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Journal articles on the topic 'Sharpe ratio'

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Published: 4 June 2021
Last updated: 25 April 2022

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1

Sharpe, William F. "The Sharpe Ratio." Journal of Portfolio Management 21, no. 1 (October 31, 1994): 49–58. http://dx.doi.org/10.3905/jpm.1994.409501.

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2

Hung, Mao-Wei, and Yin-Ching Jan. "Sharpe Timing Ratio." Journal of Investing 14, no. 4 (November 30, 2005): 75–79. http://dx.doi.org/10.3905/joi.2005.605285.

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3

Mukherjee, Debasri, and Aman Ullah. "Nonparametric Sharpe Ratio." Journal of Quantitative Economics 2, no. 2 (July 2004): 172–85. http://dx.doi.org/10.1007/bf03404616.

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4

Wong, W. K., J. A. Wright, S. C. P. Yam, and S. P. Yung. "A mixed Sharpe ratio." Risk and Decision Analysis 3, no. 1-2 (2012): 37–65. http://dx.doi.org/10.3233/rda-2012-0051.

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5

Agarwal, Ankush, and Matthew Lorig. "The implied Sharpe ratio." Quantitative Finance 20, no. 6 (February 19, 2020): 1009–26. http://dx.doi.org/10.1080/14697688.2020.1718194.

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6

Bailey, David, and Marcos López de Prado. "The Sharpe ratio efficient frontier." Journal of Risk 15, no. 2 (December 2012): 3–44. http://dx.doi.org/10.21314/jor.2012.255.

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7

Baweja, Meena, Ratnesh R. Saxena, and Deepak Sehgal. "Portfolio Optimization Using Conditional Sharpe Ratio." International Letters of Chemistry, Physics and Astronomy 53 (July 2015): 130–36. http://dx.doi.org/10.18052/www.scipress.com/ilcpa.53.130.

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In this paper we propose a portfolio optimization model that selects the portfolio with the largest worse-case-scenario sharpe ratio with a given confidence level. We highlight the relationship between conditional value-atrisk based sharpe ratio and standard deviation based sharpe ratio proposed in literature. By utilizing the results of Rockafellar and Uryasev [5], we evaluate conditional value- at- risk for each portfolio. Our model is expected to enlarge the application area of practical investment problems for which the original sharpe ratio is not suitable, however should device effective computational methods to solve optimal portfolio selection problems with large number of investment opportunities. Here conditional sharpe ratio is defined as the ratio of expected excess return to the expected shortfall. This optimization considers both risk and return, of which changes will effect the sharpe ratio. That is the fitness function for dynamic portfolio is the objective function of the model.
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8

Israelsen, Craig. "A refinement to the Sharpe ratio and information ratio." Journal of Asset Management 5, no. 6 (April 2005): 423–27. http://dx.doi.org/10.1057/palgrave.jam.2240158.

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9

Farinelli, Simone, Manuel Ferreira, Damiano Rossello, Markus Thoeny, and Luisa Tibiletti. "Beyond Sharpe ratio: Optimal asset allocation using different performance ratios." Journal of Banking & Finance 32, no. 10 (October 2008): 2057–63. http://dx.doi.org/10.1016/j.jbankfin.2007.12.026.

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10

Peake, Charles F. "The Symmetric Downside-Risk Sharpe Ratio." CFA Digest 36, no. 2 (May 2006): 83–85. http://dx.doi.org/10.2469/dig.v36.n2.4123.

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11

Ziemba, William T. "The Symmetric Downside-Risk Sharpe Ratio." Journal of Portfolio Management 32, no. 1 (October 31, 2005): 108–22. http://dx.doi.org/10.3905/jpm.2005.599515.

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12

Bednarek, Ziemowit, Pratish Patel, and Cyrus A. Ramezani. "Time aggregation of the Sharpe ratio." Journal of Asset Management 17, no. 7 (July 8, 2016): 540–55. http://dx.doi.org/10.1057/s41260-016-0003-x.

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13

Spurgin, Richard B. "How to Game Your Sharpe Ratio." Journal of Alternative Investments 4, no. 3 (December 31, 2001): 38–46. http://dx.doi.org/10.3905/jai.2001.319019.

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14

Tajdini, Saeid, Mohsen Mehrara, Reza Tehrani, and David McMillan. "Double-sided balanced conditional Sharpe ratio." Cogent Economics & Finance 7, no. 1 (January 1, 2019): 1630931. http://dx.doi.org/10.1080/23322039.2019.1630931.

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15

Prado-Dominguez, Javier, and Carlos Fernández-Herráiz. "A Sharpe-ratio-based measure for currencies." European Journal of Government and Economics 4, no. 1 (June 29, 2015): 67. http://dx.doi.org/10.17979/ejge.2015.4.1.4307.

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The Sharpe Ratio offers an excellent summary of the excess return required per unit of risk invested. This work presents an adaptation of the ex-ante Sharpe Ratio for currencies where we consider a random walk approach for the currency behavior and implied volatility as a proxy for market expectations of future realized volatility. The outcome of the proposed measure seems to gauge some information on the expected required return attached to the “peso problem”.
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16

Kalra, Rajiv. "Adjusting for Risk: An Improved Sharpe Ratio." CFA Digest 31, no. 2 (May 2001): 74–76. http://dx.doi.org/10.2469/dig.v31.n2.881.

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17

Kourtis, Apostolos. "The Sharpe ratio of estimated efficient portfolios." Finance Research Letters 17 (May 2016): 72–78. http://dx.doi.org/10.1016/j.frl.2016.01.009.

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18

Unhapipat, Suntaree, Jun-Yu Chen, and Nabendu Pal. "Small Sample Inferences on the Sharpe Ratio." American Journal of Mathematical and Management Sciences 35, no. 2 (January 28, 2016): 105–23. http://dx.doi.org/10.1080/01966324.2015.1121847.

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19

Choey, Mark, and Andreas S. Weigend. "Nonlinear Trading Models Through Sharpe Ratio Maximization." International Journal of Neural Systems 08, no. 04 (August 1997): 417–31. http://dx.doi.org/10.1142/s0129065797000410.

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While many trading strategies are based on price prediction, traders in financial markets are typically interested in optimizing risk-adjusted performance such as the Sharpe Ratio, rather than the price predictions themselves. This paper introduces an approach which generates a nonlinear strategy that explicitly maximizes the Sharpe Ratio. It is expressed as a neural network model whose output is the position size between a risky and a risk-free asset. The iterative parameter update rules are derived and compared to alternative approaches. The resulting trading strategy is evaluated and analyzed on both computer-generated data and real world data (DAX, the daily German equity index). Trading based on Sharpe Ratio maximization compares favorably to both profit optimization and probability matching (through cross-entropy optimization). The results show that the goal of optimizing out-of-sample risk-adjusted profit can indeed be achieved with this nonlinear approach.
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20

Schuster, Martin, and Benjamin R. Auer. "A note on empirical Sharpe ratio dynamics." Economics Letters 116, no. 1 (July 2012): 124–28. http://dx.doi.org/10.1016/j.econlet.2012.02.005.

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21

Kaplanski, Guy, Haim Levy, Chris Veld, and Yulia Veld-Merkoulova. "Past returns and the perceived Sharpe ratio." Journal of Economic Behavior & Organization 123 (March 2016): 149–67. http://dx.doi.org/10.1016/j.jebo.2015.11.010.

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22

Scholz, Hendrik, and Marco Wilkens. "Zur Relevanz von Sharpe Ratio und Treynor Ratio: Ein investorspezifisches Performancemaß." Zeitschrift für Bankrecht und Bankwirtschaft 15, no. 1 (January 1, 2003): 1–8. http://dx.doi.org/10.15375/zbb-2003-0101.

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23

Nielsen, Lars Tyge, and Maria Vassalou. "Sharpe Ratios and Alphas in Continuous Time." Journal of Financial and Quantitative Analysis 39, no. 1 (March 2004): 103–14. http://dx.doi.org/10.1017/s0022109000003902.

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AbstractThis paper proposes modified versions of the Sharpe ratio and Jensen's alpha, which are appropriate in a simple continuous-time model. Both are derived from optimal portfolio selection. The modified Sharpe ratio equals the ordinary Sharpe ratio plus half of the volatility of the fund. The modified alpha also differs from the ordinary alpha by a second-moment adjustment. The modified and the ordinary Sharpe ratios may rank funds differently. In particular, if two funds have the same ordinary Sharpe ratio, then the one with the higher volatility will rank higher according to the modified Sharpe ratio. This is justified by the underlying dynamic portfolio theory. Unlike their discrete-time versions, the continuous-time performance measures take into account that it is optimal for investors to change the fractions of their wealth held in the fund vs. the riskless asset over time.
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24

CHRISTENSEN, MORTEN MOSEGAARD, and ECKHARD PLATEN. "SHARPE RATIO MAXIMIZATION AND EXPECTED UTILITY WHEN ASSET PRICES HAVE JUMPS." International Journal of Theoretical and Applied Finance 10, no. 08 (December 2007): 1339–64. http://dx.doi.org/10.1142/s0219024907004688.

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We analyze portfolio strategies which are locally optimal, meaning that they maximize the Sharpe ratio in a general continuous time jump-diffusion framework. These portfolios are characterized explicitly and compared to utility based strategies. We show that in the presence of jumps, maximizing the Sharpe ratio is generally inconsistent with maximizing expected utility, in the sense that a utility maximizing individual will not choose a strategy which has a maximal Sharpe ratio. This result will hold unless markets are incomplete or jump risk has no risk premium. In case of an incomplete market we show that the optimal portfolio of a utility maximizing individual may "accidentally" have maximal Sharpe ratio. Furthermore, if there is no risk premium for jump risk, a utility maximizing investor may select a portfolio having a maximal Sharpe ratio, if jump risk can be hedged away. We note that uncritical use of the Sharpe ratio as a performance measure in a world where asset prices exhibit jumps may lead to unreasonable investments with positive probability of ruin.
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25

Maller, Ross. "Bias and consistency of the maximum Sharpe ratio." Journal of Risk 7, no. 4 (June 2005): 103–15. http://dx.doi.org/10.21314/jor.2005.117.

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26

Maller, Ross, Robert Durand, and Hediah Jafarpour. "Optimal portfolio choice using the maximum Sharpe ratio." Journal of Risk 12, no. 4 (June 2010): 49–73. http://dx.doi.org/10.21314/jor.2010.212.

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27

Best, Ronald W., Charles W. Hodges, and James A. Yoder. "The Sharpe Ratio and Long-Run Investment Decisions." Journal of Investing 16, no. 2 (May 31, 2007): 70–76. http://dx.doi.org/10.3905/joi.2007.686413.

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28

Auer, Benjamin R. "The low return distortion of the Sharpe ratio." Financial Markets and Portfolio Management 27, no. 3 (July 9, 2013): 299–306. http://dx.doi.org/10.1007/s11408-013-0213-x.

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29

Lettau, Martin, and Harald Uhlig. "THE SHARPE RATIO AND PREFERENCES: A PARAMETRIC APPROACH." Macroeconomic Dynamics 6, no. 2 (April 2002): 242–65. http://dx.doi.org/10.1017/s1365100502031036.

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We use a log-normal framework to examine the effect of preferences on the market price for risk, that is, the Sharpe ratio. In our framework, the Sharpe ratio can be calculated directly from the elasticity of the stochastic discount factor with respect to consumption innovations as well as the volatility of consumption innovations. This can be understood as an analytical shortcut to the calculation of the Hansen–Jagannathan volatility bounds, and therefore provides a convenient tool for theorists searching for models capable of explaining asset-pricing facts. To illustrate the usefulness of our approach, we examine several popular preference specifications, such as CRRA, various types of habit formation, and the recursive preferences of Epstein–Zin–Weil. Furthermore, we show how the models with idiosyncratic consumption shocks can be studied.
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30

Durand, Robert B., Hedieh Jafarpour, Claudia Klüppelberg, and Ross Maller. "Maximize the Sharpe Ratio and Minimize a VaR." Journal of Wealth Management 13, no. 1 (April 30, 2010): 91–102. http://dx.doi.org/10.3905/jwm.2010.13.1.091.

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31

Frahm, Gabriel. "An Intersection–Union Test for the Sharpe Ratio." Risks 6, no. 2 (April 19, 2018): 40. http://dx.doi.org/10.3390/risks6020040.

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32

Guerreiro, Andreia P., and Carlos M. Fonseca. "An analysis of the Hypervolume Sharpe-Ratio Indicator." European Journal of Operational Research 283, no. 2 (June 2020): 614–29. http://dx.doi.org/10.1016/j.ejor.2019.11.023.

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33

Ledoit, Oliver, and Michael Wolf. "Robust performance hypothesis testing with the Sharpe ratio." Journal of Empirical Finance 15, no. 5 (December 2008): 850–59. http://dx.doi.org/10.1016/j.jempfin.2008.03.002.

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34

Van Dyk, Francois, Gary Van Vuuren, and Andre Heymans. "The Bias Ratio As A Hedge Fund Fraud Indicator: An Empirical Performance Study Under Different Economic Conditions." International Business & Economics Research Journal (IBER) 13, no. 4 (June 30, 2014): 867. http://dx.doi.org/10.19030/iber.v13i4.8698.

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The Sharpe ratio is widely used as a performance evaluation measure for traditional (i.e., long only) investment funds as well as less-conventional funds such as hedge funds. Based on mean-variance theory, the Sharpe ratio only considers the first two moments of return distributions, so hedge funds characterised by complex, asymmetric, highly-skewed returns with non-negligible higher moments may be misdiagnosed in terms of performance. The Sharpe ratio is also susceptible to manipulation and estimation error. These drawbacks have demonstrated the need for augmented measures, or, in some cases, replacement fund performance metrics. Over the period January 2000 to December 2011 the monthly returns of 184 international long/short (equity) hedge funds with investment mandates that span the geographical areas of North America, Europe, and Asia were examined. This study compares results obtained using the Sharpe ratio (in which returns are assumed to be serially uncorrelated) with those obtained using a technique which does account for serial return correlation. Standard techniques for annualising Sharpe ratios, based on monthly estimators, do not account for serial return correlation this study compares Sharpe ratio results obtained using a technique which accounts for serial return correlation. In addition, this study assess whether the Bias ratio supplements the Sharpe ratio in the evaluation of hedge fund risk and thus in the investment decision-making process. The Bias and Sharpe ratios were estimated on a rolling basis to ascertain whether the Bias ratio does indeed provide useful additional information to investors to that provided solely by the Sharpe ratio.
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35

Barillas, Francisco, Raymond Kan, Cesare Robotti, and Jay Shanken. "Model Comparison with Sharpe Ratios." Journal of Financial and Quantitative Analysis 55, no. 6 (August 9, 2019): 1840–74. http://dx.doi.org/10.1017/s0022109019000589.

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We show how to conduct asymptotically valid tests of model comparison when the extent of model mispricing is gauged by the squared Sharpe ratio improvement measure. This is equivalent to ranking models on their maximum Sharpe ratios, effectively extending the Gibbons, Ross, and Shanken (1989) test to accommodate the comparison of nonnested models. Mimicking portfolios can be substituted for any nontraded model factors, and estimation error in the portfolio weights is taken into account in the statistical inference. A variant of the Fama and French (2018) 6-factor model, with a monthly updated version of the usual value spread, emerges as the dominant model.
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36

Van Dyk, Francois, Gary Van Vuuren, and Andre Heymans. "Hedge Fund Performance Evaluation Using The Sharpe And Omega Ratios." International Business & Economics Research Journal (IBER) 13, no. 3 (April 28, 2014): 485. http://dx.doi.org/10.19030/iber.v13i3.8588.

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The Sharpe ratio is widely used as a performance evaluation measure for traditional (i.e., long only) investment funds as well as less-conventional funds such as hedge funds. Based on mean-variance theory, the Sharpe ratio only considers the first two moments of return distributions, so hedge funds characterised by asymmetric, highly-skewed returns with non-negligible higher moments may be misdiagnosed in terms of performance. The Sharpe ratio is also susceptible to manipulation and estimation error. These drawbacks have demonstrated the need for augmented measures, or, in some cases, replacement fund performance metrics. Over the period January 2000 to December 2011 the monthly returns of 184 international long/short (equity) hedge funds with geographical investment mandates spanning North America, Europe, and Asia were examined. This study compares results obtained using the Sharpe ratio (in which returns are assumed to be serially uncorrelated) with those obtained using a technique which does account for serial return correlation. Standard techniques for annualising Sharpe ratios, based on monthly estimators, do not account for this effect. In addition, this study assesses whether the Omega ratio supplements the Sharpe Ratio in the evaluation of hedge fund risk and thus in the investment decision-making process. The Omega and Sharpe ratios were estimated on a rolling basis to ascertain whether the Omega ratio does indeed provide useful additional information to investors to that provided by the Sharpe ratio alone.
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37

Tang, Yi, and Robert F. Whitelaw. "Time-Varying Sharpe Ratios and Market Timing." Quarterly Journal of Finance 01, no. 03 (September 2011): 465–93. http://dx.doi.org/10.1142/s2010139211000122.

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This paper documents predictable time-variation in stock market Sharpe ratios. Predetermined financial variables are used to estimate both the conditional mean and volatility of equity returns, and these moments are combined to estimate the conditional Sharpe ratio, or the Sharpe ratio is estimated directly as a linear function of these same variables. In sample, estimated conditional Sharpe ratios show substantial time-variation that coincides with the phases of the business cycle. Generally, Sharpe ratios are low at the peak of the cycle and high at the trough. In an out-of-sample analysis, using 10-year rolling regressions, relatively naive market-timing strategies that exploit this predictability can identify periods with Sharpe ratios more than 45% larger than the full sample value. In spite of the well-known predictability of volatility and the more controversial forecastability of returns, it is the latter factor that accounts primarily for both the in-sample and out-of-sample results.
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38

Gunning, Wade, and Gary van Vuuren. "Optimal omega-ratio portfolio performance constrained by tracking error." Investment Management and Financial Innovations 17, no. 3 (September 29, 2020): 263–80. http://dx.doi.org/10.21511/imfi.17(3).2020.20.

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The mean-variance framework coupled with the Sharpe ratio identifies optimal portfolios under the passive investment style. Optimal portfolio identification under active investment approaches, where performance is measured relative to a benchmark, is less well-known. Active portfolios subject to tracking error (TE) constraints lie on distorted elliptical frontiers in return/risk space. Identifying optimal active portfolios, however defined, have only recently begun to be explored. The Ω – ratio considers both down and upside portfolio potential. Recent work has established a technique to determine optimal Ω – ratio portfolios under the passive investment approach. The authors apply the identification of optimal Ω – ratio portfolios to the active arena (i.e., to portfolios constrained by a TE) and find that while passive managers should always invest in maximum Ω – ratio portfolios, active managers should first establish market conditions (which determine the sign of the main axis slope of the constant TE frontier). Maximum Sharpe ratio portfolios should be engaged when this slope is > 0 and maximum Ω – ratios when < 0.
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39

Abbas Zaidi, Syed Zakir. "Performance Appraisal of Open-ended Equity Funds in Pakistan: An alternative Approaches of Performance Measure." Jinnah Business Review 8, no. 1 (January 1, 2020): 18–40. http://dx.doi.org/10.53369/vogg5707.

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There are more than one hundred portfolio performances, which have been proposed in financial literature, (Cogneau and Hubner, 2009), but extensively used performance measure is a Sharpe ratio and in Pakistan Asset Management Companies (AMCs) also prefer to exhibit their performance in Sharpe ratio. However, financial literature has ample of evidence that recommend Sharpe ratio is valid under normal distribution of returns. The financial returns are not distributed normally as result of which standard deviation may not adequately measure risk (Bodie et al., 2009). Whereas, standard deviation of negatively skewed distribution underestimates and positively skewed overestimates volatility that would be misleading Sharpe index. In this study, we concluded that for skewed and non-normal distribution Omega ratio or Sharpe-Omega are alternative performance measures.
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40

Azmi, Zulfiyah, and Bayu Arie Fianto. "PENGUKURAN KINERJA REKSA DANA PADA REKSA DANA SYARIAH DAN REKSA DANA KONVENSIONAL DI INDONESIA PERIODE 2008 – 2018." Jurnal Ekonomi Syariah Teori dan Terapan 6, no. 9 (January 17, 2020): 1851. http://dx.doi.org/10.20473/vol6iss20199pp1851-1861.

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This research measured and compared the performance between Islamic mutual funds and conventional mutual funds using Sharpe Ratio, Treynor Index, Jensen Alpha, Modigliani Measure, Appraisal Ratio, and Adjusted Sharpe Ratio. This research used quantitative approach with panel data that was measured by using different test and it aimed to find out the comparation of the samples. This research used Net Asset Value (NAV), Joint Stock Price Index, BI Rate to find out return and risk that will be implemented on the measured methods. The results of the research based on T-test are that there is no significant difference of performance between Islamic mutual funds and conventional mutual funds, except the Appraisal Ratio method that shows the difference on Islamic mutual funds that has a better performance.Keywords: Sharpe Ratio, Treynor Index, Jensen Alpha, Modigliani Measure, Appraisal Ratio, Adjusted Sharpe Ratio
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41

Hodges, Charles W., Walton R. L. Taylor, and James A. Yoder. "Stocks, Bonds, the Sharpe Ratio, and the Investment Horizon." Financial Analysts Journal 53, no. 6 (November 1997): 74–80. http://dx.doi.org/10.2469/faj.v53.n6.2132.

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42

McLeod, W., and G. van Vuuren. "Interpreting the Sharpe ratio when excess returns are negative." Investment Analysts Journal 33, no. 59 (January 2004): 15–20. http://dx.doi.org/10.1080/10293523.2004.11082455.

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43

Bednarek, Ziemowit, and Pratish Patel. "Effect of Booms and Busts on the Sharpe Ratio." Journal of Portfolio Management 43, no. 2 (January 31, 2017): 105–14. http://dx.doi.org/10.3905/jpm.2017.43.2.105.

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44

Jacobsen, Brian, and Wai Lee. "Risk-Parity Optimality Even with Negative Sharpe Ratio Assets." Journal of Portfolio Management 46, no. 6 (March 20, 2020): 110–19. http://dx.doi.org/10.3905/jpm.2020.1.151.

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45

Agarwal, Ankush, and Ronnie Sircar. "Portfolio Benchmarking Under Drawdown Constraint and Stochastic Sharpe Ratio." SIAM Journal on Financial Mathematics 9, no. 2 (January 2018): 435–64. http://dx.doi.org/10.1137/16m1100861.

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46

Liu, Ying, Marie Rekkas, and Augustine Wong. "Inference for the Sharpe Ratio Using a Likelihood-Based Approach." Journal of Probability and Statistics 2012 (2012): 1–24. http://dx.doi.org/10.1155/2012/878561.

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The Sharpe ratio is the prominent risk-adjusted performance measure used by practitioners. Statistical testing of this ratio using its asymptotic distribution has lagged behind its use. In this paper, highly accurate likelihood analysis is applied for inference on the Sharpe ratio. Both the one- and two-sample problems are considered. The methodology hasO(n−3/2)distributional accuracy and can be implemented using any parametric return distribution structure. Simulations are provided to demonstrate the method's superior accuracy over existing methods used for testing in the literature.
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47

Alvi, Jahanzaib, Muhammad Rehan, and Sania Saeed. "Modified Sharpe Ratio Application in Calculation of Mutual Fund Star Ranking." Global Journal of Business, Economics and Management: Current Issues 10, no. 1 (March 30, 2020): 58–82. http://dx.doi.org/10.18844/gjbem.v10i1.4714.

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Purpose of this study is to apply to modify Sharpe Ratio to calculate Star Ranking of Equity-based mutual funds registered in Mutual Fund Association of Pakistan, further, the idea was to recalibrate locally developed models being used in Pakistan by autonomous professional bodies who professionally assigns star ranking of mutual funds, equity market exhibited negative returns from July 2017 onwards this research which brought the problem to assign star ranking due to model structure, model relies on risk-adjusted return (Sharpe Ratio), therefore Sharpe Ratio has a limitation in negative excess return. Two developed models were simultaneously compared to witness the predictive power of these models, (1) modified Sharpe and (2) VIS Credit Rating Company (Explaining the Stars) Model. Data was collected from March 2013 to March 2018 quarterly and the exercise was done quarterly. Findings revealed a magnificent piece of work, (1) there is no difference between model 1 and model 2 by both way results exhibited same mutual fund star rankings, (2) both methods have a different way of calculating final score with same results, and (3) modified Sharpe ratio is quite well when excess return is negative but when there is a mix of negative and positive better to use VIS model as well as in positive excess returns. A research paper could not calibrate other models developed by rating companies (Pakistan Credit Rating Company) which is a future research gap.
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48

Qudratullah, Mohammad Farhan. "Measuring Islamic Stock Performance in Indonesia with A Modified Sharpe Ratio." Share: Jurnal Ekonomi dan Keuangan Islam 10, no. 2 (December 31, 2021): 155. http://dx.doi.org/10.22373/share.v10i2.10493.

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Since the late 1960s, one of the stock performance analysis tools commonly used is Sharpe Ratio. The Sharpe Ratio consists of three components, namely stock return, risk-free returns, and stock risk. Many studies approach risk-free returns with interest rates, including when measuring the performance of Islamic stocks, while interest rates are prohibited in the concept of Islamic finance. Moreover, the stock risk is measured by a standard deviation which assumes returns are normally distributed, while many stock returns are non-normally distributed. This paper intends to measure the performance of Islamic stocks listed on the Indonesian Stock Exchange (IDX) for the period of January 2011 to July 2018 using a modified Sharpe Ratio. The ratio is modified by replacing the interest rate with four approaches: eliminating the interest rate, changing with zakah rates, changing with inflation, changing with the nominal gross domestic product, and replacing the risk measurement from Standard Deviation to Value at Risk (VaR). The findings provide almost the same results as the original measurement and thus, show very high suitability for using these models in other circumstances. Therefore, on the concept of Islamic finance, risk-free returns can be measured using these four approaches, especially inflation and GDP. This study also recommends inflation and GDP to measure risk-free returns in the Sharia's Compliant Asset Pricing Model (SCAPM) or Islamic Capital Asset Pricing Model (ICAPM).====================================================================================================ABSTRAK – Pengukuran Kinerja Saham Syariah di Indonesia menggunakan Sharpe Ratio Modifikasi. Sejak akhir 1960-an, salah satu alat mengukur kinerja saham yang biasa digunakan adalah Sharpe Ratio. Model Sharpe Ratio terdiri atas tiga komponen, yaitu return saham, return bebas risiko, dan risiko saham. Return bebas risiko diukur mengunakan variabel suku bunga yang digolongkan riba dan dilarang dalam konsep keuangan islam. Sedangkan risiko saham diukur dengan standar deviasi yang mengasumsikan data berdistribusi normal. Paper ini bertujuan untuk mengukur kinerja saham syariah yang terdaftar pada Bursa Efek Indonesia (BEI) untuk periode Januari 2011 sampai Juli 2018 dengan menggunakan Sharpe Ratio modifikasi. Kajian akan memodifikasi model Sharpe Ratio dengan mencari variabel alternatif penganti suku bunga dengan empat pendekatan, yaitu: menghilangkan variabel suku bunga tersebut, mengganti dengan zakat rate, mengganti dengan inflasi, dan mengganti dengan produk domestik bruto, serta mengganti standar deviasi dengan Value at Risk (VaR) sebagai pengukur risiko saham yang selanjutnya diimplementasikan pada pasar modal syariah di Indonesia periode Januari 2011 - Juli 2018. Hasil kajian menunjukkan kesesuaian yang sangat tinggi untuk hasil pengukuran kelima model tersebut. Dilihat dari kedekatan hasil pengukuran kinerja, kelima model tersebut dapat dikelompokkan menjadi dua, yaitu model dengan tingkat suku bunga, inflasi, dan PDB sebagai kelompok pertama, sedangkan model tanpa suku bunga dan tingkat zakat sebagai kelompok kedua
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49

Van Dyk, Francois, Gary Van Vuuren, and Andre Heymans. "Hedge Fund Performance Using Scaled Sharpe And Treynor Measures." International Business & Economics Research Journal (IBER) 13, no. 6 (October 31, 2014): 1261. http://dx.doi.org/10.19030/iber.v13i6.8920.

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The Sharpe ratio is widely used as a performance measure for traditional (i.e., long only) investment funds, but because it is based on mean-variance theory, it only considers the first two moments of a return distribution. It is, therefore, not suited for evaluating funds characterised by complex, asymmetric, highly-skewed return distributions such as hedge funds. It is also susceptible to manipulation and estimation error. These drawbacks have demonstrated the need for new and additional fund performance metrics. The monthly returns of 184 international long/short (equity) hedge funds from four geographical investment mandates were examined over an 11-year period.This study contributes to recent research on alternative performance measures to the Sharpe ratio and specifically assesses whether a scaled-version of the classic Sharpe ratio should augment the use of the Sharpe ratio when evaluating hedge fund risk and in the investment decision-making process. A scaled Treynor ratio is also compared to the traditional Treynor ratio. The classic and scaled versions of the Sharpe and Treynor ratios were estimated on a 36-month rolling basis to ascertain whether the scaled ratios do indeed provide useful additional information to investors to that provided solely by the classic, non-scaled ratios.
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50

Zakamulin, Valeriy. "Sharpe (Ratio) Thinking about the Investment Opportunity Set and CAPM Relationship." Economics Research International 2011 (July 12, 2011): 1–9. http://dx.doi.org/10.1155/2011/781760.

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In the presence of a risk-free asset the investment opportunity set obtained via the Markowitz portfolio optimization procedure is usually characterized in terms of the vector of excess returns on individual risky assets and the variance-covariance matrix. We show that the investment opportunity set can alternatively be characterized in terms of the vector of Sharpe ratios of individual risky assets and the correlation matrix. This implies that the changes in the characteristics of individual risky assets that preserve the Sharpe ratios and the correlation matrix do not change the investment opportunity set. The alternative characterization makes it simple to perform a comparative static analysis that provides an answer to the question of what happens with the investment opportunity set when we change the risk-return characteristics of individual risky assets. We demonstrate the advantages of using the alternative characterization of the investment opportunity set in the investment practice. The Sharpe ratio thinking also motivates reconsidering the CAPM relationship and adjusting Jensen's alpha in order to properly measure abnormal portfolio performance.
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