Feature Why Hedge Funds Make Sense

Total Page:16

File Type:pdf, Size:1020Kb

Feature Why Hedge Funds Make Sense Q UANTITATIVE S TRATEGIES MORGAN STANLEY DEAN WITTER WHY HEDGE FUNDS MAKE SENSE FEATURE OVERVIEW NOVEMBER 2000 The hedge fund industry has experienced enormous growth in the last decade, growing by some estimates from as few as 300 funds in 1990 to more than 3000 today. They have become highly vis- ible and are thought to command up to $400 billion in capital before leverage. In this article, we analyze the performance and risk characteristics of hedge funds. We evaluate the reasons why hedge funds have produced high levels of risk-adjusted return and alpha, and show how portfolios of hedge funds can enhance strategic asset allocation for both pension funds and endowments. We conclude that hedge funds will increasingly challenge conventional investment management. • Over the past ten years, the typical individual hedge fund has produced risk adjusted returns that are quite similar to the typical mutual fund manager. However, individual hedge funds have realized a much wider range of performance compared with mutual funds. • Indexes of hedge funds have displayed risk-adjusted performance superior to traditional active managers and passive benchmarks over the past ten years. – Volatility of hedge fund indexes is typically much lower than that of mutual fund indexes and equity benchmarks. We believe that this is due to the low correlation among individual hedge funds. • The performance of hedge fund indexes can be closely approximated with a portfolio of as few as 20 hedge funds, suggesting a pooled fund-of-funds approach as a viable alternative investment strategy. • Hedge fund portfolios also exhibit a low correlation with traditional asset classes, suggest- ing that hedge funds should play an important role in strategic asset allocation. – We illustrate the efficacy of hedge funds for typical pension and endowment funds using MSDW’s asset-liability modeling framework. • Evidence points to continued success for hedge fund managers. – Historical performance of hedge funds appears to be based on the exploitation of market inefficiencies. Due to the expected growth in the supply of these inefficiencies, the advan- tages of hedge fund investments are not likely to diminish soon. MICHAEL W. PESKIN MICHAEL S. URIAS SATISH I. ANJILVEL BRYAN E. BOUDREAU (1-212) 761-6525 (1-212) 762-4788 (1-212) 762-4785 (1-212) 762-4875 [email protected] [email protected] [email protected] [email protected] This memorandum is based on information available to the public. No representation is made that it is accurate or complete. This memorandum is not an offer to buy or sell or a solicitation of an offer to buy or sell the securities or instruments or to participate in any particular trading strategy mentioned. Please refer to the notes at the end of this report. Global Equity and Derivative Markets November 2000 | Page 1 Q UANTITATIVE S TRATEGIES MORGAN STANLEY DEAN WITTER WHY HEDGE FUNDS MAKE SENSE MICHAEL W. PESKIN MICHAEL S. URIAS* SATISH I. ANJILVEL BRYAN E. BOUDREAU he hedge fund industry has experi- database covering the last ten years. We that when structured as portfolios, hedge enced enormous growth in the last evaluate trends in the capital markets that funds can provide a meaningful improve- T decade, growing by some estimates support a favorable outlook for hedge funds ment in the risk-reward tradeoff for an from as few as 300 funds in 1990 to more going forward, and show how hedge funds investor. can enhance Broadly speaking, hedge funds can be Hedge fund portfolios can provide a meaningful strategic asset defined as unregulated investment pools, improvement in the risk-reward tradeoff for an investor. allocation for generally with fewer than 100 investors, both pension that may invest in any asset class as well as than 3000 today.1 They have become funds and endowments. Our work shows derivative securities and use long and short highly visible in markets and the press, and are thought to 1 command up to $400 billion 1 A TAXONOMY OF HEDGE FUND STRATEGIES in capital before leverage. 2 Hedge funds, like # of Distinct other established alternative Strategy Description Sub-Strategies Fund Histories1 investments including real DIRECTIONAL Directional trading strategies are based upon Discretionary 408 estate, commodities, venture TRADING speculation of market direction in multiple asset Trading, Macro capital, and private equity, classes. Both model-based systems and Trading, Systems Trading are thought to provide access subjective judgment are used to make trading decisions. to returns that are uncorre- lated with traditional invest- RELATIVE Relative value strategies focus on spread Convergence 348 ments, and superior risk- VALUE relationships between pricing components of Arbitrage, Merger adjusted returns as well. financial assets. Market risk is kept to a minimum. Arbitrage, Statistical Many managers use leverage to enhance returns. Arbitrage There is a trend among fund management compa- SPECIALIST Specialist credit strategies are based around Distressed 80 nies to introduce hedge fund- CREDIT lending to credit sensitive issuers. Funds in this Securities, Positive like products, and large asset strategy conduct a high level of due diligence in Carry, Private order to identify relatively inexpensive securities. Placements owners have begun to make strategic allocations to hedge STOCK Stock selection strategies combine long and short Long Bias, No Bias, 547 funds (more on this later). SELECTION positions, primarily in equities, in order to exploit Short Bias, Variable In this article we ana- under and overvalued securities. Market exposure Bias lyze the performance and can vary substantially. risk characteristics of hedge 1. Since 1990. funds using a comprehensive 1. See Barry Riley, “Hedge Funds Come in From the Cold,” Financial Times, June 12, 2000. 2. Hedge funds are private funds that are available only to certain institutional and individual investors who meet specified legal and investment criteria. * We are grateful for the significant contributions of Joan K. Tse and Bradford J. Johmann to this article. We also thank Peter Fanelli for his comments and input. This memorandum is based on information available to the public. No representation is made that it is accurate or complete. This memorandum is not an offer to buy or sell or a solicitation of an offer to buy or sell the securities or instruments or to participate in any particular trading strategy mentioned. Please refer to the notes at the end of this report. Page 2 | November 2000 Global Equity and Derivative Markets Q UANTITATIVE S TRATEGIES MORGAN STANLEY DEAN WITTER WHY HEDGE FUNDS MAKE SENSE positions, as well as leverage. A distin- guishing feature of hedge funds is their rou- 2 MEDIAN INDIVIDUAL HEDGE FUND PERFORMANCE AND RISK CHARACTERISTICS tine use of long or short positions to offset “market” risks and isolate arbitrage oppor- 20% Directional Trading tunities, although these opportunities are 16% Relative Value not without their own specific risks. Hedge Specialist Credit funds are generally more nimble and 12% dynamic in their trading strategies than tra- Stock Selection ditional active funds. 8% Lipper Large Cap Core To establish the historical facts about 4% Lipper Large Cap Grow th Geometric Return hedge fund performance and risk, we study Lipper Large Cap Value a comprehensive database maintained by 0% Financial Risk Management, Limited 0% 5% 10% 15% 20% 25% (FRM). The Appendix describes the con- Volatility tents of the FRM database. Also, since Note: Performance is annualized from monthly total return data, covering 1990 through June 2000. hedge fund performance data is susceptible to biases, we will adjust our figures where appropriate, with details on the methodol- return/risk profile of the typical Directional distribution of hedge fund managers is their ogy given in the Appendix. Trading or Stock Selection hedge fund man- much greater performance dispersion rela- Exhibit 1 briefly summarizes the data- ager is not demonstrably different from that tive to the Lipper large cap managers. base according to a classification system of the typical active manager. Exhibit 3 provides details on individual recently adopted by FRM and Morgan Stanley Capital Inter- The return/risk profile of the typical Stock Selection and Directional Trading national, Inc. (MSCI)1 , Given the wide range and hedge fund managers is similar to that of the typical active manager, but the idiosyncratic nature of hedge variation in performance across managers is wider for hedge funds. fund specialties, any classifi- cation system will not completely character- Focusing on the period since 1990, hedge fund manager performance and risk ize some managers, but we believe this one Exhibit 2 plots the annualized geometric for the four broad strategies and their sub- to be a reasonable description of the uni- return and standard deviation of the median strategies, as well as the three Lipper large verse. individual hedge fund in each of the four cap categories, for various subperiods from strategy categories, as well as the median in 1990. Looking at the percentiles of individ- THE FACTS ABOUT HEDGE three categories of Lipper large cap U.S. ual manager return and risk, hedge funds in 2 FUNDS mutual funds. The median number gives a particular category are much less similar an indication of the experience for a typical to one another, no matter how narrow the THE PROPERTIES OF INDIVIDUAL HEDGE manager. The median Relative Value and category is. Of course, the benchmark- FUNDS Specialist Credit managers have been less driven approach of the Lipper managers To begin our analysis of hedge fund perfor- volatile than the median Lipper large cap suggests such an outcome, but the actual mance and risk, we look at the distribution manager, while the median returns of the difference is striking. of individual hedge fund total returns, net of four hedge fund strategy managers have Compare, for example, the Stock fees, and compare it to that of traditional been in the same ballpark as the median Selection hedge fund managers and the Lip- active managers as well as individual Lipper managers.
Recommended publications
  • Portfolio Optimization with Drawdown Constraints
    Implemented in Portfolio Safeguard by AOrDa.com PORTFOLIO OPTIMIZATION WITH DRAWDOWN CONSTRAINTS Alexei Chekhlov1, Stanislav Uryasev2, Michael Zabarankin2 Risk Management and Financial Engineering Lab Center for Applied Optimization Department of Industrial and Systems Engineering University of Florida, Gainesville, FL 32611 Date: January 13, 2003 Correspondence should be addressed to: Stanislav Uryasev Abstract We propose a new one-parameter family of risk functions defined on portfolio return sample -paths, which is called conditional drawdown-at-risk (CDaR). These risk functions depend on the portfolio drawdown (underwater) curve considered in active portfolio management. For some value of the tolerance parameter a , the CDaR is defined as the mean of the worst (1-a) *100% drawdowns. The CDaR risk function contains the maximal drawdown and average drawdown as its limiting cases. For a particular example, we find optimal portfolios with constraints on the maximal drawdown, average drawdown and several intermediate cases between these two. The CDaR family of risk functions originates from the conditional value-at-risk (CVaR) measure. Some recommendations on how to select the optimal risk measure for getting practically stable portfolios are provided. We solved a real life portfolio allocation problem using the proposed risk functions. 1. Introduction Optimal portfolio allocation is a longstanding issue in both practical portfolio management and academic research on portfolio theory. Various methods have been proposed and studied (for a review, see, for example, Grinold and Kahn, 1999). All of them, as a starting point, assume some measure of portfolio risk. From a standpoint of a fund manager, who trades clients' or bank’s proprietary capital, and for whom the clients' accounts are the only source of income coming in the form of management and incentive fees, losing these accounts is equivalent to the death of his business.
    [Show full text]
  • ESG Considerations for Securitized Fixed Income Notes
    ESG Considerations for Securitized Fixed Neil Hohmann, PhD Managing Director Income Notes Head of Structured Products BBH Investment Management [email protected] +1.212.493.8017 Executive Summary Securitized assets make up over a quarter of the U.S. corporations since 2010, we find median price declines fixed income markets,1 yet assessments of environmental, for equity of those companies of -16%, and -3% median social, and governance (ESG) risks related to this sizable declines for their corporate bonds.3 Yet, the median price segment of the bond market are notably lacking. decline for securitized notes during these periods is 0% – securitized notes are as likely to climb in price as they are The purpose of this study is to bring securitized notes to fall in price, through these incidents. This should not be squarely into the realm of responsible investing through the surprising – securitizations are designed to insulate inves- development of a specialized ESG evaluation framework tors from corporate distress. They have a senior security for securitizations. To date, securitization has been notably interest in collateral, ring-fenced legal structures and absent from responsible investing discussions, probably further structural protections – all of which limit their owing to the variety of securitization types, their structural linkage to the originating company. However, we find that complexities and limited knowledge of the sector. Manag- a few securitization asset types, including whole business ers seem to either exclude securitizations from ESG securitizations and RMBS, can still bear substantial ESG assessment or lump them into their corporate exposures. risk. A specialized framework for securitizations is needed.
    [Show full text]
  • Portfolio Optimization with Drawdown Constraints
    PORTFOLIO OPTIMIZATION WITH DRAWDOWN CONSTRAINTS Alexei Chekhlov1, Stanislav Uryasev2, Michael Zabarankin2 Risk Management and Financial Engineering Lab Center for Applied Optimization Department of Industrial and Systems Engineering University of Florida, Gainesville, FL 32611 Date: January 13, 2003 Correspondence should be addressed to: Stanislav Uryasev Abstract We propose a new one-parameter family of risk functions defined on portfolio return sample -paths, which is called conditional drawdown-at-risk (CDaR). These risk functions depend on the portfolio drawdown (underwater) curve considered in active portfolio management. For some value of the tolerance parameter a , the CDaR is defined as the mean of the worst (1-a) *100% drawdowns. The CDaR risk function contains the maximal drawdown and average drawdown as its limiting cases. For a particular example, we find optimal portfolios with constraints on the maximal drawdown, average drawdown and several intermediate cases between these two. The CDaR family of risk functions originates from the conditional value-at-risk (CVaR) measure. Some recommendations on how to select the optimal risk measure for getting practically stable portfolios are provided. We solved a real life portfolio allocation problem using the proposed risk functions. 1. Introduction Optimal portfolio allocation is a longstanding issue in both practical portfolio management and academic research on portfolio theory. Various methods have been proposed and studied (for a review, see, for example, Grinold and Kahn, 1999). All of them, as a starting point, assume some measure of portfolio risk. From a standpoint of a fund manager, who trades clients' or bank’s proprietary capital, and for whom the clients' accounts are the only source of income coming in the form of management and incentive fees, losing these accounts is equivalent to the death of his business.
    [Show full text]
  • T013 Asset Allocation Minimizing Short-Term Drawdown Risk And
    Asset Allocation: Minimizing Short-Term Drawdown Risk and Long-Term Shortfall Risk An institution’s policy asset allocation is designed to provide the optimal balance between minimizing the endowment portfolio’s short-term drawdown risk and minimizing its long-term shortfall risk. Asset Allocation: Minimizing Short-Term Drawdown Risk and Long-Term Shortfall Risk In this paper, we propose an asset allocation framework for long-term institutional investors, such as endowments, that are seeking to satisfy annual spending needs while also maintaining and growing intergenerational equity. Our analysis focuses on how asset allocation contributes to two key risks faced by endowments: short-term drawdown risk and long-term shortfall risk. Before approving a long-term asset allocation, it is important for an endowment’s board of trustees and/or investment committee to consider the institution’s ability and willingness to bear two risks: short-term drawdown risk and long-term shortfall risk. We consider various asset allocations along a spectrum from a bond-heavy portfolio (typically considered “conservative”) to an equity-heavy portfolio (typically considered “risky”) and conclude that a typical policy portfolio of 60% equities, 30% bonds, and 10% real assets represents a good balance between these short-term and long-term risks. We also evaluate the impact of alpha generation and the choice of spending policy on an endowment’s expected risk and return characteristics. We demonstrate that adding alpha through manager selection, tactical asset allocation, and the illiquidity premium significantly reduces long-term shortfall risk while keeping short-term drawdown risk at a desired level. We also show that even a small change in an endowment’s spending rate can significantly impact the risk profile of the portfolio.
    [Show full text]
  • A Nonlinear Optimisation Model for Constructing Minimal Drawdown Portfolios
    A nonlinear optimisation model for constructing minimal drawdown portfolios C.A. Valle1 and J.E. Beasley2 1Departamento de Ci^enciada Computa¸c~ao, Universidade Federal de Minas Gerais, Belo Horizonte, MG 31270-010, Brazil [email protected] 2Mathematical Sciences, Brunel University, Uxbridge UB8 3PH, UK [email protected] August 2019 Abstract In this paper we consider the problem of minimising drawdown in a portfolio of financial assets. Here drawdown represents the relative opportunity cost of the single best missed trading opportunity over a specified time period. We formulate the problem (minimising average drawdown, maximum drawdown, or a weighted combination of the two) as a non- linear program and show how it can be partially linearised by replacing one of the nonlinear constraints by equivalent linear constraints. Computational results are presented (generated using the nonlinear solver SCIP) for three test instances drawn from the EURO STOXX 50, the FTSE 100 and the S&P 500 with daily price data over the period 2010-2016. We present results for long-only drawdown portfolios as well as results for portfolios with both long and short positions. These indicate that (on average) our minimal drawdown portfolios dominate the market indices in terms of return, Sharpe ratio, maximum drawdown and average drawdown over the (approximately 1800 trading day) out-of-sample period. Keywords: Index out-performance; Nonlinear optimisation; Portfolio construction; Portfo- lio drawdown; Portfolio optimisation 1 Introduction Given a portfolio of financial assets then, as time passes, the price of each asset changes and so by extension the value of the portfolio changes.
    [Show full text]
  • Maximum Drawdown Is Generally Greater Than in IID Normal Case
    THE HIGH COST OF SIMPLIFIED MATH: Overcoming the “IID Normal” Assumption in Performance Evaluation Marcos López de Prado Hess Energy Trading Company Lawrence Berkeley National Laboratory Electronic copy available at: http://ssrn.com/abstract=2254668 Key Points • Investment management firms routinely hire and fire employees based on the performance of their portfolios. • Such performance is evaluated through popular metrics that assume IID Normal returns, like Sharpe ratio, Sortino ratio, Treynor ratio, Information ratio, etc. • Investment returns are far from IID Normal. • If we accept first-order serial correlation: – Maximum Drawdown is generally greater than in IID Normal case. – Time Under Water is generally longer than in IID Normal case. – However, Penance is typically shorter than 3x (IID Normal case). • Conclusion: Firms evaluating performance through Sharpe ratio are firing larger numbers of skillful managers than originally targeted, at a substantial cost to investors. 2 Electronic copy available at: http://ssrn.com/abstract=2254668 SECTION I The Need for Performance Evaluation Electronic copy available at: http://ssrn.com/abstract=2254668 Why Performance Evaluation? • Hedge funds operate as banks lending money to Portfolio Managers (PMs): – This “bank” charges ~80% − 90% on the PM’s return (not the capital allocated). – Thus, it requires each PM to outperform the risk free rate with a sufficient confidence level: Sharpe ratio. – This “bank” pulls out the line of credit to underperforming PMs. Allocation 1 … Investors’ funds Allocation N 4 How much is Performance Evaluation worth? • A successful hedge fund serves its investors by: – building and retaining a diversified portfolio of truly skillful PMs, taking co-dependencies into account, allocating capital efficiently.
    [Show full text]
  • Capital Adequacy Requirements (CAR)
    Guideline Subject: Capital Adequacy Requirements (CAR) Chapter 3 – Credit Risk – Standardized Approach Effective Date: November 2017 / January 20181 The Capital Adequacy Requirements (CAR) for banks (including federal credit unions), bank holding companies, federally regulated trust companies, federally regulated loan companies and cooperative retail associations are set out in nine chapters, each of which has been issued as a separate document. This document, Chapter 3 – Credit Risk – Standardized Approach, should be read in conjunction with the other CAR chapters which include: Chapter 1 Overview Chapter 2 Definition of Capital Chapter 3 Credit Risk – Standardized Approach Chapter 4 Settlement and Counterparty Risk Chapter 5 Credit Risk Mitigation Chapter 6 Credit Risk- Internal Ratings Based Approach Chapter 7 Structured Credit Products Chapter 8 Operational Risk Chapter 9 Market Risk 1 For institutions with a fiscal year ending October 31 or December 31, respectively Banks/BHC/T&L/CRA Credit Risk-Standardized Approach November 2017 Chapter 3 - Page 1 Table of Contents 3.1. Risk Weight Categories ............................................................................................. 4 3.1.1. Claims on sovereigns ............................................................................... 4 3.1.2. Claims on unrated sovereigns ................................................................. 5 3.1.3. Claims on non-central government public sector entities (PSEs) ........... 5 3.1.4. Claims on multilateral development banks (MDBs)
    [Show full text]
  • Multi-Period Portfolio Selection with Drawdown Control
    Author's personal copy Annals of Operations Research (2019) 282:245–271 https://doi.org/10.1007/s10479-018-2947-3 S.I.: APPLICATION OF O. R. TO FINANCIAL MARKETS Multi-period portfolio selection with drawdown control Peter Nystrup1,2 · Stephen Boyd3 · Erik Lindström4 · Henrik Madsen1 Published online: 20 June 2018 © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract In this article, model predictive control is used to dynamically optimize an investment port- folio and control drawdowns. The control is based on multi-period forecasts of the mean and covariance of financial returns from a multivariate hidden Markov model with time- varying parameters. There are computational advantages to using model predictive control when estimates of future returns are updated every time new observations become available, because the optimal control actions are reconsidered anyway. Transaction and holding costs are discussed as a means to address estimation error and regularize the optimization problem. The proposed approach to multi-period portfolio selection is tested out of sample over two decades based on available market indices chosen to mimic the major liquid asset classes typ- ically considered by institutional investors. By adjusting the risk aversion based on realized drawdown, it successfully controls drawdowns with little or no sacrifice of mean–variance efficiency. Using leverage it is possible to further increase the return without increasing the maximum drawdown. Keywords Risk management · Maximum drawdown · Dynamic asset allocation · Model predictive control · Regime switching · Forecasting This work was supported by Sampension and Innovation Fund Denmark under Grant No. 4135-00077B.. B Peter Nystrup [email protected] Stephen Boyd [email protected] Erik Lindström [email protected] Henrik Madsen [email protected] 1 Department of Applied Mathematics and Computer Science, Technical University of Denmark, Asmussens Allé, Building 303B, 2800 Kgs.
    [Show full text]
  • What Do One Million Credit Line Observations Tell Us About Exposure at Default? a Study of Credit Line Usage by Spanish Firms
    What Do One Million Credit Line Observations Tell Us about Exposure at Default? A Study of Credit Line Usage by Spanish Firms Gabriel Jiménez Banco de España [email protected] Jose A. Lopez Federal Reserve Bank of San Francisco [email protected] Jesús Saurina Banco de España [email protected] DRAFT…….Please do not cite without authors’ permission Draft date: June 16, 2006 ABSTRACT Bank credit lines are a major source of funding and liquidity for firms and a key source of credit risk for the underwriting banks. In fact, credit line usage is addressed directly in the current Basel II capital framework through the exposure at default (EAD) calculation, one of the three key components of regulatory capital calculations. Using a large database of Spanish credit lines across banks and years, we model the determinants of credit line usage by firms. We find that the risk profile of the borrowing firm, the risk profile of the lender, and the business cycle have a significant impact on credit line use. During recessions, credit line usage increases, particularly among the more fragile borrowers. More importantly, we provide robust evidence of more intensive use of credit lines by borrowers that later default on those lines. Our data set allows us to enter the policy debate on the EAD components of the Basel II capital requirements through the calculation of credit conversion factors (CCF) and loan equivalent exposures (LEQ). We find that EAD exhibits procyclical characteristics and is affected by credit line characteristics, such as commitment size, maturity, and collateral requirements.
    [Show full text]
  • Implementing and Testing the Maximum Drawdown at Risk
    Finance Research Letters 22 (2017) 95–100 Contents lists available at ScienceDirect Finance Research Letters journal homepage: www.elsevier.com/locate/frl Implementing and testing the Maximum Drawdown at Risk ∗ Beatriz Vaz de Melo Mendes a, , Rafael Coelho Lavrado b a COPPEAD/IM, Federal University at Rio de Janeiro, Brazil b IMPA, Instituto Nacional de Matemática Pura e Aplicada, Brazil a r t i c l e i n f o a b s t r a c t Article history: Financial managers are mainly concerned about long lasting accumulated large losses Received 22 October 2016 which may lead to massive money withdrawals. To assess this risk feeling we compute Revised 26 May 2017 the Maximum Drawdown, the largest price loss of an investment during some fixed time Accepted 3 June 2017 period. The Maximum Drawdown at Risk has become an important risk measure for com- Available online 9 June 2017 modity trading advisors, hedge funds managers, and regulators. In this study we propose JEL classification: an estimation methodology based on Monte Carlo simulations and empirically validate the C14 procedure using international stock indices. We find that this tool provides more accurate C15 market risk control and may be used to manage portfolio exposure, being useful to practi- C53 tioners and financial analysts. C58 ©2017 Elsevier Inc. All rights reserved. G32 Keywords: Risk management Maximum drawdown ARMA-GARCH Simulations 1. Introduction Financial managers are mainly concerned about large losses because they may destroy accumulated wealth leading to massive money withdrawals and risking the continuity of businesses. Financial crises such as the 2008 global one have shown how extensive these losses could be, with investment funds all over the world showing more than 50% losses and survivors taking several years to recover.
    [Show full text]
  • Pros and Cons of “Drawdown” As a Statistical Meadure of Risk For
    The Pros and Cons of “Drawdown” as a Statistical Measure of Risk for Investments By David Harding, Georgia Nakou & Ali Nejjar Winton Capital Management 1a St Mary Abbot’s Place London W8 6LS, United Kingdom. Tel. +44 20 7610 5350 Fax. +44 20 7610 5301 www.wintoncapital.com A key measure of track record quality and strategy “riskiness” in the managed futures industry is drawdown, which measures the decline in net asset value from the historic high point. In this discussion we want to look at its strengths and weaknesses as a summary statistic, and examine some of its frequently overlooked features. Under the Commodity Futures Trading Commission’s mandatory disclosure regime, managed futures advisors are obliged to disclose as part of their capsule performance record their “worst peak-to-valley drawdown”1. As a description of an aspect of historical performance, drawdown has one key positive attribute: it refers to a physical reality, and as such it is less abstract than concepts such as volatility. It represents the amount by which you are less well off than you were; or, put differently, it measures the magnitude of the loss an investor could have incurred by investing with the manager in the past. Managers are obliged to wear their worst historical drawdown like a scarlet letter for the rest of their lives. However, this number is less straightforwardly indicative of manager quality as is often assumed. The seeming solidity of the drawdown statistic dissipates under closer examination, due to a host of limitations which are rarely explored sufficiently when assessing its significance as a guide to the future performance of an investment.
    [Show full text]
  • Picking the Right Risk-Adjusted Performance Metric
    WORKING PAPER: Picking the Right Risk-Adjusted Performance Metric HIGH LEVEL ANALYSIS QUANTIK.org Investors often rely on Risk-adjusted performance measures such as the Sharpe ratio to choose appropriate investments and understand past performances. The purpose of this paper is getting through a selection of indicators (i.e. Calmar ratio, Sortino ratio, Omega ratio, etc.) while stressing out the main weaknesses and strengths of those measures. 1. Introduction ............................................................................................................................................. 1 2. The Volatility-Based Metrics ..................................................................................................................... 2 2.1. Absolute-Risk Adjusted Metrics .................................................................................................................. 2 2.1.1. The Sharpe Ratio ............................................................................................................................................................. 2 2.1. Relative-Risk Adjusted Metrics ................................................................................................................... 2 2.1.1. The Modigliani-Modigliani Measure (“M2”) ........................................................................................................ 2 2.1.2. The Treynor Ratio .........................................................................................................................................................
    [Show full text]