Forecasting Investment Returns and Expected Return Assumptions for Pension Actuaries
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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 They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.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). -
Expected Stock Returns and Volatility Kenneth R
University of Pennsylvania ScholarlyCommons Finance Papers Wharton Faculty Research 1987 Expected Stock Returns and Volatility Kenneth R. French G. William Schwert Robert F. Stambaugh University of Pennsylvania Follow this and additional works at: http://repository.upenn.edu/fnce_papers Part of the Finance Commons, and the Finance and Financial Management Commons Recommended Citation French, K. R., Schwert, G., & Stambaugh, R. F. (1987). Expected Stock Returns and Volatility. Journal of Financial Economics, 19 (1), 3-29. http://dx.doi.org/10.1016/0304-405X(87)90026-2 At the time of publication, author Robert F. Stambaugh was affiliated with the University of Chicago. Currently, he is a faculty member at the Wharton School at the University of Pennsylvania. This paper is posted at ScholarlyCommons. http://repository.upenn.edu/fnce_papers/363 For more information, please contact [email protected]. Expected Stock Returns and Volatility Abstract This paper examines the relation between stock returns and stock market volatility. We find ve idence that the expected market risk premium (the expected return on a stock portfolio minus the Treasury bill yield) is positively related to the predictable volatility of stock returns. There is also evidence that unexpected stock market returns are negatively related to the unexpected change in the volatility of stock returns. This negative relation provides indirect evidence of a positive relation between expected risk premiums and volatility. Disciplines Finance | Finance and Financial Management Comments At the time of publication, author Robert F. Stambaugh was affiliated with the University of Chicago. Currently, he is a faculty member at the Wharton School at the University of Pennsylvania. -
Estimating Value at Risk
Estimating Value at Risk Eric Marsden <[email protected]> Do you know how risky your bank is? Learning objectives 1 Understand measures of financial risk, including Value at Risk 2 Understand the impact of correlated risks 3 Know how to use copulas to sample from a multivariate probability distribution, including correlation The information presented here is pedagogical in nature and does not constitute investment advice! Methods used here can also be applied to model natural hazards 2 / 41 Warmup. Before reading this material, we suggest you consult the following associated slides: ▷ Modelling correlations using Python ▷ Statistical modelling with Python Available from risk-engineering.org 3 / 41 Risk in finance There are 1011 stars in the galaxy. That used to be a huge number. But it’s only a hundred billion. It’s less than the national deficit! We used to call them astronomical numbers. ‘‘ Now we should call them economical numbers. — Richard Feynman 4 / 41 Terminology in finance Names of some instruments used in finance: ▷ A bond issued by a company or a government is just a loan • bond buyer lends money to bond issuer • issuer will return money plus some interest when the bond matures ▷ A stock gives you (a small fraction of) ownership in a “listed company” • a stock has a price, and can be bought and sold on the stock market ▷ A future is a promise to do a transaction at a later date • refers to some “underlying” product which will be bought or sold at a later time • example: farmer can sell her crop before harvest, -
Risk, Return, and Diversification a Reading Prepared by Pamela Peterson Drake
Risk, return, and diversification A reading prepared by Pamela Peterson Drake O U T L I N E 1. Introduction 2. Diversification and risk 3. Modern portfolio theory 4. Asset pricing models 5. Summary 1. Introduction As managers, we rarely consider investing in only one project at one time. Small businesses and large corporations alike can be viewed as a collection of different investments, made at different points in time. We refer to a collection of investments as a portfolio. While we usually think of a portfolio as a collection of securities (stocks and bonds), we can also think of a business in much the same way -- a portfolios of assets such as buildings, inventories, trademarks, patents, et cetera. As managers, we are concerned about the overall risk of the company's portfolio of assets. Suppose you invested in two assets, Thing One and Thing Two, having the following returns over the next year: Asset Return Thing One 20% Thing Two 8% Suppose we invest equal amounts, say $10,000, in each asset for one year. At the end of the year we will have $10,000 (1 + 0.20) = $12,000 from Thing One and $10,000 (1 + 0.08) = $10,800 from Thing Two, or a total value of $22,800 from our original $20,000 investment. The return on our portfolio is therefore: ⎛⎞$22,800-20,000 Return = ⎜⎟= 14% ⎝⎠$20,000 If instead, we invested $5,000 in Thing One and $15,000 in Thing Two, the value of our investment at the end of the year would be: Value of investment =$5,000 (1 + 0.20) + 15,000 (1 + 0.08) = $6,000 + 16,200 = $22,200 and the return on our portfolio would be: ⎛⎞$22,200-20,000 Return = ⎜⎟= 11% ⎝⎠$20,000 which we can also write as: Risk, return, and diversification, a reading prepared by Pamela Peterson Drake 1 ⎡⎤⎛⎞$5,000 ⎡ ⎛⎞$15,000 ⎤ Return = ⎢⎥⎜⎟(0.2) +=⎢ ⎜⎟(0.08) ⎥ 11% ⎣⎦⎝⎠$20,000 ⎣ ⎝⎠$20,000 ⎦ As you can see more immediately by the second calculation, the return on our portfolio is the weighted average of the returns on the assets in the portfolio, where the weights are the proportion invested in each asset. -
A Securitization-Based Model of Shadow Banking with Surplus Extraction and Credit Risk Transfer
A Securitization-based Model of Shadow Banking with Surplus Extraction and Credit Risk Transfer Patrizio Morganti∗ August, 2017 Abstract The paper provides a theoretical model that supports the search for yield motive of shadow banking and the traditional risk transfer view of securitization, which is consistent with the factual background that had characterized the U.S. financial system before the recent crisis. The shadow banking system is indeed an important provider of high-yield asset-backed securi- ties via the underlying securitized credit intermediation process. Investors' sentiment on future macroeconomic conditions affects the reservation prices related to the demand for securitized assets: high-willing payer (\optimistic") investors are attracted to these investment opportu- nities and offer to intermediaries a rent extraction incentive. When the outside wealth is high enough that securitization occurs, asset-backed securities are used by intermediaries to extract the highest feasible surplus from optimistic investors and to offload credit risk. Shadow banking is pro-cyclical and securitization allows risks to be spread among market participants coherently with their risk attitude. Keywords: securitization, shadow banking, credit risk transfer, surplus extraction JEL classification: E44, G21, G23 1 Introduction During the last four decades we witnessed to fundamental changes in financial techniques and financial regulation that have gradually transformed the \originate-to-hold" banking model into a \originate-to-distribute" model based on a securitized credit intermediation process relying upon i) securitization techniques, ii) securities financing transactions, and iii) mutual funds industry.1 ∗Tuscia University in Viterbo, Department of Economics and Engineering. E-mail: [email protected]. I am most grateful to Giuseppe Garofalo for his continuous guidance and support. -
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 ....................................................................................................................... -
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 Cross-Section of Volatility and Expected Returns
THE JOURNAL OF FINANCE • VOL. LXI, NO. 1 • FEBRUARY 2006 The Cross-Section of Volatility and Expected Returns ANDREW ANG, ROBERT J. HODRICK, YUHANG XING, and XIAOYAN ZHANG∗ ABSTRACT We examine the pricing of aggregate volatility risk in the cross-section of stock returns. Consistent with theory, we find that stocks with high sensitivities to innovations in aggregate volatility have low average returns. Stocks with high idiosyncratic volatility relative to the Fama and French (1993, Journal of Financial Economics 25, 2349) model have abysmally low average returns. This phenomenon cannot be explained by exposure to aggregate volatility risk. Size, book-to-market, momentum, and liquidity effects cannot account for either the low average returns earned by stocks with high exposure to systematic volatility risk or for the low average returns of stocks with high idiosyncratic volatility. IT IS WELL KNOWN THAT THE VOLATILITY OF STOCK RETURNS varies over time. While con- siderable research has examined the time-series relation between the volatility of the market and the expected return on the market (see, among others, Camp- bell and Hentschel (1992) and Glosten, Jagannathan, and Runkle (1993)), the question of how aggregate volatility affects the cross-section of expected stock returns has received less attention. Time-varying market volatility induces changes in the investment opportunity set by changing the expectation of fu- ture market returns, or by changing the risk-return trade-off. If the volatility of the market return is a systematic risk factor, the arbitrage pricing theory or a factor model predicts that aggregate volatility should also be priced in the cross-section of stocks. -
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.