Oklahoma State University Spears School of Business. Risk & Return

Similar documents
Lecture 4. Risk and Return: Lessons from Market History

Lesson 4. Capital market theory: an overview

Ch. 8 Risk and Rates of Return. Return, Risk and Capital Market. Investment returns

Risk and Return. 9.1 Returns. Lessons from Market History PART THREE CHAPTER. Dollar Returns

1 A Brief History of. Chapter. Risk and Return. Dollar Returns. PercentReturn. Learning Objectives. A Brief History of Risk and Return

An investment s return is your reward for investing. An investment s risk is the uncertainty of what will happen with your investment dollar.

CHAPTER 1 A Brief History of Risk and Return

AN INTRODUCTION TO RISK AND RETURN. Chapter 7

CHAPTER 5. Introduction to Risk, Return, and the Historical Record INVESTMENTS BODIE, KANE, MARCUS. McGraw-Hill/Irwin

10. Lessons From Capital Market History

Measures of Variation. Section 2-5. Dotplots of Waiting Times. Waiting Times of Bank Customers at Different Banks in minutes. Bank of Providence

Making Sense of Cents

AP Statistics Chapter 6 - Random Variables

Introduction To Risk & Return

Principles of Finance Risk and Return. Instructor: Xiaomeng Lu

Valuing Stock Options: The Black-Scholes-Merton Model. Chapter 13

Math 140 Introductory Statistics. First midterm September

Quarterly Market Review Market Slides. Fourth Quarter 2015

Chapter 1 A Brief History of Risk and Return

Math 243 Lecture Notes

1/12/2011. Chapter 5: z-scores: Location of Scores and Standardized Distributions. Introduction to z-scores. Introduction to z-scores cont.

As you draw random samples of size n, as n increases, the sample means tend to be normally distributed.

FNCE 4030 Fall 2012 Roberto Caccia, Ph.D. Midterm_2a (2-Nov-2012) Your name:

Ibbotson SBBI 2009 Valuation Yearbook. Market Results for Stocks, Bonds, Bills, and Inflation

Section Introduction to Normal Distributions

Inputs Methodology. Portfolio Strategist

Important Concepts LECTURE 3.2: OPTION PRICING MODELS: THE BLACK-SCHOLES-MERTON MODEL. Applications of Logarithms and Exponentials in Finance

Statistics vs. statistics

Continuous Probability Distributions

Measuring Risk. Expected value and expected return 9/4/2018. Possibilities, Probabilities and Expected Value

Chapter 7 1. Random Variables

Measure of Variation

CHAPTER 5. Introduction to Risk, Return, and the Historical Record INVESTMENTS BODIE, KANE, MARCUS. McGraw-Hill/Irwin

Chapter 7: SAMPLING DISTRIBUTIONS & POINT ESTIMATION OF PARAMETERS

NORMAL RANDOM VARIABLES (Normal or gaussian distribution)

Paul D. Kaplan, Ph.D., CFA Quantitative Research Director, Morningstar Europe, Ltd.

For 9.220, Term 1, 2002/03 02_Lecture12.ppt Student Version. What is risk? An overview of market performance Measuring performance

1 Describing Distributions with numbers

8.1 Estimation of the Mean and Proportion

Chapter 3. Numerical Descriptive Measures. Copyright 2016 Pearson Education, Ltd. Chapter 3, Slide 1

CHAPTER 5. Introduction to Risk, Return, and the Historical Record INVESTMENTS BODIE, KANE, MARCUS

Business Statistics 41000: Probability 3

MidTerm 1) Find the following (round off to one decimal place):

Section 6.5. The Central Limit Theorem

Mr. Orchard s Math 141 WIR 8.5, 8.6, 5.1 Week 13

Chapter 2: Descriptive Statistics. Mean (Arithmetic Mean): Found by adding the data values and dividing the total by the number of data.

BOND ANALYTICS. Aditya Vyas IDFC Ltd.

FINC 430 TA Session 7 Risk and Return Solutions. Marco Sammon

Refer to Ex 3-18 on page Record the info for Brand A in a column. Allow 3 adjacent other columns to be added. Do the same for Brand B.

Biostatistics and Design of Experiments Prof. Mukesh Doble Department of Biotechnology Indian Institute of Technology, Madras

Chapter 4 Continuous Random Variables and Probability Distributions

FEEG6017 lecture: The normal distribution, estimation, confidence intervals. Markus Brede,

Chapter 10: Capital Markets and the Pricing of Risk

STAT:2010 Statistical Methods and Computing. Using density curves to describe the distribution of values of a quantitative

Chapter 4 Variability

Lecture 18 Section Mon, Feb 16, 2009

Lecture 18 Section Mon, Sep 29, 2008

STAT Chapter 6 The Standard Deviation (SD) as a Ruler and The Normal Model

STAT Chapter 6 The Standard Deviation (SD) as a Ruler and The Normal Model

6 Central Limit Theorem. (Chs 6.4, 6.5)

Chapter 12. Some Lessons from Capital Market History. Dongguk University, Prof. Sun-Joong Yoon

Estimation and Confidence Intervals

Lecture 9. Probability Distributions. Outline. Outline

6.2 Normal Distribution. Normal Distributions

State of Alaska Department of Revenue. Alaska Retirement Management Board

Lecture 9. Probability Distributions

Notes: Review of Future & Present Value, Some Statistics & Calculating Security Returns

Chapter 4 Continuous Random Variables and Probability Distributions

Diversification. Finance 100

MA131 Lecture 8.2. The normal distribution curve can be considered as a probability distribution curve for normally distributed variables.

The Binomial Probability Distribution

3.1 Measures of Central Tendency

A continuous random variable is one that can theoretically take on any value on some line interval. We use f ( x)

CHAPTER 9 SOME LESSONS FROM CAPITAL MARKET HISTORY

Chapter 7 Study Guide: The Central Limit Theorem

4.2 Probability Distributions

Random Variables Part 2

Section 6.3b The Binomial Distribution

Simple Descriptive Statistics

2. The sum of all the probabilities in the sample space must add up to 1

MA 1125 Lecture 14 - Expected Values. Wednesday, October 4, Objectives: Introduce expected values.

ECON 214 Elements of Statistics for Economists 2016/2017

VARIABILITY: Range Variance Standard Deviation

Population Mean GOALS. Characteristics of the Mean. EXAMPLE Population Mean. Parameter Versus Statistics. Describing Data: Numerical Measures

Point Estimation. Stat 4570/5570 Material from Devore s book (Ed 8), and Cengage

INTRODUCTION TO PORTFOLIO ANALYSIS. Dimensions of Portfolio Performance

PSYCHOLOGICAL STATISTICS

Frequency Distribution Models 1- Probability Density Function (PDF)

CHAPTER 6 Random Variables

In terms of covariance the Markowitz portfolio optimisation problem is:

Measures of Dispersion (Range, standard deviation, standard error) Introduction

Risk and Return. Gestão Financeira II Undergraduate Courses Gestão Financeira II Licenciatura Clara Raposo

Standard Deviation. Lecture 18 Section Robb T. Koether. Hampden-Sydney College. Mon, Sep 26, 2011

ECON 214 Elements of Statistics for Economists

Computing Statistics ID1050 Quantitative & Qualitative Reasoning

CHAPTER 5 SAMPLING DISTRIBUTIONS

2 DESCRIPTIVE STATISTICS

Lecture 2 INTERVAL ESTIMATION II

II. Random Variables

3 3 Measures of Central Tendency and Dispersion from grouped data.notebook October 23, 2017

Transcription:

Oklahoma State University Spears School of Business Risk & Return

Slide 2 Returns Dollar Returns the sum of the cash received and the change in value of the asset, in dollars. Dividends Ending market value Time 0 1 Percentage Returns Initial investment the sum of the cash received and the change in value of the asset divided by the initial investment.

Slide 3 Returns Dollar Return = Dividend + Change in Market Value percentage return = dollar return beginning market value = dividend+ change in market value beginning market value = dividend yield + capital gains yield

Slide 4 Returns: Example Suppose you bought 100 shares of Wal-Mart (WMT) one year ago today at $25. Over the last year, you received $20 in dividends (20 cents per share 100 shares). At the end of the year, the stock sells for $30. How did you do? Quite well. You invested $25 100 = $2,500. At the end of the year, you have stock worth $3,000 and cash dividends of $20. Your dollar gain was $520 = $20 + ($3,000 $2,500). $520 Your percentage gain for the year is:20.8% = $2,500

Slide 5 Returns: Example Dollar Return: $520 gain $20 $3,000 Time 0 1 Percentage Return: -$2,500 $520 20.8% = $2,500

Slide 6 Holding Period Returns The holding period return is the return that an investor would get when holding an investment over a period of n years, when the return during year iis given as r i : holding period return = = (1+ r ) (1+ r ) L (1+ r ) 1 1 2 n

Slide 7 Holding Period Return: Example Suppose your investment provides the following returns over a four-year period: Year Return Your holding period return= 1 10% = (1+ r1 ) (1+ r2 ) (1+ r3 ) (1+ r4 ) 2-5% = (1.10) (.95) (1.20) (1.15) 1 3 20% 4 15% =.4421= 44.21% 1

Slide 8 Holding Period Returns A famous set of studies dealing with rates of returns on common stocks, bonds, and Treasury bills was conducted by Roger Ibbotson and Rex Sinquefield. They present year-by-year historical rates of return starting in 1926 for the following five important types of financial instruments in the United States: Large-company Common Stocks Small-company Common Stocks Long-term Corporate Bonds Long-term U.S. Government Bonds U.S. Treasury Bills

Slide 9 Return Statistics The history of capital market returns can be summarized by describing the: average return ( R RT ) R 1 + L+ = T the standard deviation of those returns SD= VAR = ( R 1 R) the frequency distribution of the returns 2 + ( R 2 R) T 1 2 + L( R T R) 2

Slide 10 Historical Returns, 1926-2004 Average Standard Series Annual Return Deviation Distribution Large Company Stocks 12.3% 20.2% Small Company Stocks 17.4 32.9 Long-Term Corporate Bonds 6.2 8.5 Long-Term Government Bonds 5.8 9.2 U.S. Treasury Bills 3.8 3.1 Inflation 3.1 4.3 90% 0% + 90% Source: Stocks, Bonds, Bills, and Inflation 2006 Yearbook, Ibbotson Associates, Inc., Chicago (annually updates work by Roger G. Ibbotson and Rex A. Sinquefield). All rights reserved.

Slide 11 Stock Returns and Risk-Free Returns The Risk Premiumis the added return (over and above the risk-free rate) resulting from bearing risk. One of the most significant observations of stock market data is the long-run excess of stock return over the risk-free return. The average excess return from large company common stocks for the period 1926 through 2005 was: 8.5% = 12.3% 3.8% The average excess return from small company common stocks for the period 1926 through 2005 was: 13.6% = 17.4% 3.8% The average excess return from long-term corporate bonds for the period 1926 through 2005 was: 2.4% = 6.2% 3.8%

Slide 12 Risk Premia Suppose that The Wall Street Journalannounced that the current rate for one-year Treasury bills is 5%. What is the expected return on the market of smallcompany stocks? Recall that the average excess return on small company common stocks for the period 1926 through 2005 was 13.6%. Given a risk-free rate of 5%, we have an expected return on the market of small-company stocks of 18.6% = 13.6% + 5%

Slide 13 The Risk-Return Tradeoff 18% 16% Small-Company Stocks Annual Return Average 14% 12% 10% 8% 6% 4% 2% Large-Company Stocks T-Bonds T-Bills 0% 5% 10% 15% 20% 25% 30% 35% Annual Return Standard Deviation

Slide 14 Risk Statistics There is no universally agreed-upon definition of risk. The measures of risk that we discuss are variance and standard deviation. The standard deviation is the standard statistical measure of the spread of a sample, and it will be the measure we use most of this time. Its interpretation is facilitated by a discussion of the normal distribution.

Slide 15 Normal Distribution A large enough sample drawn from a normal distribution looks like a bell-shaped curve. Probability The probability that a yearly return will fall within 20.2 percent of the mean of 12.3 percent will be approximately 2/3. 3σ 48.3% 2σ 28.1% 1σ 7.9% 0 12.3% 68.26% + 1σ 32.5% + 2σ 52.7% + 3σ 72.9% Return on large company common stocks 95.44% 99.74%

Slide 16 Normal Distribution The 20.2% standard deviation we found for large stock returns from 1926 through 2005 can now be interpreted in the following way: if stock returns are approximately normally distributed, the probability that a yearly return will fall within 20.2 percent of the mean of 12.3% will be approximately 2/3.

Slide 17 Example Return and Variance Year Actual Return Average Return Deviation from the Mean Squared Deviation 1.15.105.045.002025 2.09.105 -.015.000225 3.06.105 -.045.002025 4.12.105.015.000225 Totals.00.0045 Variance =.0045 / (4-1) =.0015 Standard Deviation =.03873

Slide 18 More on Average Returns Arithmetic average return earned in an average period over multiple periods Geometric average averagecompound return per period over multiple periods The geometric average will be less than the arithmetic average unless all the returns are equal. Which is better? The arithmetic average is overly optimistic for long horizons. The geometric average is overly pessimistic for short horizons.

Slide 19 Geometric Return: Example Recall our earlier example: Year Return Geometric average return= 1 10% 4 (1+ rg ) = (1+ r1 ) (1+ r2 ) (1+ r3 ) (1+ 2-5% 4 3 20% rg = (1.10) (.95) (1.20) (1.15) 1 4 15% =.095844= 9.58% So, our investor made an average of 9.58% per year, realizing a holding period return of 44.21%. 1.4421= 4 (1.095844) r 4 )

Slide 20 Geometric Return: Example Note that the geometric average is not the same as the arithmetic average: Year Return 1 10% 2-5% 3 20% 4 15% r1 + r2 + r3 + r4 Arithmetic average return= 4 10% 5% + 20% + 15% = = 10% 4

Slide 21 Forecasting Return To address the time relation in forecasting returns, use Blume s formula: T 1 T R ( T ) = GeometricA verage + 1 1 Arithmetic Average where, Tis the forecast horizon and Nis the number of years of historical data we are working with. Tmust be less than N.