Forecasting expected returns in the financial markets /

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Dr Stephen Satchell brings together a collection of leading thinkers from around the world to address this complex and central challenge in portfolio management.

Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Satchell, Stephen, 1949-
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Oxford : Elsevier/AP, 2007.
Colección:Quantitative finance series.
Temas:
Stock price forecasting.
Stock price forecasting > Mathematical models.
Investment analysis.
Actions (Titres de société) > Prix > Prévision.
Actions (Titres de société) > Prix > Prévision > Modèles mathématiques.
Analyse financière.
BUSINESS & ECONOMICS > Investments & Securities > Stocks.
Investment analysis
Stock price forecasting
Stock price forecasting > Mathematical models
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Cover
  • Contents
  • List of contributors
  • Introduction
  • Chapter 1 Market efficiency and forecasting
  • 1.1 Introduction
  • 1.2 A modern view of market efficiency and predictability
  • 1.3 Weak-form predictability
  • 1.4 Semi-strong form predictability
  • 1.5 Methodological issues
  • 1.6 Perspective
  • 1.7 Conclusion
  • References
  • Chapter 2 A step-by-step guide to the Black-Litterman model
  • 2.1 Introduction
  • 2.2 Expected returns
  • 2.3 The Black-Litterman model
  • 2.4 A new method for incorporating user-specified confidence levels
  • 2.5 Conclusion
  • References
  • Chapter 3 A demystification of the Black-Litterman model: managing quantitative and traditional portfolio construction
  • 3.1 Introduction
  • 3.2 Workings of the model
  • 3.3 Examples
  • 3.4 Alternative formulations
  • 3.5 Conclusion
  • Appendix
  • References
  • Chapter 4 Optimal portfolios from ordering information
  • 4.1 Introduction
  • 4.2 Efficient portfolios
  • 4.3 Optimal portfolios
  • 4.4 A variety of sorts
  • 4.5 Empirical tests
  • 4.6 Conclusion
  • Appendix A
  • Appendix B
  • References
  • Chapter 5 Some choices in forecast construction
  • 5.1 Introduction
  • 5.2 Linear factor models
  • 5.3 Approximating risk with a mixture of normals
  • 5.4 Practical problems in the model-building process
  • 5.5 Optimization with non-normal return expectations
  • 5.6 Conclusion
  • References
  • Chapter 6 Bayesian analysis of the Black-Scholes option price
  • 6.1 Introduction
  • 6.2 Derivation of the prior and posterior densities
  • 6.3 Numerical evaluation
  • 6.4 Results
  • 6.5 Concluding remarks and issues for further research
  • Appendix
  • References
  • Chapter 7 Bayesian forecasting of options prices: a natural framework for pooling historical and implied volatility information
  • 7.1 Introduction
  • 7.2 A classical framework for option pricing
  • 7.3 A Bayesian framework for option pricing
  • 7.4 Empirical implementation
  • 7.5 Conclusion
  • Appendix
  • References
  • Chapter 8 Robust optimization for utilizing forecasted returns in institutional investment
  • 8.1 Introduction
  • 8.2 Notions of robustness
  • 8.3 Case study: an implementation of robustness via forecast errors and quadratic constraints
  • 8.4 Extensions to the theory
  • 8.5 Conclusion
  • References
  • Chapter 9 Cross-sectional stock returns in the UK market: the role of liquidity risk
  • 9.1 Introduction
  • 9.2 Hypotheses and calculating factors
  • 9.3 Empirical results
  • 9.4 Conclusions
  • References
  • Chapter 10 The information horizon
  • optimal holding period, strategy aggression and model combination in a multi-horizon framework
  • 10.1 The information coefficient and information decay
  • 10.2 Returns and information decay in the single model case
  • 10.3 Model combination
  • 10.4 Information decay in models
  • 10.5 Models
  • optimal horizon, aggression and model combination
  • Reference
  • Chapter 11 Optimal forecasting horizon for skilled investo.

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