Forecasting Expected Returns in the Financial Markets (eBook)

Front Cover 1
Forecasting Expected Returns in the Financial Markets 4
Copyright Page 5
Table of Contents 6
List of contributors 10
Introduction 12
Chapter 1 Market efficiency and forecasting 14
1.1 Introduction 14
1.2 A modern view of market efficiency and predictability 15
1.3 Weak-form predictability 16
1.4 Semi-strong form predictability 18
1.5 Methodological issues 21
1.6 Perspective 23
1.7 Conclusion 25
References 25
Chapter 2 A step-by-step guide to the Black–Litterman model 30
2.1 Introduction 30
2.2 Expected returns 31
2.3 The Black–Litterman model 34
2.4 A new method for incorporating user-specified confidence levels 45
2.5 Conclusion 49
References 50
Chapter 3 A demystification of the Black–Litterman model: managing quantitative and traditional portfolio construction 52
3.1 Introduction 52
3.2 Workings of the model 53
3.3 Examples 55
3.4 Alternative formulations 59
3.5 Conclusion 63
Appendix 63
References 66
Chapter 4 Optimal portfolios from ordering information 68
4.1 Introduction 68
4.2 Efficient portfolios 71
4.3 Optimal portfolios 83
4.4 A variety of sorts 90
4.5 Empirical tests 95
4.6 Conclusion 108
Appendix A 109
Appendix B 110
References 112
Chapter 5 Some choices in forecast construction 114
5.1 Introduction 114
5.2 Linear factor models 117
5.3 Approximating risk with a mixture of normals 119
5.4 Practical problems in the model-building process 121
5.5 Optimization with non-normal return expectations 125
5.6 Conclusion 128
References 128
Chapter 6 Bayesian analysis of the Black–Scholes option price 130
6.1 Introduction 130
6.2 Derivation of the prior and posterior densities 132
6.3 Numerical evaluation 144
6.4 Results 147
6.5 Concluding remarks and issues for further research 153
Appendix 155
References 161
Chapter 7 Bayesian forecasting of options prices: a natural framework for pooling historical and implied volatility information 164
7.1 Introduction 164
7.2 A classical framework for option pricing 168
7.3 A Bayesian framework for option pricing 169
7.4 Empirical implementation 176
7.5 Conclusion 185
Appendix 186
References 187
Chapter 8 Robust optimization for utilizing forecasted returns in institutional investment 190
8.1 Introduction 190
8.2 Notions of robustness 191
8.3 Case study: an implementation of robustness via forecast errors and quadratic constraints 195
8.4 Extensions to the theory 197
8.5 Conclusion 200
References 201
Chapter 9 Cross-sectional stock returns in the UK market: the role of liquidity risk 204
9.1 Introduction 204
9.2 Hypotheses and calculating factors 206
9.3 Empirical results 209
9.4 Conclusions 224
References 225
Chapter 10 The information horizon – optimal holding period, strategy aggression and model combination in a multi-horizon framework 228
10.1 The information coefficient and information decay 228
10.2 Returns and information decay in the single model case 230
10.3 Model combination 234
10.4 Information decay in models 235
10.5 Models – optimal horizon, aggression and model combination 237
Reference 239
Chapter 11 Optimal forecasting horizon for skilled investors 240
11.1 Introduction 240
11.2 Analysis of the single model problem 241
11.3 Closed-form solutions 245
11.4 Multi-model horizon framework 249
11.5 An alternative formulation of the multi-model problem 254
11.6 Conclusions 256
Appendix A 257
Appendix B 259
References 263
Chapter 12 Investments as bets in the binomial asset pricing model 264
12.1 Introduction 264
12.2 Actual versus risk-neutral probabilities 265
12.3 Replicating investments with bets 268
12.4 Log optimal (Kelly) betting 269
12.5 Replicating Kelly bets with puts and calls 271
12.6 Options on Kelly bets 272
12.7 Conclusion 273
References 274
Chapter 13 The hidden binomial economy and the role of forecasts in determining prices 278
13.1 Introduction 278
13.2 General set-up 279
13.3 Power utility 284
13.4 Exponential utility, loss aversion and mixed equilibria 289
13.5 Conclusions 290
Appendix 291
References 291
Index 294