the beginning of the downtrend in the most emerging stock markets.

Keywords: Finance; Filter rule; Optimal stopping; Emerging stock markets (search for similar items in EconPapers)
Date: 2023-11-01
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Published in Annals of Operations Research, 2023, 330, pp.211-242. ⟨10.1007/s10479-021-04381-w⟩

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
Journal Article: Optimal filter rules for selling stocks in the emerging stock markets (2023)
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:hal:journl:hal-03511438

DOI: 10.1007/s10479-021-04381-w

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().