Understanding Alpha Versus Beta in High Yield Bond Investing
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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. -
HIGH YIELD OR “JUNK' BONDS, HAVEN OR HORROR by Mason A
HIGH YIELD OR “JUNK’ BONDS, HAVEN OR HORROR By Mason A. Dinehart III, RFC A high yield, or “junk” bond is a bond issued by a company that is considered to be a higher credit risk. The credit rating of a high yield bond is considered “speculative” grade or below “investment grade”. This means that the chance of default with high yield bonds is higher than for other bonds. Their higher credit risk means that “junk” bond yields are higher than bonds of better credit quality. Studies have demonstrated that portfolios of high yield bonds have higher returns than other bond portfolios, suggesting that the higher yields more than compensate for their additional default risk. It should be noted that “unrated” bonds are legally in the “junk” category and may or may not be speculative in terms of credit quality. High yield or “junk” bonds get their name from their characteristics. As credit ratings were developed for bonds, the credit agencies created a grading system to reflect the relative credit quality of bond issuers. The highest quality bonds are “AAA” and the credit scale descends to “C” and finally to “D” or default category. Bonds considered to have an acceptable risk of default are “investment grade” and encompass “BBB” bonds (Standard & Poors) or “Baa” bonds (Moody’s) and higher. Bonds “BB” and lower are called “speculative grade” and have a higher risk of default. Rulemakers soon began to use this demarcation to establish investment policies for financial institutions, and government regulation has adopted these standards. Since most investors were restricted to investment grade bonds, speculative grade bonds soon developed negative connotations and were not widely held in investment portfolios. -
Foundations of High-Yield Analysis
Research Foundation Briefs FOUNDATIONS OF HIGH-YIELD ANALYSIS Martin Fridson, CFA, Editor In partnership with CFA Society New York FOUNDATIONS OF HIGH-YIELD ANALYSIS Martin Fridson, CFA, Editor Statement of Purpose The CFA Institute Research Foundation is a not- for-profit organization established to promote the development and dissemination of relevant research for investment practitioners worldwide. Neither the Research Foundation, CFA Institute, nor the publication’s editorial staff is responsible for facts and opinions presented in this publication. This publication reflects the views of the author(s) and does not represent the official views of the CFA Institute Research Foundation. The CFA Institute Research Foundation and the Research Foundation logo are trademarks owned by The CFA Institute Research Foundation. CFA®, Chartered Financial Analyst®, AIMR- PPS®, and GIPS® are just a few of the trademarks owned by CFA Institute. To view a list of CFA Institute trademarks and the Guide for the Use of CFA Institute Marks, please visit our website at www.cfainstitute.org. © 2018 The CFA Institute Research Foundation. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the copyright holder. This publication is designed to provide accurate and authoritative information in regard to the subject matter covered. It is sold with the understanding that the publisher is not engaged in rendering legal, accounting, or other professional service. If legal advice or other expert assistance is required, the services of a competent professional should be sought. -
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)). -
GGD-89-48 High Yield Bonds
GAO Iteport, to Congressional Rcqueskrs -..- ~--. .--- March 1!))I!) HIGH YIELD BONDS Issues Concerning Thrift Investments in High Yield Bonds i ,I unitedstates General Accounting Office GAO Wahington, D.C. 20548 General Government Division B-231276.8 March 2, I989 The Honorable Donald W. Riegle Chairman, Committee on Banking, Housing and Urban Affairs United States Senate The Honorable Henry B. Gonzalez Chairman, Committee on Banking, Finance and Urban Affairs House of Representatives This report completes our responseto the requirements of Section 1201 of the Competitive Equality Banking Act of 1987 (P.L. lOO-86),which directed us to study several aspects of the high yield bond market As required by the act, we did our work in consultation with the Securities and Exchange Commission, the Federal Home Loan Bank Board, the Comptroller of the Currency, the Board of Governors of the Federal ReserveSystem, the Federal Savings and Loan Insurance Corporation, the Federal Deposit Insurance Corporation, the Secretary of the Treasury, and the Secretary of Labor. Copies of this report are being provided to those agenciesand are available to others on request. This report was prepared under the direction of Craig A. Simmons, Director, Financial Institutions and Markets Issues, Other major contributors are listed in the appendix. Richard L. Fogel Assistant Comptroller General Ekecutive Summ~ The growth of the high yield (“junk”) bond market in the 1980s has Purpose been fraught with controversy. Numerous congressionalhearings have focused on the role of these bonds as a tool to finance takeovers-of com- panies. Major figures in the high yield bond market have been the target of investigations by congressionalcommittees, the Securities and Exchange Commission, and the Justice Department. -
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. -
Alternative Beta
What a CAIA Member Should Know Alternative Beta: Redefining Alpha and Beta Soheil Galal Alternative beta (alt beta) strategies extend These strategies can complement traditional, Managing Director the concept of “beta investing” from long-only actively managed hedge fund allocations and Global Investment Management traditional strategies to strategies that include provide more discriminating tools to support Solutions, J.P. Morgan Asset Management both long and short investing. Although alt alternative manager due diligence. beta approaches have relevance for different Alternative beta (alt beta) strategies have Rafael Silveira categories of alternatives, this article focuses opened a new avenue for accessing the Executive Director and Portfolio on hedge fund-related strategies, currently the investment characteristics for which hedge Strategist, most prevalent form. Institutional Solutions & Advisory funds have become highly valued. J.P. Morgan Asset Management Alt beta strategies are rules-based strategies These strategies provide ready access to designed to provide access to the portion of Alison Rapaport uncorrelated returns that can help improve hedge fund returns attributable to systematic Associate and Client Portfolio portfolio diversification, risk-return efficiency, risks (beta) vs. idiosyncratic manager skill Manager, and volatility management—without the high Multi-Asset Solutions, (alpha). As a result of these new strategies, a fees, lock-ups, and limited transparency often J.P. Morgan Asset Management component of hedge fund returns previously associated with hedge funds.1 viewed as alpha has been redefined as beta. A passive, rules-based approach gives alt beta We see this redefinition of alpha as beta to be a strategies the ability to provide liquid, low- transformational trend in hedge fund investing: cost, and transparent access to the beta (vs. -
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,