Alpha, Beta and the CAPM Financial Markets, Day 1, Class 3
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
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 -
Disciplined Alpha Dividend As of 6/30/2021
Disciplined Alpha Dividend As of 6/30/2021 Equity Sectors (Morningstar) Growth of $100,000 % Time Period: 1/1/2003 to 6/30/2021 Consumer Defensive 22.3 625,000 Healthcare 15.8 550,000 Consumer Cyclical 14.7 Financial Services 13.9 475,000 Industrials 12.6 400,000 Technology 8.2 325,000 Energy 4.4 250,000 Communication Services 3.7 Real Estate 2.5 175,000 Basic Materials 1.9 100,000 Total 100.0 25,000 Strategy Highlights Pursues a high level of current income and long-term capital appreciation utilizing Trailing Returns Inception Date: 1/1/2003 proprietary top-down and bottom-up analysis Seeks a substantially higher dividend yield than the broad market YTD 1 Yr 3 Yrs 5 Yrs 10 Yrs 15 Yrs Incpt Invests primarily in 25- 50 companies with dividend growth potential Disciplined Alpha Dividend (Gross) 16.19 40.38 14.47 14.15 12.74 10.09 10.18 Offers the potential for competitive upside performance in strong market Disciplined Alpha Dividend (Net) 15.63 39.02 13.28 12.93 11.51 8.82 8.90 environments and the potential for lower downside risk in weak environments Morningstar US Value TR USD 17.03 41.77 10.76 11.29 10.92 7.61 9.43 Calendar Year Returns Disciplined Alpha Dividend – Top Holdings* 2020 2019 2018 2017 2016 2015 2014 2013 2012 2011 Portfolio Disciplined Alpha Dividend (Gross) 8.62 25.26 -3.48 16.20 17.06 -3.52 11.05 38.86 10.97 2.96 Weighting % Disciplined Alpha Dividend (Net) 7.51 23.91 -4.54 14.93 15.72 -4.58 9.81 37.33 9.67 1.77 Exxon Mobil Corp 2.52 Welltower Inc 2.43 Morningstar US Value TR USD -1.31 25.09 -7.51 14.23 20.79 -2.16 -
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. -
The Hidden Alpha in Equity Trading Steps to Increasing Returns with the Advanced Use of Information
THE HIDDEN ALPHA IN EQUITY TRADING STEPS TO INCREASING RETURNS WITH THE ADVANCED USE OF INFORMATION TABLE OF CONTENTS 1 INTRODUCTION 4 2 MARKET FRAGMENTATION AND THE GROWTH OF INFORMATION 5 3 THE PROLIFERATION OF HIGH FREQUENCY TRADING 8 4 FLASH CRASHES, BOTCHED IPOS AND OTHER SYSTEMIC CONCERNS 10 5 THE CHANGING INVESTOR-BROKER RELATIONSHIP 11 6 THE INFORMATION OPPORTUNITY FOR INSTITUTIONAL INVESTORS 13 7 STEPS TO FINDING HIDDEN ALPHA AND INCREASED RETURNS 15 1. INTRODUCTION Following regulatory initiatives aimed at creating THE TIME DIFFERENCE IN TRADING competition between trading venues, the equities market has fragmented. Liquidity is now dispersed ROUTES; PEOPLE VS. COMPUTERS across many lit equity trading venues and dark pools. This complexity, combined with trading venues becoming electronic, has created profit opportunities for technologically sophisticated players. High frequency traders (HFTs) use ultra-high speed connections with trading venues and sophisticated trading algorithms to exploit inefficiencies created by the new market vs. structure and to identify patterns in 3rd parties’ trading that they can use to their own advantage. Person Computer For traditional investors, however, these new market conditions are less welcome. Institutional investors find JUST HOW FAST ARE TRADERS PROCESSING TRADES? themselves falling behind these new competitors, in » Traders that take advantage of technology can create large part because the game has changed and because programs that trade in milliseconds. In many cases they lack the tools -
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. -
Sources of Hedge Fund Returns: Alpha, Beta, and Costs
Yale ICF Working Paper No. 06-10 September 2006 The A,B,Cs of Hedge Funds: Alphas, Betas, and Costs Roger G. Ibbotson, Yale School of Management Peng Chen, Ibbotson Associates This paper can be downloaded without charge from the Social Science Research Network Electronic Paper Collection: http://ssrn.com/abstract=733264 Working Paper The A,B,Cs of Hedge Funds: Alphas, Betas, and Costs Roger G. Ibbotson, Ph.D. Professor in the Practice of Finance Yale School of Management 135 Prospect Street New Haven, CT 06520-8200 Phone: (203) 432-6021 Fax: (203) 432-6970 Chairman & CIO Zebra Capital Mgmt, LLC Phone: (203) 878-3223 Peng Chen, Ph.D., CFA President & Chief Investment Officer Ibbotson Associates, Inc. 225 N. Michigan Ave. Suite 700 Chicago, IL 60601-7676 Phone: (312) 616-1620 Fax: (312) 616-0404 Email: [email protected] August 2005 June 2006 September 2006 1 The A,B,Cs of Hedge Funds ABSTRACT In this paper, we focus on two issues. First, we analyze the potential biases in reported hedge fund returns, in particular survivorship bias and backfill bias, and attempt to create an unbiased return sample. Second, we decompose these returns into their three A,B,C components: the value added by hedge funds (alphas), the systematic market exposures (betas), and the hedge fund fees (costs). We analyze the performance of a universe of about 3,500 hedge funds from the TASS database from January 1995 through April 2006. Our results indicate that both survivorship and backfill biases are potentially serious problems. The equally weighted performance of the funds that existed at the end of the sample period had a compound annual return of 16.45% net of fees. -
Arbitrage Pricing Theory: Theory and Applications to Financial Data Analysis Basic Investment Equation
Risk and Portfolio Management Spring 2010 Arbitrage Pricing Theory: Theory and Applications To Financial Data Analysis Basic investment equation = Et equity in a trading account at time t (liquidation value) = + Δ Rit return on stock i from time t to time t t (includes dividend income) = Qit dollars invested in stock i at time t r = interest rate N N = + Δ + − ⎛ ⎞ Δ ()+ Δ Et+Δt Et Et r t ∑Qit Rit ⎜∑Qit ⎟r t before rebalancing, at time t t i=1 ⎝ i=1 ⎠ N N N = + Δ + − ⎛ ⎞ Δ + ε ()+ Δ Et+Δt Et Et r t ∑Qit Rit ⎜∑Qit ⎟r t ∑| Qi(t+Δt) - Qit | after rebalancing, at time t t i=1 ⎝ i=1 ⎠ i=1 ε = transaction cost (as percentage of stock price) Leverage N N = + Δ + − ⎛ ⎞ Δ Et+Δt Et Et r t ∑Qit Rit ⎜∑Qit ⎟r t i=1 ⎝ i=1 ⎠ N ∑ Qit Ratio of (gross) investments i=1 Leverage = to equity Et ≥ Qit 0 ``Long - only position'' N ≥ = = Qit 0, ∑Qit Et Leverage 1, long only position i=1 Reg - T : Leverage ≤ 2 ()margin accounts for retail investors Day traders : Leverage ≤ 4 Professionals & institutions : Risk - based leverage Portfolio Theory Introduce dimensionless quantities and view returns as random variables Q N θ = i Leverage = θ Dimensionless ``portfolio i ∑ i weights’’ Ei i=1 ΔΠ E − E − E rΔt ΔE = t+Δt t t = − rΔt Π Et E ~ All investments financed = − Δ Ri Ri r t (at known IR) ΔΠ N ~ = θ Ri Π ∑ i i=1 ΔΠ N ~ ΔΠ N ~ ~ N ⎛ ⎞ ⎛ ⎞ 2 ⎛ ⎞ ⎛ ⎞ E = θ E Ri ; σ = θ θ Cov Ri , R j = θ θ σ σ ρ ⎜ Π ⎟ ∑ i ⎜ ⎟ ⎜ Π ⎟ ∑ i j ⎜ ⎟ ∑ i j i j ij ⎝ ⎠ i=1 ⎝ ⎠ ⎝ ⎠ ij=1 ⎝ ⎠ ij=1 Sharpe Ratio ⎛ ΔΠ ⎞ N ⎛ ~ ⎞ E θ E R ⎜ Π ⎟ ∑ i ⎜ i ⎟ s = s()θ ,...,θ = ⎝ ⎠ = i=1 ⎝ ⎠ 1 N ⎛ ΔΠ ⎞ N σ ⎜ ⎟ θ θ σ σ ρ Π ∑ i j i j ij ⎝ ⎠ i=1 Sharpe ratio is homogeneous of degree zero in the portfolio weights. -
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)). -
Betting Against Alpha
Betting Against Alpha Alex R. Horenstein Department of Economics School of Business Administration University of Miami [email protected] December 11, 2017 Abstract. I sort stocks based on realized alphas estimated from the CAPM, Carhart (1997), and Fama-French Five Factor (FF5, 2015) models and nd that realized alphas are negatively related with future stock returns, future alpha, and Sharpe Ratios. Thus, I construct a Betting Against Alpha (BAA) factor that buys a portfolio of low-alpha stocks and sells a portfolio of high-alpha stocks. Using rank estimation methods, I show that the BAA factor spans a dimension of stock returns dierent than Frazzini and Pedersen's (2014) Betting Against Beta (BAB) factor. Additionally, the BAA factor captures information about the cross-section of stock returns missed by the CAPM, Carhart, and FF5 models. The performance of the BAA factor further improves if the low alpha portfolio is calculated from low beta stocks and the high alpha portfolio from high beta stocks. I call this factor Betting Against Alpha and Beta (BAAB). I discuss several reasons that support the existence of this counter-intuitive low-alpha anomaly. Keywords. betting against beta, betting against alpha, low-alpha anomaly, leverage, benchmark- ing. JEL classication. G10, G12. I thank Aurelio Vásquez, Manuel Santos, Markus Pelger and Min Ahn for their helpful comments and suggestions. 1 1 Introduction The primary purpose of this paper is to document a new anomaly and propose a new tradable factor based on it. I call this new anomaly the low-alpha anomaly: Portfolios constructed with assets having low realized alphas (overpriced according to the CAPM) have higher Sharpe Ratios, future alphas, and expected returns than portfolios constructed with assets having high-realized alphas (underpriced according to the CAPM). -
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.