A Behavioral Approach To Asset Pricing

by Shefrin

• ISBN13: 9780126393712
• ISBN10: 0126393710
• Format: Hardcover
• Copyright: 2005-01-21
• Publisher: Elsevier Science & Technology

Summary

A Behavioral Approach to Asset Pricing Theory examines the reigning assumptions of asset pricing theory and reconstructs them to incorporate findings from behavioral finance. It constructs a solid, intact structure that challenges classic assumptions and at the same time provides a strong theory and efficient empirical tools. Building on the models developed by both traditional asset pricing theorists and behavioral asset pricing theorists, this book takes the discussion to the next step. The author provides a general behaviorally based intertemporal treatment of asset pricing theory that extends to the discussion of derivatives, fixed income securities, mean-variance efficient portfolios, and the market portfolio. The book develops a series of examples to illustrate the theoretical results. The CD-ROM contains most of the examples, worked out as Excel spreadsheets, so that a diligent reader can follow them through. Instructors might also want to use the examples to assign class exercises, asking students to modify the numbers and see what happens.

Author Biography

Hersh Shefrin is the Mario Belotti Professor of Finance, Santa Clara University, California.

Table of Contents

Preface	xvii
1 Introduction	1	(12)
1.1 Why Read This Book?	2	(4)
1.1.1 Value to Proponents of Traditional Asset Pricing	2	(3)
1.1.2 Value to Proponents of Behavioral Asset Pricing	5	(1)
1.2 Organization: How the Ideas in This Book Tie Together	6	(6)
1.2.1 Heuristics and Representativeness: Experimental Evidence	7	(1)
1.2.2 Heuristics and Representativeness: Investor Expectations	7	(1)
1.2.3 Developing Behavioral Asset Pricing Models	7	(1)
1.2.4 Heterogeneity in Risk Tolerance and Time Discounting	8	(1)
1.2.5 Sentiment and Behavioral SDF	9	(1)
1.2.6 Applications of Behavioral SDF	9	(2)
1.2.7 Prospect Theory	11	(1)
1.2.8 Closure	12	(1)
1.3 Summary	12	(1)
I Heuristics and Representativeness: Experimental Evidence	13	(46)
2 Representativeness and Bayes Rule: Psychological Perspective	15	(10)
2.1 Explaining Representativeness	16	(1)
2.2 Implications for Bayes Rule	16	(1)
2.3 Experiment	16	(3)
2.3.1 Three Groups	17	(1)
2.3.2 Bayesian Hypothesis	18	(1)
2.3.3 Results	18	(1)
2.4 Representativeness and Prediction	19	(4)
2.4.1 Two Extreme Cases	20	(1)
2.4.2 Representativeness and Regression to the Mean	21	(1)
2.4.3 Results for the Prediction Study	21	(1)
2.4.4 Strength of Relationship Between Signal and Prediction	21	(1)
2.4.5 How Regressive?	22	(1)
2.5 Summary	23	(2)
3 Representativeness and Bayes Rule: Economics Perspective	25	(8)
3.1 The Grether Experiment	25	(3)
3.1.1 Design	25	(1)
3.1.2 Experimental Task: Bayesian Approach	26	(2)
3.2 Representativeness	28	(1)
3.3 Results	28	(4)
3.3.1 Underweighting Base Rate Information	31	(1)
3.4 Summary	32	(1)
4 A Simple Asset Pricing Model Featuring Representativeness	33	(10)
4.1 First Stage, Modified Experimental Structure	34	(1)
4.2 Expected Utility Model	34	(3)
4.2.1 Bayesian Solution	36	(1)
4.3 Equilibrium Prices	37	(1)
4.4 Representativeness	38	(1)
4.5 Second Stage: Signal-Based Market Structure	39	(2)
4.6 Summary	41	(2)
5 Heterogeneous Judgments in Experiments	43	(18)
5.1 Grether Experiment	43	(1)
5.2 Heterogeneity in Predictions of GPA	44	(2)
5.3 The De Bondt Experiment	46	(9)
5.3.1 Forecasts of the S&P Index: Original Study	46	(6)
5.3.2 Replication of De Bondt Study	52	(2)
5.3.3 Overconfidence	54	(1)
5.4 Why Some Bet on Trends and Others Commit Gambler's Fallacy	55	(2)
5.5 Summary	57	(2)
II Heuristics and Representativeness: Investor Expectations	59	(38)
6 Representativeness and Heterogeneous Beliefs Among Individual Investors, Financial Executives, and Academics	61	(14)
6.1 Individual Investors	61	(8)
6.1.1 Bullish Sentiment and Heterogeneity	62	(1)
6.1.2 The UBS-Gallup Survey	63	(1)
6.1.3 Heterogeneous Beliefs	63	(1)
6.1.4 Trend Following	64	(2)
6.1.5 The Impact of Demographic Variables	66	(1)
6.1.6 Own Experience: Availability Bias	67	(1)
6.1.7 Do Individual Investors Bet on Trends? Perceptions and Reactions to Mispricing	68	(1)
6.2 The Expectations of Academic Economists	69	(4)
6.2.1 Heterogeneous Beliefs	70	(2)
6.2.2 Welch's 1999 and 2001 Surveys	72	(1)
6.3 Financial Executives	73	(1)
6.3.1 Volatility and Overconfidence	74	(1)
6.4 Summary	74	(1)
7 Representativeness and Heterogeneity in the Judgments of Professional Investors	75	(24)
7.1 Contrasting Predictions: How Valid?	75	(1)
7.2 Update to Livingston Survey	76	(4)
7.2.1 Heterogeneity	77	(3)
7.3 Individual Forecasting Records	80	(8)
7.3.1 Frank Cappiello	82	(4)
7.3.2 Ralph Acampora	86	(2)
7.4 Gambler's Fallacy	88	(5)
7.4.1 Forecast Accuracy	89	(1)
7.4.2 Excessive Pessimism	90	(1)
7.4.3 Predictions of Volatility	91	(2)
7.5 Why Heterogeneity Is Time Varying	93	(1)
7.5.1 Heterogeneity and Newsletter Writers	94	(1)
7.6 Summary	94	(3)
III Developing Behavioral Asset Pricing Models	97	(62)
8 A Simple Asset Pricing Model with Heterogeneous Beliefs	99	(12)
8.1 A Simple Model with Two Investors	99	(3)
8.1.1 Probabilities	100	(1)
8.1.2 Utility Functions	100	(1)
8.1.3 State Prices	100	(1)
8.1.4 Budget Constraint	101	(1)
8.1.5 Expected Utility Maximization	101	(1)
8.2 Equilibrium Prices	102	(2)
8.2.1 Formal Argument	103	(1)
8.2.2 Representative Investor	104	(1)
8.3 Fixed Optimism and Pessimism	104	(3)
8.3.1 Impact of Heterogeneity	107	(1)
8.4 Incorporating Representativeness	107	(2)
8.5 Summary	109	(2)
9 Heterogeneous Beliefs and Inefficient Markets	111	(12)
9.1 Defining Market Efficiency	111	(4)
9.1.1 Riskless Arbitrage	113	(1)
9.1.2 Risky Arbitrage	113	(1)
9.1.3 Fundamental Value	114	(1)
9.1.4 When Π Is Nonexistent	114	(1)
9.2 Market Efficiency and Logarithmic Utility	115	(1)
9.2.1 Example of Market Inefficiency	115	(1)
9.3 Equilibrium Prices as Aggregators	116	(1)
9.4 Market Efficiency: Necessary and Sufficient Condition	117	(2)
9.5 Interpreting the Efficiency Condition	119	(3)
9.5.1 When the Market Is Naturally Efficient	119	(1)
9.5.2 Knife-Edge Efficiency	120	(1)
9.5.3 When the Market Is Naturally Inefficient	120	(2)
9.6 Summary	122	(1)
10 A Simple Market Model of Prices and Trading Volume	123	(18)
10.1 The Model	123	(3)
10.1.1 Expected Utility Maximization	123	(3)
10.2 Analysis of Returns	126	(1)
10.2.1 Market Portfolio	126	(1)
10.2.2 Risk-Free Security	127	(1)
10.3 Analysis of Trading Volume	127	(4)
10.3.1 Theory	129	(2)
10.4 Example	131	(8)
10.4.1 Stochastic Processes	131	(2)
10.4.2 Available Securities	133	(1)
10.4.3 Initial Portfolios	134	(1)
10.4.4 Equilibrium Portfolio Strategies	134	(3)
10.4.5 Markov Structure, Continuation, and Asymmetric Volatility	137	(2)
10.5 Arbitrage	139	(1)
10.5.1 State Prices	139	(1)
10.6 Summary	140	(1)
11 Efficiency and Entropy: Long-Run Dynamics	141	(18)
11.1 Introductory Example	142	(5)
11.1.1 The Market	143	(1)
11.1.2 Budget Share Equations	144	(1)
11.1.3 Portfolio Relationships	144	(1)
11.1.4 Wealth Share Equations	145	(2)
11.2 Entropy	147	(1)
11.3 Numerical Illustration	148	(1)
11.4 Markov Beliefs	149	(1)
11.5 Heterogeneous Time Preference, Entropy, and Efficiency	150	(4)
11.5.1 Modeling Heterogeneous Rates of Time Preference	151	(1)
11.5.2 Market Portfolio	152	(1)
11.5.3 Digression: Hyperbolic Discounting	153	(1)
11.5.4 Long-Run Dynamics When Time Preference Is Heterogeneous	154	(1)
11.6 Entropy and Market Efficiency	154	(3)
11.7 Summary	157	(2)
IV Heterogeneity in Risk Tolerance and Time Discounting	159	(42)
12 CRRA and CARA Utility Functions	161	(14)
12.1 Arrow-Pratt Measure	161	(1)
12.2 Proportional Risk	162	(1)
12.3 Constant Relative Risk Aversion	162	(2)
12.3.1 Graphical Illustration	163	(1)
12.3.2 Risk Premia	163	(1)
12.4 Logarithmic Utility	164	(1)
12.4.1 Risk Premium in a Discrete Gamble	164	(1)
12.5 CRRA Demand Function	165	(1)
12.6 Representative Investor	166	(1)
12.7 Example	167	(3)
12.7.1 Aggregation and Exponentiation	169	(1)
12.8 CARA Utility	170	(4)
12.8.1 CARA Demand Function	171	(1)
12.8.2 Aggregate Demand and Equilibrium	172	(2)
12.9 Summary	174	(1)
13 Heterogeneous Risk Tolerance and Time Preference	175	(10)
13.1 Survey Evidence	175	(4)
13.1.1 Questions to Elicit Relative Risk Aversion	175	(2)
13.1.2 Two Waves	177	(1)
13.1.3 Status Quo Bias	178	(1)
13.1.4 Risky Choice	179	(1)
13.2 Extended Survey	179	(3)
13.3 Time Preference	182	(1)
13.4 Summary	183	(2)
14 Representative Investors in a Heterogeneous CRRA Model	185	(16)
14.1 Relationship to Representative Investor Literature	186	(3)
14.1.1 Additional Literature	188	(1)
14.2 Modeling Preliminaries	189	(1)
14.3 Efficient Prices	190	(1)
14.4 Representative Investor Characterization Theorem	191	(4)
14.4.1 Discussion	194	(1)
14.4.2 Nonuniqueness	195	(1)
14.5 Comparison Example	195	(3)
14.6 Pitfall: The Representative Investor Theorem Is False	198	(2)
14.6.1 Argument Claiming that Theorem 14.1 Is False	198	(1)
14.6.2 Identifying the Flaw	199	(1)
14.7 Summary	200	(1)
V Sentiment and Behavioral SDF	201	(38)
15 Sentiment	203	(18)
15.1 Intuition: Kahneman's Perspective	203	(3)
15.1.1 Relationship to Theorem 14.1	204	(2)
15.1.2 Defining Market Efficiency	206	(1)
15.2 Sentiment	206	(1)
15.2.1 Formal Definition	207	(1)
15.3 Example Featuring Heterogeneous Risk Tolerance	207	(2)
15.4 Example Featuring Log-Utility	209	(9)
15.4.1 Representativeness: Errors in First Moments	209	(3)
15.4.2 Overconfidence: Errors in Second Moments	212	(3)
15.4.3 Link to Empirical Evidence	215	(1)
15.4.4 Evidence of Clustering	216	(2)
15.5 Sentiment as a Stochastic Process	218	(1)
15.6 Summary	219	(2)
16 Behavioral SDF and the Sentiment Premium	221	(18)
16.1 The SDF	222	(1)
16.2 Sentiment and the SDF	223	(3)
16.2.1 Example	224	(2)
16.3 Pitfalls	226	(4)
16.3.1 Pitfall: The Behavioral Framework Admits a Traditional SDF	226	(1)
16.3.2 Pitfall: Heterogeneity Need Not Imply Sentiment	227	(1)
16.3.3 Pitfall: Heterogeneity in Risk Tolerance Is Sufficient to Explain Asset Pricing	228	(2)
16.4 Sentiment and Expected Returns	230	(4)
16.4.1 Interpretation and Discussion	232	(1)
16.4.2 Example Illustrating Theorem 16.2	233	(1)
16.5 Entropy and Long-Run Efficiency	234	(2)
16.5.1 Formal Argument	235	(1)
16.6 Learning: Bayesian and Non-Bayesian	236	(1)
16.7 Summary	237	(2)
VI Applications of Behavioral SDF	239	(124)
17 Behavioral Betas and Mean-Variance Portfolios	241	(14)
17.1 Mean-Variance Efficiency and Market Efficiency	241	(1)
17.2 Characterizing Mean-variance Efficient Portfolios	242	(2)
17.3 The Shape of Mean-Variance Returns	244	(3)
17.4 The Market Portfolio	247	(2)
17.5 Behavioral Beta: Decomposition Result	249	(4)
17.5.1 Informal Discussion: Intuition	249	(1)
17.5.2 Formal Argument	250	(2)
17.5.3 Example	252	(1)
17.6 Summary	253	(2)
18 Cross-section of Return Expectations	255	(24)
18.1 Literature Review	256	(5)
18.1.1 Winner-Loser Effect	256	(1)
18.1.2 Book-to-Market Equity and the Winner-Loser Effect	257	(1)
18.1.3 January and Momentum	258	(1)
18.1.4 General Momentum Studies	259	(1)
18.1.5 Glamour and Value	260	(1)
18.2 Factor Models and Risk	261	(1)
18.3 Differentiating Fundamental Risk and Investor Error	262	(5)
18.3.1 Psychology of Risk and Return	263	(1)
18.3.2 Evidence About Judgments of Risk and Return	264	(1)
18.3.3 Psychology Underlying a Negative Relationship Between Risk and Return	265	(2)
18.4 Implications for the Broad Debate	267	(1)
18.5 Analysts' Return Expectations	268	(1)
18.6 How Consciously Aware Are Investors When Forming Judgments?	269	(1)
18.7 How Reliable Is the Evidence on Expected Returns?	270	(2)
18.8 Alternative Theories	272	(5)
18.8.1 The Dynamics of Expectations: Supporting Data	275	(2)
18.9 Summary	277	(2)
19 Testing for a Sentiment Premium	279	(10)
19.1 Diether-Malloy-Scherbina: Returns Are Negatively Related to Dispersion	280	(2)
19.2 Ghysels-Juergens: Dispersion Factor	282	(4)
19.2.1 Basic Approach	282	(1)
19.2.2 Factor Structure	282	(1)
19.2.3 General Properties of the Data	283	(1)
19.2.4 Expected Returns	284	(1)
19.2.5 Findings	284	(1)
19.2.6 Volatility	285	(1)
19.2.7 Direction of Mispricing	285	(1)
19.2.8 Opposite Signs for Short and Long Horizons	286	(1)
19.3 Estimating a Structural SDF-Based Model	286	(2)
19.3.1 Proxy for hz,0	287	(1)
19.3.2 Findings	287	(1)
19.4 Summary	288	(1)
20 A Behavioral Approach to the Term Structure of Interest Rates	289	(12)
20.1 The Term Structure of Interest Rates	289	(1)
20.2 Pitfall: The Bond Pricing Equation in Theorem 20.1 Is False	290	(2)
20.2.1 Identifying the Flaw in the Analysis	292	(1)
20.3 Volatility	292	(4)
20.3.1 Heterogeneous Risk Tolerance	295	(1)
20.4 Expectations Hypothesis	296	(3)
20.4.1 Example	298	(1)
20.5 Summary	299	(2)
21 Behavioral Black-Scholes	301	(20)
21.1 Call and Put Options	301	(1)
21.2 Risk-Neutral Densities and Option Pricing	302	(3)
21.2.1 Option Pricing Equation 1	302	(2)
21.2.2 Option Pricing Equations 2 and 3	304	(1)
21.3 Option Pricing Examples	305	(6)
21.3.1 Discrete Time Example	305	(4)
21.3.2 Continuous Time Example	309	(2)
21.4 Smile Patterns	311	(5)
21.4.1 Downward Sloping Smile Patterns in the IVF Function	314	(2)
21.5 Heterogeneous Risk Tolerance	316	(1)
21.6 Pitfall: Equation (21.12) Is False	317	(1)
21.6.1 Locating the Flaw	318	(1)
21.7 Pitfall: Beliefs Do Not Matter in Black-Scholes	318	(1)
21.7.1 Locating the Flaw	319	(1)
21.8 Summary	319	(2)
22 Irrational Exuberance and Option Smiles	321	(18)
22.1 Irrational Exuberance: Brief History	322	(4)
22.1.1 Sentiment	324	(2)
22.2 Risk-Neutral Densities and Index Option Prices	326	(4)
22.2.1 Butterfly Position Technique	328	(2)
22.3 Continuation, Reversal, and Option Prices	330	(5)
22.4 Price Pressure: Was Arbitrage Fully Carried Out?	335	(2)
22.5 Heterogeneous Beliefs	337	(1)
22.6 Summary	337	(2)
23 Empirical Evidence in Support of Behavioral SDF	339	(24)
23.1 Bollen-Whaley: Price Pressure Drives Smiles	340	(4)
23.1.1 Data	340	(1)
23.1.2 Trading Patterns	341	(1)
23.1.3 Buying Pressure and Smile Effects	342	(1)
23.1.4 Price Pressure or Learning?	343	(1)
23.1.5 Arbitrage Profits	343	(1)
23.2 Han: Smile Effects, Sentiment, and Gambler's Fallacy	344	(2)
23.2.1 Price Pressure	345	(1)
23.2.2 Impact of a Market Drop: Gambler's Fallacy	345	(1)
23.2.3 Impact of Sentiment	345	(1)
23.2.4 Time-Varying Uncertainty	346	(1)
23.3 David-Veronesi: Gambler's Fallacy and Negative Skewness	346	(2)
23.4 Jackwerth: Estimating Market Risk Aversion	348	(2)
23.4.1 Behavioral Risk Neutral Density	348	(2)
23.5 Rosenberg-Engle: Signature of Sentiment in the SDF	350	(2)
23.5.1 Two Approaches to Estimating the EPK	350	(1)
23.5.2 Estimating Market Risk Aversion	351	(1)
23.5.3 Empirical Results: Estimates of SDF	351	(1)
23.5.4 Estimates of Risk Aversion	351	(1)
23.6 Comparing the Behavioral SDF and Empirical SDF	352	(7)
23.6.1 Empirical Evidence for Clustering: Mode in the Left Tail Reflecting Pessimism	353	(2)
23.6.2 Investors and Predictions of Continuation	355	(2)
23.6.3 Mode in the Left Tail and Crashophobia	357	(1)
23.6.4 Time Variation in the SDF	358	(1)
23.7 Heterogeneous Perspectives	359	(3)
23.8 Summary	362	(1)
VII Prospect Theory	363	(84)
24 Prospect Theory: Introduction	365	(18)
24.1 Experimental Evidence	366	(8)
24.1.1 Common Ratio Effect	366	(1)
24.1.2 Subcertainty and Expected Utility	367	(1)
24.1.3 Allais Paradox and the Independence Axiom	368	(2)
24.1.4 Isolation and Common Consequence Effect	370	(1)
24.1.5 Isolation and the Independence Axiom	371	(1)
24.1.6 Loss Aversion	372	(1)
24.1.7 Ambiguity	372	(2)
24.2 Theory	374	(5)
24.2.1 The Weighting Function	374	(2)
24.2.2 Value Function	376	(1)
24.2.3 Interaction Between Value Function and Weighting Function	377	(1)
24.2.4 Framing	378	(1)
24.3 Subtle Aspects Associated with Risk Aversion	379	(2)
24.3.1 Caveats	380	(1)
24.4 Generalized Utility Theories	381	(1)
24.5 Summary	382	(1)
25 Behavioral Portfolios	383	(18)
25.1 Theory	384	(1)
25.1.1 Prospect Theory: Uncertainty Weights	384	(1)
25.1.2 Utility Function	384	(1)
25.1.3 Prospect Theory Functional	385	(1)
25.2 Prospect Theory: Indifference Map	385	(1)
25.3 Portfolio Choice: Single Mental Account	386	(3)
25.3.1 Exposure to Loss: Single Mental Account	387	(1)
25.3.2 Portfolio Payoff Return: Single Mental Account	388	(1)
25.4 Multiple Mental Accounts: Example	389	(3)
25.4.1 General Comments About Multiple Mental Accounts	391	(1)
25.4.2 Prospect Theory and Mean-Variance Efficiency	392	(1)
25.5 SP/A Theory	392	(6)
25.5.1 SP/A Efficient Frontier	394	(1)
25.5.2 Example	394	(2)
25.5.3 Formal Analysis	396	(2)
25.5.4 Additional Comments	398	(1)
25.6 Real World Portfolios and Securities	398	(2)
25.7 Summary	400	(1)
26 Prospect Theory Equilibrium	401	(18)
26.1 The Model	402	(1)
26.2 Simple Example	403	(4)
26.2.1 Neoclassical Case	403	(1)
26.2.2 Prospect Theory Investors	403	(4)
26.3 On the Boundary	407	(1)
26.4 Equilibrium Pricing	408	(2)
26.4.1 Equiprobable Loss States	410	(1)
26.5 Portfolio Insurance	410	(3)
26.5.1 Qualification: Probability Weighting	411	(1)
26.5.2 Testable Prediction	412	(1)
26.6 Risk and Return: Portfolio Insurance in a Mean-Variance Example	413	(4)
26.7 Summary	417	(2)
27 Pricing and Prospect Theory: Empirical Studies	419	(10)
27.1 Combining Behavioral Preferences and Beliefs	419	(1)
27.2 Disposition Effect: The Empirical Evidence	420	(2)
27.3 Investor Beliefs	422	(4)
27.3.1 Odean's Findings	422	(1)
27.3.2 A Size Effect	423	(1)
27.3.3 A Volume Effect	424	(2)
27.4 Momentum and the Disposition Effect	426	(2)
27.4.1 Theoretical Hypotheses	426	(1)
27.4.2 Empirical Evidence	427	(1)
27.5 Summary	428	(1)
28 Reflections on the Equity Premium Puzzle	429	(18)
28.1 Basis for Puzzles in Traditional Framework	429	(4)
28.1.1 Brief Review	430	(1)
28.1.2 Attaching Numbers to Equations	431	(2)
28.2 Erroneous Beliefs	433	(4)
28.2.1 Livingston Data	433	(3)
28.2.2 The Market and the Economy: Upwardly Biased Covariance Estimate	436	(1)
28.3 Alternative Rationality-Based Models	437	(3)
28.3.1 Habit Formation	437	(1)
28.3.2 Habit Formation SDF	438	(1)
28.3.3 Habit Formation SDF Versus the Empirical SDF	439	(1)
28.4 Behavioral Preferences and the Equity Premium	440	(4)
28.4.1 Myopic Loss Aversion	440	(2)
28.4.2 Transaction Utility	442	(2)
28.5 Risks, Small and Large	444	(1)
28.6 Summary	445	(2)
VIII Closure	447	(10)
29 Conclusion	449	(8)
29.1 Recapitulating the Main Points	449	(3)
29.2 Testable Predictions	452	(1)
29.3 Future Directions	453	(4)
References	457	(16)
Index	473