Forecasting expected returns in the financial markets:
Gespeichert in:
| Format: | Elektronisch E-Book |
|---|---|
| Sprache: | English |
| Veröffentlicht: |
Oxford
Elsevier/AP
2007
|
| Schriftenreihe: | Quantitative finance series
|
| Schlagworte: | |
| Online-Zugang: | FAW01 FAW02 Volltext |
| Beschreibung: | Includes bibliographical references and index 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 Dr Stephen Satchell brings together a collection of leading thinkers from around the world to address this complex and central challenge in portfolio management |
| Beschreibung: | 1 Online-Ressource (x, 286 p.) |
| ISBN: | 0080550673 1281057657 9780080550671 9780750683210 9781281057655 |
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| 500 | |a - 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 -- | ||
| 500 | |a - 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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| indexdate | 2024-07-10T07:18:26Z |
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| isbn | 0080550673 1281057657 9780080550671 9780750683210 9781281057655 |
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| spelling | Forecasting expected returns in the financial markets edited by Stephen Satchell Oxford Elsevier/AP 2007 1 Online-Ressource (x, 286 p.) txt rdacontent c rdamedia cr rdacarrier Quantitative finance series Includes bibliographical references and index 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 Dr Stephen Satchell brings together a collection of leading thinkers from around the world to address this complex and central challenge in portfolio management BUSINESS & ECONOMICS / Investments & Securities / Stocks bisacsh Mathematisches Modell Wirtschaft Stock price forecasting Stock price forecasting Mathematical models Investment analysis Finanzanalyse (DE-588)4133000-6 gnd rswk-swf Konjunkturprognose (DE-588)4139119-6 gnd rswk-swf Mathematisches Modell (DE-588)4114528-8 gnd rswk-swf Aktienkurs (DE-588)4141736-7 gnd rswk-swf Aktienkurs (DE-588)4141736-7 s Konjunkturprognose (DE-588)4139119-6 s Finanzanalyse (DE-588)4133000-6 s Mathematisches Modell (DE-588)4114528-8 s 1\p DE-604 Satchell, S. Sonstige oth Erscheint auch als Druck-Ausgabe, Hardcover 0-7506-8321-X http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=205483 Aggregator Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Forecasting expected returns in the financial markets BUSINESS & ECONOMICS / Investments & Securities / Stocks bisacsh Mathematisches Modell Wirtschaft Stock price forecasting Stock price forecasting Mathematical models Investment analysis Finanzanalyse (DE-588)4133000-6 gnd Konjunkturprognose (DE-588)4139119-6 gnd Mathematisches Modell (DE-588)4114528-8 gnd Aktienkurs (DE-588)4141736-7 gnd |
| subject_GND | (DE-588)4133000-6 (DE-588)4139119-6 (DE-588)4114528-8 (DE-588)4141736-7 |
| title | Forecasting expected returns in the financial markets |
| title_auth | Forecasting expected returns in the financial markets |
| title_exact_search | Forecasting expected returns in the financial markets |
| title_full | Forecasting expected returns in the financial markets edited by Stephen Satchell |
| title_fullStr | Forecasting expected returns in the financial markets edited by Stephen Satchell |
| title_full_unstemmed | Forecasting expected returns in the financial markets edited by Stephen Satchell |
| title_short | Forecasting expected returns in the financial markets |
| title_sort | forecasting expected returns in the financial markets |
| topic | BUSINESS & ECONOMICS / Investments & Securities / Stocks bisacsh Mathematisches Modell Wirtschaft Stock price forecasting Stock price forecasting Mathematical models Investment analysis Finanzanalyse (DE-588)4133000-6 gnd Konjunkturprognose (DE-588)4139119-6 gnd Mathematisches Modell (DE-588)4114528-8 gnd Aktienkurs (DE-588)4141736-7 gnd |
| topic_facet | BUSINESS & ECONOMICS / Investments & Securities / Stocks Mathematisches Modell Wirtschaft Stock price forecasting Stock price forecasting Mathematical models Investment analysis Finanzanalyse Konjunkturprognose Aktienkurs |
| url | http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=205483 |
| work_keys_str_mv | AT satchells forecastingexpectedreturnsinthefinancialmarkets |