The Statistics of Sharpe Ratios

By Andrew W. Lo. 2002

760 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

In Finance

Citations in the corpus

Financial Time Series Prediction Using Deep Learning (2017, methodology): “Figure 11 shows the annual Sharpe Ratio [34] versus the commissions for different assets using the proposed approach.”
Comparing Sharpe ratios: So where are the p-values? (2007, methodology): “This is the definition of the Sharpe ratio as presented by Jobson and Korkie (1981), Sharpe (1994), Memmel (2003), and others. An arguably more widely used version is presented in Lo (2002) and Christie (2005) as: μ σ − = S f S S R SR (and for the”
High frequency trading and the new market makers (2013, background): “The Lo (2002) correction for first-order autocorrelation appears”
An Economic Evaluation of Empirical Exchange Rate Models (2009, methodology): “Speci cally, following Lo (2002), we multiply the monthly Sharpe ratios by the adjustment factor 12p 12+2 P11 k=1 (12 k) k , where k is the autocorrelation coe¢ cient of portfolio returns at lag k.”
Can Hedge-Fund Returns Be Replicated?: The Linear Case (2006, neutral): “(1973) and Ross (1976). We believe that hedge funds are practical illustrations of these multi-factor models of expected returns.”
Does the Measure Matter in the Mutual Fund Industry? (2008, background): “• It provides a convenient summary of two important aspects (risk and return) of any investment strategy (Sharpe 1994) and is probably the best understood performance measure (Lo 2002).”
The Road Less Traveled: Strategy Distinctiveness and Hedge Fund Performance (2012, neutral): “We also consider the smoothingadjusted Sharpe ratio to control for illiquidity and smoothing in hedge fund returns, following Getmansky, Lo, and Makarov (2004)(5).”
On Testing the Equality of the Multiple Sharpe Ratios, with Application on the Evaluation of Ishares (2008, methodology): “On the other hand, Lo (2002) derives a statistical distribution of the Sharpe ratio by using the standard econometric methods with several different sets of assumptions on the statistical behavior of the return series.”
Robust Performance Hypothesis Testing with the Sharpe Ratio (2008, positive): “To this end, we suggest to use a calibration method, a concept dating back to Loh (1987). One can think of the actual coverage level 1−λ of a block bootstrap confidence interval as a function of the block size b, conditional on the underlying probability mechanism P which generated the bivariate tim”
The Sharpe Ratio Efficient Frontier (2012, neutral): “Following Lo (2002), in this section we will derive what this distribution is in the case of IID Normal returns.”
Econometric Measures of Systemic Risk in the Finance and Insurance Sectors (2010, background): “Lo (2002) and Getmansky, Lo, and Makarov (2004) suggest using return autocorrelations to gauge the illiquidity risk exposure, hence we report asset-weighted autocorrelations in Table 5.”
Ensembles of Text and Time-Series Models for Automatic Generation of Financial Trading Signals from Social Media Content (2018, background): “Overstated Sharpe ratios stem from the presence of serial correlation in returns [10].”
Sharpe Ratios and Alphas in Continuous Time (2004, neutral): “Moreover, under the joint assumption of constant volatility and a constant instantaneous Sharpe ratio, the excess returns are i.i.d., and the standard estimation theory for the Sharpe ratio derived by Lo (2002) applies.”
Portfolio Performance Manipulation and Manipulation-Proof Performance Measures (2007, neutral): “The Henriksson-Merton (HM) and Treynor-Mazuy (TM) measures are based on the regression x̃t = γ 0 + γ 1m̃t + γ 2w̃t + ε̃t (D1) where x̃t and m̃t are the excess returns on the portfolio and the market and w̃t is a variable used to capture timing ability.”