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Backtesting

32 Pages Posted: 27 Oct 2013 Last revised: 30 Jul 2015

Campbell R. Harvey

Duke University - Fuqua School of Business; National Bureau of Economic Research (NBER); Duke Innovation & Entrepreneurship Initiative

Yan Liu

Texas A&M University, Department of Finance

Date Written: July 28, 2015

Abstract

When evaluating a trading strategy, it is routine to discount the Sharpe ratio from a historical backtest. The reason is simple: there is inevitable data mining by both the researcher and by other researchers in the past. Our paper provides a statistical framework that systematically accounts for these multiple tests. We propose a method to determine the appropriate haircut for any given reported Sharpe ratio. We also provide a profit hurdle that any strategy needs to achieve in order to be deemed "significant".

Notes: Related papers: Multiple Testing in Economics and …and the Cross-Section of Expected Returns

Keywords: Sharpe ratio, Multiple tests, Backtest, Haircut, Trading Strategies, Out-of-Sample tests, In-Sample tests

JEL Classification: G12, G14, G30, G00, C12, C20, B41

Suggested Citation

Harvey, Campbell R. and Liu, Yan, Backtesting (July 28, 2015). Available at SSRN: https://ssrn.com/abstract=2345489 or http://dx.doi.org/10.2139/ssrn.2345489

Campbell Harvey (Contact Author)

Duke University - Fuqua School of Business ( email )

Box 90120
Durham, NC 27708-0120
United States
919-660-7768 (Phone)
919-660-8030 (Fax)

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Duke Innovation & Entrepreneurship Initiative ( email )

215 Morris St., Suite 300
Durham, NC 27701
United States

Yan Liu

Texas A&M University, Department of Finance ( email )

Wehner 401Q, MS 4353
College Station, TX 77843-4218
United States

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