On the backtesting of trading strategies

Yen Lok ()

2018 Papers from Job Market Papers

Abstract: The contribution of this paper is two-fold. The first contribution is the development of a filter-combine scheme for trading strategies to diversify model risk. Multiple statistical machine learning models are used to predict the price direction of multiple assets. We demonstrate the effectiveness of model-averaging after under-performing models are removed via a filtering algorithm. The second contribution is the identification of appropriate measures of performance for selecting models. In the literature, different measures are usually designed for different applications and purposes, and it is not always clear as to whether certain measures are relevant to a particular trading strategy. By identifying relevant measures, one can identify the key drivers underlying well-performing models, and allocate more resources in optimising and improving the appropriate models.

JEL-codes: C51 C52 (search for similar items in EconPapers)
Date: 2018-06-22
New Economics Papers: this item is included in nep-big, nep-cmp, nep-for and nep-knm
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://ideas.repec.org/jmp/2018/plo493.pdf

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:jmp:jm2018:plo493

Access Statistics for this paper

More papers in 2018 Papers from Job Market Papers
Bibliographic data for series maintained by RePEc Team ().