How to Know What Rate of Return to Expect from Your Stocks: Part 1
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MPFD Lesson 2B: Meeting Financial Goals—Rate of Return
Unit 2 Planning and Tracking Lesson 2B: Meeting Financial Goals—Rate of Return Rule 2: Have a Plan. Financial success depends primarily on two things: (i) developing a plan to meet your established goals and (ii) tracking your progress with respect to that plan. Too often peo - ple set vague goals (“I want to be rich.”), make unrealistic plans, or never bother to assess the progress toward their goals. These lessons look at important financial indicators you should understand and monitor both in setting goals and attaining them. Lesson Description Students are shown the two ways investments can earn a return and then calculate the annual rate of return, the real rate of return, and the expected rate of return on various assets. Standards and Benchmarks (see page 49) Grade Level 9-12 Concepts Appreciation Depreciation Expected rate of return Inflation Inflation rate Rate of return Real rate of return Return Making Personal Finance Decisions ©2019, Minnesota Council on Economic Education. Developed in partnership with the Federal Reserve Bank of St. Louis. Permission is granted to reprint or photocopy this lesson in its entirety for educational purposes, provided the user credits the Minnesota Council on Economic Education. 37 Unit 2: Planning and Tracking Lesson 2B: Meeting Financial Goals—Rate of Return Compelling Question How is an asset’s rate of return measured? Objectives Students will be able to • describe two ways that an asset can earn a return and • determine and distinguish among the rate of return on an asset, its real rate of return, and its expected rate of return. -
The Wisdom of the Robinhood Crowd
NBER WORKING PAPER SERIES THE WISDOM OF THE ROBINHOOD CROWD Ivo Welch Working Paper 27866 http://www.nber.org/papers/w27866 NATIONAL BUREAU OF ECONOMIC RESEARCH 1050 Massachusetts Avenue Cambridge, MA 02138 September 2020, Revised December 2020 The views expressed herein are those of the author and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications. © 2020 by Ivo Welch. All rights reserved. Short sections of text, not to exceed two paragraphs, may be quoted without explicit permission provided that full credit, including © notice, is given to the source. The Wisdom of the Robinhood Crowd Ivo Welch NBER Working Paper No. 27866 September 2020, Revised December 2020 JEL No. D9,G11,G4 ABSTRACT Robinhood (RH) investors collectively increased their holdings in the March 2020 COVID bear market, indicating an absence of panic and margin calls. Their steadfastness was rewarded in the subsequent bull market. Despite unusual interest in some “experience” stocks, their aggregated consensus portfolio (likely mimicking the household-equal-weighted portfolio) primarily tilted towards stocks with high past share volume and dollar-trading volume. These were mostly big stocks. Both their timing and their consensus portfolio performed well from mid-2018 to mid-2020. Ivo Welch Anderson School at UCLA (C519) 110 Westwood Place (951481) Los Angeles, CA 90095-1482 and NBER [email protected] The online retail brokerage company Robinhood (RH) was founded in 2013 based on a business plan to make it easier and cheaper for small investors to participate in the stock and option markets. -
An Overview of the Empirical Asset Pricing Approach By
AN OVERVIEW OF THE EMPIRICAL ASSET PRICING APPROACH BY Dr. GBAGU EJIROGHENE EMMANUEL TABLE OF CONTENT Introduction 1 Historical Background of Asset Pricing Theory 2-3 Model and Theory of Asset Pricing 4 Capital Asset Pricing Model (CAPM): 4 Capital Asset Pricing Model Formula 4 Example of Capital Asset Pricing Model Application 5 Capital Asset Pricing Model Assumptions 6 Advantages associated with the use of the Capital Asset Pricing Model 7 Hitches of Capital Pricing Model (CAPM) 8 The Arbitrage Pricing Theory (APT): 9 The Arbitrage Pricing Theory (APT) Formula 10 Example of the Arbitrage Pricing Theory Application 10 Assumptions of the Arbitrage Pricing Theory 11 Advantages associated with the use of the Arbitrage Pricing Theory 12 Hitches associated with the use of the Arbitrage Pricing Theory (APT) 13 Actualization 14 Conclusion 15 Reference 16 INTRODUCTION This paper takes a critical examination of what Asset Pricing is all about. It critically takes an overview of its historical background, the model and Theory-Capital Asset Pricing Model and Arbitrary Pricing Theory as well as those who introduced/propounded them. This paper critically examines how securities are priced, how their returns are calculated and the various approaches in calculating their returns. In this Paper, two approaches of asset Pricing namely Capital Asset Pricing Model (CAPM) as well as the Arbitrage Pricing Theory (APT) are examined looking at their assumptions, advantages, hitches as well as their practical computation using their formulae in their examination as well as their computation. This paper goes a step further to look at the importance Asset Pricing to Accountants, Financial Managers and other (the individual investor). -
Portfolio Construction + Management with Factset
www.factset.com Truly Active Management Requires a Commitment to Excellence: PORTFOLIO CONSTRUCTION + MANAGEMENT WITH FACTSET Bijan Beheshti, John Guerard, and Chris Mercs Truly Active Management Requires a Commitment to Excellence: Portfolio Construction and Management with FactSet1 Bijan Beheshti FactSet Research Systems Inc. San Francisco, CA John Guerard McKinley Capital Management, LLC Anchorage, AK 99503 ([email protected]) and Chris Mercs FactSet Research Systems Inc. San Francisco, CA February 2020 This paper is forthcoming in John Guerard and William T. Ziemba, Editors, Handbook of Applied Investment Research (Singapore: World Scientific Publishing, 2020). 1 The authors thank Ross Sharp, formerly of FactSet, who created the R-robust regression script. Truly Active Management Requires a Commitment to Excellence: Portfolio Construction and Management with FactSet Financial anomalies have been studied in the U.S. and recent evidence suggests that they have diminished in the U.S. and possibly in non-U.S. portfolios. Have the anomalies changed and are they persistent? Have historical and earnings forecasting data been a consistent and highly statistically significant source of excess returns? We test many financial anomalies of the 1980s-1990s and report that several models and strategies continue to produce statistically significant excess returns. We also test a large set of U.S. and global variables over the past 16 years and report that many of these fundamental, earnings forecasts, revisions, breadth and momentum, and cash deployment strategies maintained their statistical significance during the 2003-2018 time period. Moreover, the earnings forecasting model and robust regression estimated composite model excess returns are greater in Non-U.S. -
Equity-Bond Yield Correlation and the FED Model: Evidence of Switching Behaviour from the G7 Markets
This is a post-peer-review, pre-copyedit version of an article published in Journal of Asset Management. The final authenticated version is available online at: https://doi.org/10.1057/s41260- 018-0091-x Equity-Bond Yield Correlation and the FED Model: Evidence of Switching Behaviour from the G7 Markets February 2018 Revised April 2018 This Version June 2018 Abstract This paper considers how the strength and nature of the relation between the equity and bond yield varies with the level of the real bond yield. We demonstrate that at low levels of the real bond yield, the correlation between the equity and bond yields turns negative. This arises as the lower bond yield implies heightened macroeconomic risk (e.g., deflation and economic stagnation) and causes equity and bond prices to move in opposite directions. The FED model relies on a positive relation for its success in predicting future returns. Thus, we argue that the mixed empirical evidence regarding the FED model arises due to this switch in correlation behaviour. We present supportive evidence for the switching relation and its link to the level of the bond yield using linear and non-linear smooth transition panel regression techniques for the G7 markets. The results presented here should be of interest to market practitioners who may wish to use the FED model to aid market timing decisions and for academics interested in understanding the interrelations between markets. Keywords: Equity Returns, Bond Returns, Correlation, Bond Yield, Switching JEL: C22, C23, E44, G12, G15 1 1. Introduction. The FED model implies a positive relation between the equity and bond yields. -
The Predictive Ability of the Bond-Stock Earnings Yield Differential Model
The Predictive Ability of the Bond-Stock Earnings Yield Differential Model KLAUS BERGE,GIORGIO CONSIGLI, AND WILLIAM T. ZIEMBA FORMAT ANY IN KLAUS BERGE he Federal Reserve (Fed) model prices and stock indices has been studied by is a financial controller provides a framework for discussing Campbell [1987, 1990, 1993]; Campbell and for Allianz SE in Munich, stock market over- and undervalu- Shiller [1988]; Campbell and Yogo [2006]; Germany. [email protected] ation. It was introduced by market Fama and French [1988a, 1989]; Goetzmann Tpractitioners after Alan Greenspan’s speech on and Ibbotson [2006]; Jacobs and Levy [1988]; GIORGIO CONSIGLI the market’s irrational exuberance in ARTICLELakonishok, Schleifer, and Vishny [1994]; Polk, is an associate professor in November 1996 as an attempt to understand Thompson, and Vuolteenaho [2006], and the Department of Mathe- and predict variations in the equity risk pre- Ziemba and Schwartz [1991, 2000]. matics, Statistics, and Com- mium (ERP). The model relates the yield on The Fed model has been successful in puter Science at the THIS University of Bergamo stocks (measured by the ratio of earnings to predicting market turns, but in spite of its in Bergamo, Italy. stock prices) to the yield on nominal Treasury empirical success and simplicity, the model has [email protected] bonds. The theory behind the Fed model is been criticized. First, it does not consider the that an optimal asset allocation between stocks role played by time-varying risk premiums in WILLIAM T. Z IEMBA and bonds is related to their relative yields and the portfolio selection process, yet it does con- is Alumni Professor of Financial Modeling and when the bond yield is too high, a market sider a risk-free government interest rate as the Stochastic Optimization, adjustment is needed resulting in a shift out of discount factor of future earnings. -
Dividend Valuation Models Prepared by Pamela Peterson Drake, Ph.D., CFA
Dividend valuation models Prepared by Pamela Peterson Drake, Ph.D., CFA Contents 1. Overview ..................................................................................................................................... 1 2. The basic model .......................................................................................................................... 1 3. Non-constant growth in dividends ................................................................................................. 5 A. Two-stage dividend growth ...................................................................................................... 5 B. Three-stage dividend growth .................................................................................................... 5 C. The H-model ........................................................................................................................... 7 4. The uses of the dividend valuation models .................................................................................... 8 5. Stock valuation and market efficiency ......................................................................................... 10 6. Summary .................................................................................................................................. 10 7. Index ........................................................................................................................................ 11 8. Further readings ....................................................................................................................... -
Thinking Differently About Dividends Dividendspersp 4/25/03 1:40 PM Page 3
DividendsPersp 4/25/03 1:40 PM Page 1 Perspectives Thinking Differently About Dividends DividendsPersp 4/25/03 1:40 PM Page 3 Thinking Differently About Dividends Many senior executives view dividends as a low priority on the strategic agenda. They’re wrong. The unique set of circumstances that made dividends unfashionable during the long bull market of the 1980s and 1990s is fast disappearing. In the current economic envi- ronment, dividends are an especially impor- tant lever for generating above-average shareholder returns. That’s not to say that every company should increase its dividend yield—or even pay divi- dends at all. But every company should revisit its dividend policy. Doing so can greatly improve the strategy debate among a com- pany’s senior managers—whether they ulti- mately decide to increase the company’s dividend or not. Why Dividends Are Back in Fashion In the recent bull market, the practice of returning cash to investors through divi- dends was easy to dismiss. One familiar disin- centive was the double taxation of dividends according to U.S. tax law. Another was the fact that as executives were compensated increas- ingly through stock options, they were better off using excess cash to repurchase shares DividendsPersp 4/25/03 1:40 PM Page 4 (thus raising the value of options) rather than returning that cash to investors through dividends. Trends in the financial markets also dis- couraged dividends. With average total share- holder return (TSR) running in the high teens, a dividend yield of even 3 or 4 percent was a relatively minor contributor to achieving above-average TSR. -
Earnings Yield As a Predictor of Return on Assets, Return on Equity, Economic Value Added and the Equity Multiplier
Modern Economy, 2017, 8, 10-24 http://www.scirp.org/journal/me ISSN Online: 2152-7261 ISSN Print: 2152-7245 Earnings Yield as a Predictor of Return on Assets, Return on Equity, Economic Value Added and the Equity Multiplier Rebecca Abraham, Judith Harris, Joel Auerbach Huizenga College of Business, Nova Southeastern University, Fort Lauderdale, Florida, USA How to cite this paper: Abraham, R., Abstract Harris, J. and Auerbach, J. (2017) Earnings Yield as a Predictor of Return on Assets, This study identifies earnings yield as a measure of financial performance that Return on Equity, Economic Value Added is based on a firm’s ability to sell profitable goods. It excludes the irrationality and the Equity Multiplier. Modern Econo- that can confound market-based measures of financial performance by em- my, 8, 10-24. http://dx.doi.org/10.4236/me.2017.81002 phasizing a firm’s ability to earn profits as the indicator of superior perfor- mance. For the full sample, the differential effects of earnings yield on return Received: December 5, 2016 on assets, return on equity, stock returns, economic value added and the eq- Accepted: January 6, 2017 uity multiplier are determined for firms of different size and volatility. The Published: January 9, 2017 analysis is conducted both across industries and within the oil and gas, com- Copyright © 2017 by authors and puter software, biotechnology and retail industries. For the full sample of Scientific Research Publishing Inc. NASDAQ stocks from 2010-2014, earnings yield significantly explained re- This work is licensed under the Creative turn on assets, return on equity, stock returns, economic value added and the Commons Attribution International License (CC BY 4.0). -
1 Factor Models a (Linear) Factor Model Assumes That the Rate of Return of an Asset Is Given By
1Factor Models The Markowitz mean-variance framework requires having access to many parameters: If there are n risky assets, with rates of return ri,i=1, 2,...,n, then we must know 2 − all the n means (ri), n variances (σi ) and n(n 1)/2covariances (σij) for a total of 2n + n(n − 1)/2 parameters. If for example n = 100 we would need 4750 parameters, and if n = 1000 we would need 501, 500 parameters! At best we could try to estimate these, but how? In fact, it is easy to see that trying to estimate the means, for example, to a workable level of accuracy is almost impossible using historical (e.g., past) data over time.1 What happens is that the standard deviation of our estimate is too large (for example larger than the estimate itself), thus rendering the estimate worthless. One can bring the standard deviation down only by increasing the data to go back to (say) over ahundred years! To see this: If we want to estimate expected rate of return over a typical 1-month period we could take n monthly data points r(1),...,r(n) denoting the rate of return over individual months in the past, and then average 1 n r(j). (1) n j=1 This estimate (assuming independent and identically distributed (iid) returns over months) has a mean√ and standard deviation given by r, σ/ n respectively, where r and σ denote the true mean and standard deviation over 1-month. If, for example, a stock’s yearly expected rate of return is 16%, then the monthly such rate is r =16/12=1.333% = 0.0133. -
The Underpricing of Ipos on the Stock Exchange of Mauritius
The underpricing of IPOs on the stock exchange of Mauritius Article Accepted Version Agathee, U. S., Sannassee, R. V. and Brooks, C. (2012) The underpricing of IPOs on the stock exchange of Mauritius. Research in International Business and Finance, 26. pp. 281- 303. ISSN 0275-5319 doi: https://doi.org/10.1016/j.ribaf.2012.01.001 Available at http://centaur.reading.ac.uk/26244/ It is advisable to refer to the publisher’s version if you intend to cite from the work. See Guidance on citing . To link to this article DOI: http://dx.doi.org/10.1016/j.ribaf.2012.01.001 Publisher: Elsevier All outputs in CentAUR are protected by Intellectual Property Rights law, including copyright law. Copyright and IPR is retained by the creators or other copyright holders. Terms and conditions for use of this material are defined in the End User Agreement . www.reading.ac.uk/centaur CentAUR Central Archive at the University of Reading Reading’s research outputs online NOTICE: this is the author’s version of a work that was accepted for publication in Research in International Business and Finance. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Research in International Business and Finance, 26.2 (2012), DOI: 10.1016/j.ribaf.2012.01.001 € The Underpricing of IPOs on the Stock Exchange of Mauritius Ushad Subadar Agathee Department of Finance and Accounting, Faculty of Law and Management, University of Mauritius. -
The Mincer Equation and Beyond1
Chapter 7 EARNINGS FUNCTIONS, RATES OF RETURN AND TREATMENT EFFECTS: THE MINCER EQUATION AND BEYOND1 JAMES J. HECKMAN Department of Economics, University of Chicago, 1126 East 59th Street, Chicago, IL 60637, USA e-mail: [email protected] LANCE J. LOCHNER Department of Economics, University of Western Ontario, 1151 Richmond Street N, London, ON, N6A 5C2, Canada e-mail: [email protected] PETRA E. TODD Department of Economics, University of Pennsylvania, 20 McNeil, 3718 Locust Walk, Philadelphia, PA 19104, USA e-mail: [email protected] Contents Abstract 310 Keywords 310 1. Introduction 311 2. The theoretical foundations of Mincer’s earnings regression 315 2.1. The compensating differences model 315 2.2. The accounting-identity model 316 Implications for log earnings–age and log earnings–experience profiles and for the inter- personal distribution of life-cycle earnings 318 1 Heckman is Henry Schultz Distinguished Service Professor of Economics at the University of Chicago and Distinguished Professor of Science and Society, University College Dublin. Lochner is Associate Professor of Economics at the University of Western Ontario. Todd is Professor of Economics at the University of Pennsylvania. The first part of this chapter was prepared in June 1998. It previously circulated under the title “Fifty Years of Mincer Earnings Regressions”. Heckman’s research was supported by NIH R01-HD043411, NSF 97-09-873, NSF SES-0099195 and NSF SES-0241858. We thank Christian Belzil, George Borjas, Pedro Carneiro, Flavio Cunha, Jim Davies, Reuben Gronau, Eric Hanushek, Lawrence Katz, John Knowles, Mario Macis, Derek Neal, Aderonke Osikominu, Dan Schmierer, Jora Stixrud, Ben Williams, Kenneth Wolpin, and participants at the 2001 AEA Annual Meeting, the Labor Studies Group at the 2001 NBER Summer Institute, and participants at Stanford University and Yale University seminars for helpful comments.