Alternative Beta
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Understanding the Risks of Smart Beta and the Need for Smart Alpha
UNDERSTANDING THE RISKS OF SMART BETA, AND THE NEED FOR SMART ALPHA February 2015 UNCORRELATED ANSWERS® Key Ideas Richard Yasenchak, CFA Senior Managing Director, Head of Client Portfolio Management There has been a proliferation of smart-beta strategies the past several years. According to Morningstar, asset flows into smart-beta offerings have been considerable and there are now literally thousands of such products, all offering Phillip Whitman, PhD a systematic but ‘different’ equity exposure from that offered by traditional capitalization-weighted indices. During the five years ending December 31, 2014 assets under management (AUM) of smart-beta ETFs grew by 320%; for the same period, index funds experienced AUM growth of 235% (Figure 1 on the following page). Smart beta has also become the media darling of the financial press, who often position these strategies as the answer to every investor’s investing prayers. So what is smart beta; what are its risks; and why should it matter to investors? Is there an alternative strategy that might help investors meet their investing objectives over time? These questions and more will be answered in this paper. This page is intentionally left blank. Defining smart beta and its smart-beta strategies is that they do not hold the cap-weighted market index; instead they re-weight the index based on different intended use factors such as those mentioned previously, and are therefore not The intended use of smart-beta strategies is to mitigate exposure buy-and-hold strategies like a cap-weighted index. to undesirable risk factors or to gain a potential benefit by increasing exposure to desirable risk factors resulting from a Exposure risk tactical or strategic view on the market. -
Low-Beta Strategies
A Service of Leibniz-Informationszentrum econstor Wirtschaft Leibniz Information Centre Make Your Publications Visible. zbw for Economics Korn, Olaf; Kuntz, Laura-Chloé Working Paper Low-beta strategies CFR Working Paper, No. 15-17 [rev.] Provided in Cooperation with: Centre for Financial Research (CFR), University of Cologne Suggested Citation: Korn, Olaf; Kuntz, Laura-Chloé (2017) : Low-beta strategies, CFR Working Paper, No. 15-17 [rev.], University of Cologne, Centre for Financial Research (CFR), Cologne This Version is available at: http://hdl.handle.net/10419/158007 Standard-Nutzungsbedingungen: Terms of use: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Documents in EconStor may be saved and copied for your Zwecken und zum Privatgebrauch gespeichert und kopiert werden. personal and scholarly purposes. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle You are not to copy documents for public or commercial Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich purposes, to exhibit the documents publicly, to make them machen, vertreiben oder anderweitig nutzen. publicly available on the internet, or to distribute or otherwise use the documents in public. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, If the documents have been made available under an Open gelten abweichend von diesen Nutzungsbedingungen die in der dort Content Licence (especially Creative Commons Licences), you genannten Lizenz gewährten -
Arbitrage Pricing Theory∗
ARBITRAGE PRICING THEORY∗ Gur Huberman Zhenyu Wang† August 15, 2005 Abstract Focusing on asset returns governed by a factor structure, the APT is a one-period model, in which preclusion of arbitrage over static portfolios of these assets leads to a linear relation between the expected return and its covariance with the factors. The APT, however, does not preclude arbitrage over dynamic portfolios. Consequently, applying the model to evaluate managed portfolios contradicts the no-arbitrage spirit of the model. An empirical test of the APT entails a procedure to identify features of the underlying factor structure rather than merely a collection of mean-variance efficient factor portfolios that satisfies the linear relation. Keywords: arbitrage; asset pricing model; factor model. ∗S. N. Durlauf and L. E. Blume, The New Palgrave Dictionary of Economics, forthcoming, Palgrave Macmillan, reproduced with permission of Palgrave Macmillan. This article is taken from the authors’ original manuscript and has not been reviewed or edited. The definitive published version of this extract may be found in the complete The New Palgrave Dictionary of Economics in print and online, forthcoming. †Huberman is at Columbia University. Wang is at the Federal Reserve Bank of New York and the McCombs School of Business in the University of Texas at Austin. The views stated here are those of the authors and do not necessarily reflect the views of the Federal Reserve Bank of New York or the Federal Reserve System. Introduction The Arbitrage Pricing Theory (APT) was developed primarily by Ross (1976a, 1976b). It is a one-period model in which every investor believes that the stochastic properties of returns of capital assets are consistent with a factor structure. -
Understanding Alpha Versus Beta in High Yield Bond Investing
PERITUS ASSET MANAGEMENT, LLC Market Commentary Independent Credit Research – Leveraged Finance – July 2012 Understanding Alpha versus Beta in High Yield Bond Investing Investors have come to recognize that there is a secular change occurring in financial markets. After the 2008 meltdown, we have watched equities stage an impressive rally off the bottom, only to peter out. There is no conviction and valuations are once again stretched given the lack of growth as the world economy remains on shaky ground. In another one of our writings (“High Yield Bonds versus Equities”), we discussed our belief that U.S. businesses would plod along, but valuations would continue their march toward the lower end of their historical range. So far, this looks like the correct call. Europe still hasn’t been fixed, the China miracle is slowing and, perhaps, the commodity supercycle is also in the later innings. The amount of debt assumed by the developed world is unsustainable so deleveraging began a few years ago. These are not short or pleasant cycles and both governments and individuals will be grumpy participants in this event. It simply means that economic growth will be subdued for years to come. I have never really been able to understand economic growth rates that are significantly ahead of the population growth anyway…oh yeah, the productivity miracle. I have written chapter and verse on the history of financial markets and where returns have come from: yield. Anyone with access to the internet can verify that dividends, dividend growth and dividend re-investment are what have driven stock returns over the decades. -
Leverage and the Beta Anomaly
Downloaded from https://doi.org/10.1017/S0022109019000322 JOURNAL OF FINANCIAL AND QUANTITATIVE ANALYSIS COPYRIGHT 2019, MICHAEL G. FOSTER SCHOOL OF BUSINESS, UNIVERSITY OF WASHINGTON, SEATTLE, WA 98195 doi:10.1017/S0022109019000322 https://www.cambridge.org/core Leverage and the Beta Anomaly Malcolm Baker, Mathias F. Hoeyer, and Jeffrey Wurgler * . IP address: 199.94.10.45 Abstract , on 13 Dec 2019 at 20:01:41 The well-known weak empirical relationship between beta risk and the cost of equity (the beta anomaly) generates a simple tradeoff theory: As firms lever up, the overall cost of cap- ital falls as leverage increases equity beta, but as debt becomes riskier the marginal benefit of increasing equity beta declines. As a simple theoretical framework predicts, we find that leverage is inversely related to asset beta, including upside asset beta, which is hard to ex- plain by the traditional leverage tradeoff with financial distress that emphasizes downside risk. The results are robust to a variety of specification choices and control variables. , subject to the Cambridge Core terms of use, available at I. Introduction Millions of students have been taught corporate finance under the assump- tion of the capital asset pricing model (CAPM) and integrated equity and debt markets. Yet, it is well known that the link between textbook measures of risk and realized returns in the stock market is weak, or even backward. For example, a dollar invested in a low beta portfolio of U.S. stocks in 1968 grows to $70.50 by 2011, while a dollar in a high beta portfolio grows to just $7.61 (see Baker, Bradley, and Taliaferro (2014)). -
ALTERNATIVE BETA MATTERS Quarterly Newsletter - Q4 2017
Alt Beta Newsletter 30th October 2017 ALTERNATIVE BETA MATTERS Quarterly newsletter - Q4 2017 Introduction Welcome to CFM’s Alternative Beta Matters Quarterly Newsletter. Within this report we recap major developments in the Alternative Industry, Equities, Fixed Income / Credit, FX and Commodities as well as Trading Regulations. All discussion is agnostic to particular approaches or techniques, and where alternative benchmark strategy results are presented, the exact methodology used is given. We have also included one white paper and an extended academic abstract from a paper published during the quarter. Our hope is that these publications, which convey our views on topics related to Alternative Beta that have arisen in our many discussions with clients, can be used as a reference for our readers, and can stimulate conversations on these topical issues. Contact details Call us +33 1 49 49 59 49 Email us [email protected] www.cfm.fr CFM Alternative Beta Matters Total return for Equity Market Neutral (EMN) and CTA Quarterly review hedge fund indices over the past year1 Quantitative overview of key developments in Q3 2017 Alternative industry performance The principal implied volatility indices across four asset classes over the past year2 After a challenging Q2, Commodity trading advisors (CTAs) posted a relatively neutral quarter. Whilst the HFRX Equity Market Neutral index put in a solid showing, increasing 2.1%, the Equity Hedge index was the best performer in the HFRX universe with a 3.2% gain. At the other end of the scale, the Event Driven Distressed Restructuring index fared the worst, losing 0.1%. CTAs clawed back some of the losses in Q2, with the The log of the dollar risk weighted average daily volume 3 benchmark SG CTA index gaining 0.7% over the period. -
The Capital Asset Pricing Model (Capm)
THE CAPITAL ASSET PRICING MODEL (CAPM) Investment and Valuation of Firms Juan Jose Garcia Machado WS 2012/2013 November 12, 2012 Fanck Leonard Basiliki Loli Blaž Kralj Vasileios Vlachos Contents 1. CAPM............................................................................................................................................... 3 2. Risk and return trade off ............................................................................................................... 4 Risk ................................................................................................................................................... 4 Correlation....................................................................................................................................... 5 Assumptions Underlying the CAPM ............................................................................................. 5 3. Market portfolio .............................................................................................................................. 5 Portfolio Choice in the CAPM World ........................................................................................... 7 4. CAPITAL MARKET LINE ........................................................................................................... 7 Sharpe ratio & Alpha ................................................................................................................... 10 5. SECURITY MARKET LINE ................................................................................................... -
The Memory of Beta Factors∗
The Memory of Beta Factors∗ Janis Beckery, Fabian Hollsteiny, Marcel Prokopczuky,z, and Philipp Sibbertseny September 24, 2019 Abstract Researchers and practitioners employ a variety of time-series pro- cesses to forecast betas, using either short-memory models or implic- itly imposing infinite memory. We find that both approaches are inadequate: beta factors show consistent long-memory properties. For the vast majority of stocks, we reject both the short-memory and difference-stationary (random walk) alternatives. A pure long- memory model reliably provides superior beta forecasts compared to all alternatives. Finally, we document the relation of firm characteris- tics with the forecast error differentials that result from inadequately imposing short-memory or random walk instead of long-memory pro- cesses. JEL classification: C58, G15, G12, G11 Keywords: Long memory, beta, persistence, forecasting, predictability ∗Contact: [email protected] (J. Becker), [email protected] (F. Holl- stein), [email protected] (M. Prokopczuk), and [email protected] (P. Sibbertsen). ySchool of Economics and Management, Leibniz University Hannover, Koenigsworther Platz 1, 30167 Hannover, Germany. zICMA Centre, Henley Business School, University of Reading, Reading, RG6 6BA, UK. 1 Introduction In factor pricing models like the Capital Asset Pricing Model (CAPM) (Sharpe, 1964; Lintner, 1965; Mossin, 1966) or the arbitrage pricing theory (APT) (Ross, 1976) the drivers of expected returns are the stock's sensitivities to risk factors, i.e., beta factors. For many applications such as asset pricing, portfolio choice, capital budgeting, or risk management, the market beta is still the single most important factor. Indeed, Graham & Harvey(2001) document that chief financial officers of large U.S. -
Pax Esg Beta® Dividend Fund Commentary Q2 2017
PAX ESG BETA® DIVIDEND FUND COMMENTARY Q2 2017 Fund Overview Performance and Portfolio Update A smart beta investing strategy focused on • Th e Fund underperformed the benchmark Russell 1000 Index1 in the second quarter. companies’ ESG strength, dividend yield and ability to sustain future dividend payouts. Th e main drivers of the return diff erence were the factors used in the strategy construction as positive contributions from the profi tability and management quality factors were not enough to off set poor relative performance from the dividend yield and Investment Process earnings quality factors. Optimized, factor-based Investment Style • Th e overweight towards companies with higher dividend yield was the largest detractor Equity Income to performance for the period as dividend yielding stocks tend to struggle in an environment dominated by growth stocks. Benchmark 1 Russell 1000 Index • Th e tilt towards dividend sustainability factors benefi ted relative performance slightly. Particularly, the strategy’s exposure to companies with higher profi tability and management quality boosted performance relative to the benchmark index. Th e tilt Portfolio Characteristics as of 6/30/17 towards companies with higher earnings quality detracted from relative performance. Fund Benchmark • Environmental, social and governance (ESG) factors, as measured by the Pax Market Cap Sustainability Score, detracted from performance for the period. Th e Fund overweights (weighted avg.)2 $128,596M $151,674M its portfolio towards companies with ESG strength. During the period, companies with Forward stronger ESG profi les trailed those with weaker ESG profi les. Price/Earnings3 18.18 18.81 4 ROE 22.00 17.76 • Industry exposures, which are driven by the factor and ESG tilts, had a negligible impact Beta5 0.95 1.00 on relative returns for the quarter. -
The Rise of Algorithmic Trading and Its Effects on Return Dispersion and Market Predictability
______________________________________________________________________________ The rise of Algorithmic Trading and its effects on Return Dispersion and Market Predictability Master Thesis Finance ______________________________________________________________________________ Name: Rick Verheggen Student Number: u1257435 Student E-mail: Personal E-mail: Date: November, 2017 Supervisor: dr. R.G.P. Frehen Second Reader: dr. F. Castiglionesi ______________________________________________________________________________ ALGORITHMIC TRADING & MARKET PREDICTABILITY 2 Abstract A revolution is happening within the financial markets as trading algorithms are executing the grand majority of all trades. Moreover, computers are substituting human traders as well as the emotion involved in their trading. Since trading algorithms are not subject to emotion which is known to cause market inefficiencies, markets are thought to have become more efficient. Additionally, as fewer human traders are active within the market fewer predictable biases apply that are known within behavioral finance and thus is expected that the market has become less predictable. This study was designed to determine the effects of algorithmic trading on dispersion and forecast accuracy. Dispersion is measured through idiosyncratic volatility and tested against algorithmic trading, and by measuring the prediction error of the remaining human traders on the market it is tested to see if analysts’ predictions have indeed become less accurate with the rise of algorithmic trading. Instead, -
Tracking Problems, Hedge Fund Replication, and Alternative Beta
PART 1 Tracking Problems, Hedge Fund Replication, and Alternative Beta Thierry Roncalli — Professor of Finance, University of Evry, and Head of Research and Development, Lyxor Asset Management 1 Guillaume Weisang — Doctoral Candidate, Bentley University Abstract (2008), we detail how the Kalman !lter tracks changes in As hedge fund replication based on factor models has en- exposures, and show that it provides a replication methodol- countered growing interest among professionals and aca- ogy with a satisfying economic interpretation. Finally, we ad- demics, and despite the launch of numerous products (in- dress the problem of accessing the pure alpha by proposing dexes and mutual funds) in the past year, it has faced many a core/satellite approach of alternative investments between critics. In this paper, we consider two of the main critiques, high-liquid alternative beta and less liquid investments. Non- namely the lack of reactivity of hedge fund replication, its normality and non-linearities documented on hedge fund de!ciency in capturing tactical allocations, and the lack of returns are investigated using the same framework in a com- access to the alpha of hedge funds. To address these prob- panion paper [Roncalli and Weisang (2009)]. lems, we consider hedge fund replication as a general track- ing problem which may be solved by means of Bayesian !l- 1 The views expressed in this paper are those of the authors and do not nec- ters. Using the example provided by Roncalli and Teiletche essarily represent those of Lyxor Alternative Investments. 19 Over the past decade, hedge fund replication has encountered a growing tracking problems and Bayesian !lters and their associated algorithms, interest both from an academic and a practitioner perspective. -
Hedge Fund Replication and Alternative Beta
Investing in Hedge Funds HF Replication and Tracking Problems Alpha Considerations Conclusion Appendix Hedge Fund Replication and Alternative Beta Thierry Roncalli1 Guillaume Weisang2 1Évry University, France 2Department of Mathematical Sciences, Bentley University, MA Laboratoire J. A. Dieudonné, Université de Nice Sophia-Antipolis, April 29, 2009 Thierry Roncalli, Guillaume Weisang HF Replication and Alternative Beta Investing in Hedge Funds HF Replication and Tracking Problems Alpha Considerations Conclusion Appendix Outline 1 Investing in Hedge Funds The Success of the Hedge Fund Industry Hedge Fund Replication Factor Models Previous Works 2 HF Replication and Tracking Problems Tactical Asset Allocation and Tracking Problems Hedge Fund Replication: The Linear Gaussian Case Hedge Fund Replication: The Non-Linear Non-Gaussian Case 3 Alpha Considerations What is Alpha? Explaining the Alpha The Core/Satellite Approach 4 Conclusion HF Replication in Practice Main Ideas 5 Appendix Thierry Roncalli, Guillaume Weisang HF Replication and Alternative Beta Investing in Hedge Funds The Success of the Hedge Fund Industry HF Replication and Tracking Problems Hedge Fund Replication Alpha Considerations Factor Models Conclusion Previous Works Appendix The Success of the Hedge Fund Industry Hedge-Funds (HF) deliver higher Sharpe ratios than Buy-and-Hold strategies on traditional asset classes1: HFRI SPX UST Annualized Return 9.94% 8.18% 5.60% 1Y Volatility 7.06% 14.3% 6.95% Sharpe 0.77 0.26 0.18 The performance behaviour of the HF industry was very