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The Efficient Market Hypothesis

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Presentation on theme: "The Efficient Market Hypothesis"— Presentation transcript:

1 The Efficient Market Hypothesis
CHAPTER 11

2 Efficient Market Hypothesis (EMH)
Do security prices reflect information ? Why look at market efficiency? Implications for business and corporate finance Implications for investment Bahattin Buyuksahin, JHU, Investment

3 Figure 11.1 Cumulative Abnormal Returns Before Takeover Attempts: Target Companies
Bahattin Buyuksahin, JHU, Investment

4 Figure 11.2 Stock Price Reaction to CNBC Reports
Bahattin Buyuksahin, JHU, Investment

5 EMH and Competition Stock prices fully and accurately reflect publicly available information Once information becomes available, market participants analyze it Competition assures prices reflect information Bahattin Buyuksahin, JHU, Investment

6 Versions of the EMH Weak Semi-strong Strong
Bahattin Buyuksahin, JHU, Investment

7 Types of Stock Analysis
Technical Analysis - using prices and volume information to predict future prices Weak form efficiency & technical analysis Fundamental Analysis - using economic and accounting information to predict stock prices Semi strong form efficiency & fundamental analysis Bahattin Buyuksahin, JHU, Investment

8 Active or Passive Management
Active Management Security analysis Timing Passive Management Buy and Hold Index Funds Bahattin Buyuksahin, JHU, Investment

9 Market Efficiency & Portfolio Management
Even if the market is efficient a role exists for portfolio management: Appropriate risk level Tax considerations Other considerations Bahattin Buyuksahin, JHU, Investment

10 Event Studies Empirical financial research that enables an observer to assess the impact of a particular event on a firm’s stock price Abnormal return due to the event is estimated as the difference between the stock’s actual return and a proxy for the stock’s return in the absence of the event Bahattin Buyuksahin, JHU, Investment

11 How Tests Are Structured
Returns are adjusted to determine if they are abnormal Market Model approach a. rt = at + brmt + et (Expected Return) b. Excess Return = (Actual - Expected) et = rt - (a + brMt) Bahattin Buyuksahin, JHU, Investment

12 Are Markets Efficient Magnitude Issue Selection Bias Issue
Lucky Event Issue Bahattin Buyuksahin, JHU, Investment

13 Weak-Form Tests Returns over the Short Horizon Momentum
Returns over Long Horizons Bahattin Buyuksahin, JHU, Investment

14 Predictors of Broad Market Returns
Fama and French Aggregate returns are higher with higher dividend ratios Campbell and Shiller Earnings yield can predict market returns Keim and Stambaugh Bond spreads can predict market returns Bahattin Buyuksahin, JHU, Investment

15 Semistrong Tests: Anomalies
P/E Effect Small Firm Effect (January Effect) Neglected Firm Effect and Liquidity Effects Book-to-Market Ratios Post-Earnings Announcement Price Drift Bahattin Buyuksahin, JHU, Investment

16 Figure 11.3 Average Annual Return for 10 Size-Based Portfolios, 1926 – 2006
Bahattin Buyuksahin, JHU, Investment

17 Figure 11.4 Average Return as a Function of Book-To-Market Ratio, 1926–2006
Bahattin Buyuksahin, JHU, Investment

18 Figure 11.5 Cumulative Abnormal Returns in Response to Earnings Announcements
Bahattin Buyuksahin, JHU, Investment

19 Strong-Form Tests: Inside Information
The ability of insiders to trade profitability in their own stock has been documented in studies by Jaffe, Seyhun, Givoly, and Palmon SEC requires all insiders to register their trading activity Bahattin Buyuksahin, JHU, Investment

20 Interpreting the Evidence
Risk Premiums or market inefficiencies— disagreement here Fama and French argue that these effects can be explained as manifestations of risk stocks with higher betas Lakonishok, Shleifer, and Vishney argue that these effects are evidence of inefficient markets Bahattin Buyuksahin, JHU, Investment

21 Figure 11.6 Returns to Style Portfolio as a Predictor of GDP Growth
Bahattin Buyuksahin, JHU, Investment

22 Interpreting the Evidence Continued
Anomalies or Data Mining The noisy market hypothesis Fundamental indexing Bahattin Buyuksahin, JHU, Investment

23 Stock Market Analysts Do Analysts Add Value Mixed evidence
Ambiguity in results Bahattin Buyuksahin, JHU, Investment

24 Mutual Fund Performance
Some evidence of persistent positive and negative performance Potential measurement error for benchmark returns Style changes May be risk premiums Hot hands phenomenon Bahattin Buyuksahin, JHU, Investment

25 Figure 11.7 Estimates of Individual Mutual Fund Alphas, 1972 - 1991
Bahattin Buyuksahin, JHU, Investment

26 Table 11.1 Performance of Mutual Funds Based on Three-Index Model
Bahattin Buyuksahin, JHU, Investment

27 Figure 11.8 Persistence of Mutual Fund Performance
Bahattin Buyuksahin, JHU, Investment

28 Table 11.2 Two-Way Table of Managers Classified by Risk-Adjusted Returns over Successive Intervals
Bahattin Buyuksahin, JHU, Investment

29 Behavioral Finance and Technical Analysis
CHAPTER 12

30 Behavioral Finance Investors Do Not Always Process Information Correctly Investors Often Make Inconsistent or Systematically Suboptimal Decisions Bahattin Buyuksahin, JHU, Investment

31 Information Processing Critique
Forecasting Errors Overconfidence Conservatism Sample Size Neglect and Representativeness Bahattin Buyuksahin, JHU, Investment

32 Behavioral Biases Framing Mental Accounting Regret Avoidance
Prospect Theory Bahattin Buyuksahin, JHU, Investment

33 Figure 12.1 Prospect Theory
Bahattin Buyuksahin, JHU, Investment

34 Limits to Arbitrage Fundamental Risk Implementation Costs Model Risk
Bahattin Buyuksahin, JHU, Investment

35 Limits to Arbitrage and the Law of One Price
Siamese Twin Companies Equity Carve-outs Closed-End Funds Bahattin Buyuksahin, JHU, Investment

36 Figure 12.2 Pricing of Royal Dutch Relative to Shell (Deviation from Parity)
Bahattin Buyuksahin, JHU, Investment

37 Evaluation of the Behavioral Critiques
Bubbles and Behavioral Economics Arguments that the Evidence Does Not Support One Type of Irrationality Relatively New Field Bahattin Buyuksahin, JHU, Investment

38 Technical Analysis and Behavioral Finance
Trends and Corrections Dow Theory Moving averages Breadth Sentiment Indicators Trin Statistic Confidence Index Put/Call Ratio Bahattin Buyuksahin, JHU, Investment

39 Figure 12.3 Dow Theory Trends
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40 Figure 12.4 Dow Jones Industrial Average in 1988
Bahattin Buyuksahin, JHU, Investment

41 Figure 12.5 Moving Average for Microsoft
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42 Example 12.4 Moving Averages
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43 Figure 12.6 Moving Averages
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44 Figure 12.7 Market Diary Bahattin Buyuksahin, JHU, Investment

45 Table 12.1 Breadth Bahattin Buyuksahin, JHU, Investment

46 Figure 12.8 Actual and Simulated Levels for Stock Market Prices of 52 Weeks
Bahattin Buyuksahin, JHU, Investment

47 Figure 12.9 Actual and Simulated Changes in Stock Prices for 52 Weeks
Bahattin Buyuksahin, JHU, Investment

48 Empirical Evidence on Security Returns
CHAPTER 13

49 Overview of Investigation
Tests of the single factor CAPM or APT Model Tests of the Multifactor APT Model Results are difficult to interpret Studies on volatility of returns over time Bahattin Buyuksahin, JHU, Investment

50 The Index Model and the Single-Factor APT
Expected Return-Beta Relationship Estimating the SCL Bahattin Buyuksahin, JHU, Investment

51 Tests of the CAPM Tests of the expected return beta relationship:
First Pass Regression Estimate beta, average risk premiums and unsystematic risk Second Pass: Using estimates from the first pass to determine if model is supported by the data Most tests do not generally support the single factor model Bahattin Buyuksahin, JHU, Investment

52 Single Factor Test Results
Return % Predicted Actual Beta Bahattin Buyuksahin, JHU, Investment

53 Roll’s Criticism The only testable hypothesis is on the efficiency of the market portfolio In any sample of observations of individual returns Infinite number of ex post mean-variance efficient portfolios using the sample-period returns and covariances CAPM is not testable unless we know the exact composition of the true market portfolio and use it in the tests Benchmark error Bahattin Buyuksahin, JHU, Investment

54 Measurement Error in Beta
Statistical property If beta is measured with error in the first stage, second stage results will be biased in the direction the tests have supported Test results could result from measurement error Bahattin Buyuksahin, JHU, Investment

55 Table 13.1 Summary of Fama and MacBeth (1973) Study (All Rates in Basis Points per Month)
Bahattin Buyuksahin, JHU, Investment

56 Jaganathan and Wang Study
Included factors for cyclical behavior of betas and human capital When these factors were included the results showed returns were a function of beta Size is not an important factor when cyclical behavior and human capital are included Bahattin Buyuksahin, JHU, Investment

57 Table 13.2 Evaluation of Various CAPM Specifications
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58 Table 13.3 Portfolio Shares Relative to Total Assets by Age and Net Worth
Bahattin Buyuksahin, JHU, Investment

59 Table 13.4 Determinants of Stockholdings
Bahattin Buyuksahin, JHU, Investment

60 Tests of the Multifactor Model
Chen, Roll and Ross 1986 Study Factors Growth rate in industrial production Changes in expected inflation Unexpected inflation Unexpected Changes in risk premiums on bonds Unexpected changes in term premium on bonds Bahattin Buyuksahin, JHU, Investment

61 Study Structure & Results
Method: Two -stage regression with portfolios constructed by size based on market value of equity Fidings Significant factors: industrial production, risk premium on bonds and unanticipated inflation Market index returns were not statistically significant in the multifactor model Bahattin Buyuksahin, JHU, Investment

62 Table 13.5 Economic Variables and Pricing (Percent per Month x 10), Multivariate Approach
Bahattin Buyuksahin, JHU, Investment

63 Fama-French Three Factor Model
Size and book-to-market ratios explain returns on securities Smaller firms experience higher returns High book to market firms experience higher returns Returns are explained by size, book to market and by beta Bahattin Buyuksahin, JHU, Investment

64 Table 13.6 Three Factor Regressions for Portfolios Formed from Sorts on Size and Book-to-Market Ratios (B/M) Bahattin Buyuksahin, JHU, Investment

65 Interpretation of Three-Factor Model
Size is a proxy for risk that is not captured in CAPM Beta Premiums are due to investor irrationality or behavioral biases Bahattin Buyuksahin, JHU, Investment

66 Risk-Based Interpretations
Liew and Vassalou Petkova and Zhang Bahattin Buyuksahin, JHU, Investment

67 Figure 13.1 Difference in Return to Factor Portfolios in Year Prior to Above-Average versus Below-Average GDP Growth Bahattin Buyuksahin, JHU, Investment

68 Figure 13.2 HML Beta in Different Economic States
Bahattin Buyuksahin, JHU, Investment

69 Behavioral Explanations
Market participants are overly optimistic Analysts extrapolate recent performance too far into the future Prices on these glamour stocks are overly optimistic Lower book-to-market on these glamour firms leads to underperformance compared to value stocks Chan, Karceski and Lakonishok LaPort, Lakonishok, Shleifer and Vishny Bahattin Buyuksahin, JHU, Investment

70 Figure 13.3 The Book-to-Market Ratio Reflects Past Growth, but Not Future Growth Prospects
Bahattin Buyuksahin, JHU, Investment

71 Figure 13.4 Value minus Glamour Returns Surrounding Earnings Announcements, 1971-1992
Bahattin Buyuksahin, JHU, Investment

72 Liquidity and Asset Pricing
Acharya and Pedersen Premiums observed in the three-factor model may be illiquidity premiums Liquidity may explain the size premium but not the book-to-market premium Bahattin Buyuksahin, JHU, Investment

73 Table 13.7 Properties of Liquidity Portfolios
Bahattin Buyuksahin, JHU, Investment

74 Table 13.8 Estimates of the CAPM With and Without Liquidity Factors
Bahattin Buyuksahin, JHU, Investment

75 Time-Varying Volatility
Stock prices change primarily in reaction to information New information arrival is time varying Volatility is therefore not constant through time Bahattin Buyuksahin, JHU, Investment

76 Stock Volatility Studies and Techniques
Volatility is not constant through time Improved modeling techniques should improve results of tests of the risk-return relationship ARCH and GARCH models incorporate time varying volatility Bahattin Buyuksahin, JHU, Investment

77 Figure 13.5 Estimates of the Monthly Stock Return Variance 1835 - 1987
Bahattin Buyuksahin, JHU, Investment

78 Figure 13.6 Implied Versus Estimated Volatility
Bahattin Buyuksahin, JHU, Investment

79 Equity Premium Puzzle Rewards for bearing risk appear to be excessive
Possible Causes CAPM doesn’t consider the impact of consumption Predicting returns from realized returns Survivorship bias also creates the appearance of abnormal returns in market efficiency studies Bahattin Buyuksahin, JHU, Investment

80 Table 13.9 Annual Consumption Growth, 1954-2003 (%)
Bahattin Buyuksahin, JHU, Investment

81 Table 13.10 Annual Excess Returns and Consumption Betas
Bahattin Buyuksahin, JHU, Investment

82 Figure 13.7 Cross-Section of Stock Returns: Fama-French 25 Portfolios, 1954-2003
Bahattin Buyuksahin, JHU, Investment


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