The Adaptive Markets Hypothesis An Evolutionary Approach to Understanding Financial System Dynamics
The Adaptive Markets Hypothesis is a formal and systematic exposition. Lo and Zhang develop the mathematical foundations of the simple yet powerful evolutionary model and show that the most fundamental economic behaviours that we take for granted emerge solely through natural selection
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Oxford University Press, Incorporated
2024
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| Ausgabe: | 1st ed |
| Schriftenreihe: | Clarendon Lectures in Finance Series
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| 100 | 1 | |a Lo, Andrew W. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a The Adaptive Markets Hypothesis |b An Evolutionary Approach to Understanding Financial System Dynamics |
| 250 | |a 1st ed | ||
| 264 | 1 | |a Oxford |b Oxford University Press, Incorporated |c 2024 | |
| 264 | 4 | |c ©2024 | |
| 300 | |a 1 Online-Ressource (801 Seiten) | ||
| 336 | |b txt |2 rdacontent | ||
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| 338 | |b cr |2 rdacarrier | ||
| 490 | 0 | |a Clarendon Lectures in Finance Series | |
| 500 | |a Description based on publisher supplied metadata and other sources | ||
| 505 | 8 | |a Cover -- Title page -- Copyright page -- Acknowledgements -- Contents -- List of Figures -- List of Tables -- 1 Introduction and Roadmap -- 1.1 Behaviour and Rationality -- 1.2 'Anomalies' -- 1.3 Environment and Evolution -- 1.4 The Traditional Investment Paradigm -- 1.5 A New World Order -- 1.6 The Adaptive Markets Hypothesis -- 1.7 Practical Implications -- 1.8 Organizational Structure of the Book -- Part I. FOUNDATIONS -- 2 The Origin of Behaviour -- 2.1 The Binary Choice Model -- 2.1.1 The Role of Φ(xa,xb) -- 2.1.2 Individual Behaviour -- 2.1.3 Population Dynamics -- 2.1.4 Asymptotic Properties -- 2.2 Probability Matching -- 2.2.1 Exact Probability Matching -- 2.2.2 The General Case -- 2.2.3 Individually Optimal vs Growth-Optimal Behaviour -- 2.3 Risk Preferences -- 2.3.1 Growth-Optimal Risk Preferences -- 2.3.2 Risk Aversion -- 2.3.3 Loss Aversion -- 2.4 Idiosyncratic versus Systematic Risk -- 2.4.1 Idiosyncratic Risk -- 2.4.2 The General Case -- 2.5 Discussion -- 3 Mutation -- 3.1 Environments with Mutation -- 3.1.1 Asymptotic Population Dynamics -- 3.1.2 Extinction Probability -- 3.2 The Optimal Degree of Irrationality -- 3.2.1 An Example with Two Behaviours -- 3.3 Generalization and Simulation -- 3.3.1 Symmetric Regimes -- 3.3.2 Asymmetric Regimes -- 3.3.3 When Mutation Is Undesirable -- 3.3.4 Optimal Degree of Irrationality -- 3.4 Discussion -- 4 Group Selection -- 4.1 Environments with Factor Structure -- 4.2 Individual versus Group Optimality -- 4.3 Multinomial Choice with Multiple Factors -- 4.4 A Numerical Example -- 4.5 Discussion -- Part II. BEHAVIOUR -- 5 Probability Matching -- 5.1 The Binary Choice Game -- 5.2 Summary Statistics -- 5.3 A Model of Individual Behaviour -- 5.4 Initial Learning -- 5.5 Decision Autocorrelation -- 5.6 Probability Matching -- 5.7 Individual Differences -- 5.8 Discussion -- 6 Risk Aversion | |
| 505 | 8 | |a 6.1 Environments with Mixed Risks -- 6.2 Individual Preferences -- 6.3 Risk Aversion and Systematic Risk -- 6.4 Common Distributions of Relative Fecundity -- 6.5 Testable Implications -- 6.5.1 Biology and Behavioural Ecology -- 6.5.2 Financial Economics -- 6.5.3 The Equity Premium and Systematic Risk -- 6.6 Discussion -- 7 Cooperation -- 7.1 Environments with Interactions -- 7.1.1 Assumptions -- 7.1.2 Results -- 7.1.3 Evolutionary Optimality -- 7.1.4 Idiosyncratic Risk -- 7.1.5 Random Matching -- 7.1.6 Density Dependence -- 7.2 Behavioural Implications -- 7.2.1 Specialization -- 7.2.2 Sacrifice -- 7.2.3 Coordination -- 7.3 Discussion -- 8 Bounded Rationality and Intelligence -- 8.1 Environments with Intelligence -- 8.2 An Evolutionary Definition of Intelligence -- 8.3 Bounded Rationality -- 8.4 A Universal Measure of Intelligence and its Cost -- 8.5 Upper Bound on Correlation -- 8.6 Intelligence across Generations -- 8.6.1 No Inter-Generational Variation -- 8.6.2 Inter-Generational Variation -- 8.7 Discussion -- 9 Learning to be Bayesian -- 9.1 Environments with States -- 9.2 Bayesian Behaviours in Stationary Environments -- 9.3 What is Intelligence? Revisited -- 9.4 Finite Memory in Nonstationary Environments -- 9.5 Sampling with Limited Information -- 9.6 Discussion -- 10 The Madness of Mobs -- 10.1 Political Polarization -- 10.1.1 A Simple Example -- 10.1.2 The General Case -- 10.2 Bias and Discrimination -- 10.2.1 A Simple Example -- 10.2.2 Feedback Loops -- 10.2.3 Locally Evolutionarily Stable Strategies -- 10.2.4 Feedback Can Lead to Greater Bias -- 10.2.5 Path-Dependent Evolution -- 10.3 Practical Implications -- 10.4 Discussion -- Part III. NEURONS -- 11 Fear, Greed, and Financial Crises -- 11.1 A Brief History of the Brain -- 11.2 Fear -- 11.3 Greed -- 11.4 Risk -- 11.5 Rationality -- 11.6 Sentience -- 11.7 Interactions Among Components | |
| 505 | 8 | |a 11.8 Evolution at the Speed of Thought -- 11.9 Practical Implications -- 11.10 Discussion -- 12 The Psychophysiology of Trading -- 12.1 Measuring Emotional Response -- 12.2 Experimental Design -- 12.3 Results -- 12.4 Discussion -- 13 What Makes a Good Day Trader? -- 13.1 Risk-Taking and Emotion -- 13.1.1 Emotion, Personality, and Preferences -- 13.1.2 Emotional Response Metrics -- 13.2 Experimental Design -- 13.3 Results -- 13.3.1 Personality Traits and Trading Performance -- 13.3.2 Emotional States and Trading Performance -- 13.4 Discussion -- Part IV. FINANCIAL MARKET DYNAMICS -- 14 A Computational View of Market Efficiency -- 14.1 The Model -- 14.2 A Computational Definition of Market Efficiency -- 14.3 Market Evolution -- 14.3.1 Market Spikes -- 14.3.2 High-Memory Strategies Feed off Low-Memory Strategies -- 14.4 A Financial Turing Test -- 14.5 Experimental Design -- 14.6 Synthetic Processes and Results -- 14.6.1 Random Permutation -- 14.6.2 A Variant -- 14.6.3 AR(1) -- 14.6.4 Comparison of Random Permutation and AR(1) Results -- 14.6.5 Learning -- 14.7 Discussion -- 15 Maximizing Relative versus Absolute Wealth -- 15.1 The Kelly Criterion -- 15.2 Maximizing Relative Wealth -- 15.2.1 One-Period Results -- 15.2.2 Multi-Period Results -- 15.2.3 Infinite Horizon -- 15.3 A Numerical Example -- 15.3.1 Maximizing One-Period Relative Wealth -- 15.3.2 Maximizing Multi-Period Relative Wealth -- 15.4 Testable Implications -- 15.5 Discussion -- 16 Hedge Funds: The Galápagos Islands of Finance -- 16.1 Hedge Fund Characteristics -- 16.1.1 Fees -- 16.1.2 Leverage -- 16.1.3 Share Restrictions -- 16.1.4 Fund Flows and Capital Formation -- 16.2 An Overview of Hedge Fund Return Data -- 16.2.1 Data Sources -- 16.2.2 Biases -- 16.2.3 Entries and Exits -- 16.2.4 Hedge Fund Indexes -- 16.3 Investment Performance -- 16.3.1 Basic Performance Studies | |
| 505 | 8 | |a 16.3.2 Performance Persistence -- 16.3.3 Timing Ability -- 16.3.4 Hedge Fund Styles -- 16.4 Illiquidity -- 16.4.1 Measures of Illiquidity and Return Smoothing -- 16.4.2 Illiquidity and Statistical Biases -- 16.4.3 Measuring Illiquidity Risk Premia -- 16.4.4 The Mean-Variance-Illiquidity Frontier -- 16.5 Hedge Fund Risks -- 16.5.1 Value at Risk and Risk Shifting -- 16.5.2 Linear Factor Models -- 16.5.3 Limitations of Hedge Fund Factor Models -- 16.5.4 Operational Risks -- 16.5.5 Risk Management -- 16.5.6 Hedge Fund Beta Replication -- 16.6 The Financial Crisis -- 16.6.1 Early Warning Signs of the Crisis -- 16.6.2 Winners and Losers -- 16.6.3 Post-Crisis Performance -- 16.6.4 Hedge Funds and Systemic Risk -- 16.7 Implementation Issues for Hedge Fund Investing -- 16.7.1 The Limits of Mean-Variance Optimization -- 16.7.2 Over Diversification -- 16.7.3 Investment Implications -- 16.7.4 An Integrated Hedge Fund Investment Process -- 16.7.5 Implications for Adaptive Markets -- 16.8 Discussion -- 17 What Happened to the Quants in August 2007? -- 17.1 The Data -- 17.1.1 Compustat Data -- 17.1.2 TAQ Transactions Data -- 17.2 Factor Portfolios -- 17.2.1 Factor Construction -- 17.2.2 Market Behaviour in 2007 -- 17.2.3 Evidence from Transactions Data -- 17.3 Measures of Market Liquidity -- 17.3.1 Market-making and Contrarian Profits -- 17.3.2 Market Liquidity: 1995-2007 -- 17.3.3 Market Liquidity in 2007 -- 17.3.4 Determining the Epicentre of the Quake -- 17.4 Discussion -- Part V. FINANCIAL INSTITUTIONS AND ADAPTATION -- 18 The Co-Evolution of Financial Markets and Technology -- 18.1 Historical Context -- 18.2 The Evolution of Technology and Finance -- 18.3 Timetable of Financial Evolution -- 18.4 The Eight Eras of Financial Evolution -- 18.4.1 1944-1951: Classical Financial Era -- 18.4.2 1952-1963: The Modern Portfolio Theory Era | |
| 505 | 8 | |a 18.4.3 1964-1972: The Alpha Beta Era -- 18.4.4 1973-1981: The Derivatives Era -- 18.4.5 1982-1988: The Automation Era -- 18.4.6 1989-1999: The Financial Globalization Era -- 18.4.7 2000-2009: The Algorithmic Trading Era -- 18.4.8 2010-2021: The Digital Assets Era -- 18.5 Discussion -- 19 The Role of Culture in Finance -- 19.1 What is Culture? -- 19.2 What Determines Corporate Values? -- 19.2.1 Values from the Top Down: Authority and Leadership -- 19.2.2 Values from the Bottom Up: Composition -- 19.2.3 Values from the Environment: Risk and Regulation -- 19.2.4 Values from Economists: Responding to Incentives -- 19.2.5 Values from Evolution: The Adaptive Markets Hypothesis -- 19.3 Examples from the Financial Industry -- 19.4 Regulatory Culture -- 19.5 The Importance of Feedback Loops -- 19.6 Behavioural Risk Management -- 19.7 Discussion -- 20 Regulation and Adaptive Markets -- 20.1 Measures of Systemic Risk -- 20.2 The Shadow Banking System -- 20.3 The Shadow Hedge Fund System -- 20.4 Behavioural Foundations of Systemic Risk -- 20.5 A Process for Regulatory Design and Reform -- 20.6 The Capital Markets Safety Board -- 20.7 Transparency and Fair Value Accounting -- 20.8 The Role of Technology and Education -- 20.9 The Role of Corporate Governance -- 20.10 Discussion -- Epilogue -- Appendix A: Notational Glossary -- Appendix B: Proofs and Additional Results -- B.1 Chapter 2: The Binary Choice Model -- B.1.1 Proof of Proposition 2.1 -- B.1.2 Proof of Corollary 2.1 -- B.1.3 Proof of Proposition 2.2 -- B.1.4 Proof of Proposition 2.3 -- B.1.5 Proof of Corollary 2.2 -- B.1.6 Proof of Proposition 2.4 -- B.1.7 Proof of Proposition 2.5 -- B.1.8 Proof of Corollary 2.4 -- B.1.9 Proof of Corollary 2.5 -- B.2 Chapter 3: Mutation -- B.2.1 Proof of Lemma B.1 -- B.2.2 Proof of Proposition 3.1 -- B.2.3 Proof of Proposition 3.2 -- B.2.4 Proof of Proposition 3.3 | |
| 505 | 8 | |a B.2.5 Proof of Proposition 3.4 | |
| 520 | |a The Adaptive Markets Hypothesis is a formal and systematic exposition. Lo and Zhang develop the mathematical foundations of the simple yet powerful evolutionary model and show that the most fundamental economic behaviours that we take for granted emerge solely through natural selection | ||
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Datensatz im Suchindex
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| adam_text | |
| any_adam_object | |
| author | Lo, Andrew W. |
| author_facet | Lo, Andrew W. |
| author_role | aut |
| author_sort | Lo, Andrew W. |
| author_variant | a w l aw awl |
| building | Verbundindex |
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| contents | Cover -- Title page -- Copyright page -- Acknowledgements -- Contents -- List of Figures -- List of Tables -- 1 Introduction and Roadmap -- 1.1 Behaviour and Rationality -- 1.2 'Anomalies' -- 1.3 Environment and Evolution -- 1.4 The Traditional Investment Paradigm -- 1.5 A New World Order -- 1.6 The Adaptive Markets Hypothesis -- 1.7 Practical Implications -- 1.8 Organizational Structure of the Book -- Part I. FOUNDATIONS -- 2 The Origin of Behaviour -- 2.1 The Binary Choice Model -- 2.1.1 The Role of Φ(xa,xb) -- 2.1.2 Individual Behaviour -- 2.1.3 Population Dynamics -- 2.1.4 Asymptotic Properties -- 2.2 Probability Matching -- 2.2.1 Exact Probability Matching -- 2.2.2 The General Case -- 2.2.3 Individually Optimal vs Growth-Optimal Behaviour -- 2.3 Risk Preferences -- 2.3.1 Growth-Optimal Risk Preferences -- 2.3.2 Risk Aversion -- 2.3.3 Loss Aversion -- 2.4 Idiosyncratic versus Systematic Risk -- 2.4.1 Idiosyncratic Risk -- 2.4.2 The General Case -- 2.5 Discussion -- 3 Mutation -- 3.1 Environments with Mutation -- 3.1.1 Asymptotic Population Dynamics -- 3.1.2 Extinction Probability -- 3.2 The Optimal Degree of Irrationality -- 3.2.1 An Example with Two Behaviours -- 3.3 Generalization and Simulation -- 3.3.1 Symmetric Regimes -- 3.3.2 Asymmetric Regimes -- 3.3.3 When Mutation Is Undesirable -- 3.3.4 Optimal Degree of Irrationality -- 3.4 Discussion -- 4 Group Selection -- 4.1 Environments with Factor Structure -- 4.2 Individual versus Group Optimality -- 4.3 Multinomial Choice with Multiple Factors -- 4.4 A Numerical Example -- 4.5 Discussion -- Part II. BEHAVIOUR -- 5 Probability Matching -- 5.1 The Binary Choice Game -- 5.2 Summary Statistics -- 5.3 A Model of Individual Behaviour -- 5.4 Initial Learning -- 5.5 Decision Autocorrelation -- 5.6 Probability Matching -- 5.7 Individual Differences -- 5.8 Discussion -- 6 Risk Aversion 6.1 Environments with Mixed Risks -- 6.2 Individual Preferences -- 6.3 Risk Aversion and Systematic Risk -- 6.4 Common Distributions of Relative Fecundity -- 6.5 Testable Implications -- 6.5.1 Biology and Behavioural Ecology -- 6.5.2 Financial Economics -- 6.5.3 The Equity Premium and Systematic Risk -- 6.6 Discussion -- 7 Cooperation -- 7.1 Environments with Interactions -- 7.1.1 Assumptions -- 7.1.2 Results -- 7.1.3 Evolutionary Optimality -- 7.1.4 Idiosyncratic Risk -- 7.1.5 Random Matching -- 7.1.6 Density Dependence -- 7.2 Behavioural Implications -- 7.2.1 Specialization -- 7.2.2 Sacrifice -- 7.2.3 Coordination -- 7.3 Discussion -- 8 Bounded Rationality and Intelligence -- 8.1 Environments with Intelligence -- 8.2 An Evolutionary Definition of Intelligence -- 8.3 Bounded Rationality -- 8.4 A Universal Measure of Intelligence and its Cost -- 8.5 Upper Bound on Correlation -- 8.6 Intelligence across Generations -- 8.6.1 No Inter-Generational Variation -- 8.6.2 Inter-Generational Variation -- 8.7 Discussion -- 9 Learning to be Bayesian -- 9.1 Environments with States -- 9.2 Bayesian Behaviours in Stationary Environments -- 9.3 What is Intelligence? Revisited -- 9.4 Finite Memory in Nonstationary Environments -- 9.5 Sampling with Limited Information -- 9.6 Discussion -- 10 The Madness of Mobs -- 10.1 Political Polarization -- 10.1.1 A Simple Example -- 10.1.2 The General Case -- 10.2 Bias and Discrimination -- 10.2.1 A Simple Example -- 10.2.2 Feedback Loops -- 10.2.3 Locally Evolutionarily Stable Strategies -- 10.2.4 Feedback Can Lead to Greater Bias -- 10.2.5 Path-Dependent Evolution -- 10.3 Practical Implications -- 10.4 Discussion -- Part III. NEURONS -- 11 Fear, Greed, and Financial Crises -- 11.1 A Brief History of the Brain -- 11.2 Fear -- 11.3 Greed -- 11.4 Risk -- 11.5 Rationality -- 11.6 Sentience -- 11.7 Interactions Among Components 11.8 Evolution at the Speed of Thought -- 11.9 Practical Implications -- 11.10 Discussion -- 12 The Psychophysiology of Trading -- 12.1 Measuring Emotional Response -- 12.2 Experimental Design -- 12.3 Results -- 12.4 Discussion -- 13 What Makes a Good Day Trader? -- 13.1 Risk-Taking and Emotion -- 13.1.1 Emotion, Personality, and Preferences -- 13.1.2 Emotional Response Metrics -- 13.2 Experimental Design -- 13.3 Results -- 13.3.1 Personality Traits and Trading Performance -- 13.3.2 Emotional States and Trading Performance -- 13.4 Discussion -- Part IV. FINANCIAL MARKET DYNAMICS -- 14 A Computational View of Market Efficiency -- 14.1 The Model -- 14.2 A Computational Definition of Market Efficiency -- 14.3 Market Evolution -- 14.3.1 Market Spikes -- 14.3.2 High-Memory Strategies Feed off Low-Memory Strategies -- 14.4 A Financial Turing Test -- 14.5 Experimental Design -- 14.6 Synthetic Processes and Results -- 14.6.1 Random Permutation -- 14.6.2 A Variant -- 14.6.3 AR(1) -- 14.6.4 Comparison of Random Permutation and AR(1) Results -- 14.6.5 Learning -- 14.7 Discussion -- 15 Maximizing Relative versus Absolute Wealth -- 15.1 The Kelly Criterion -- 15.2 Maximizing Relative Wealth -- 15.2.1 One-Period Results -- 15.2.2 Multi-Period Results -- 15.2.3 Infinite Horizon -- 15.3 A Numerical Example -- 15.3.1 Maximizing One-Period Relative Wealth -- 15.3.2 Maximizing Multi-Period Relative Wealth -- 15.4 Testable Implications -- 15.5 Discussion -- 16 Hedge Funds: The Galápagos Islands of Finance -- 16.1 Hedge Fund Characteristics -- 16.1.1 Fees -- 16.1.2 Leverage -- 16.1.3 Share Restrictions -- 16.1.4 Fund Flows and Capital Formation -- 16.2 An Overview of Hedge Fund Return Data -- 16.2.1 Data Sources -- 16.2.2 Biases -- 16.2.3 Entries and Exits -- 16.2.4 Hedge Fund Indexes -- 16.3 Investment Performance -- 16.3.1 Basic Performance Studies 16.3.2 Performance Persistence -- 16.3.3 Timing Ability -- 16.3.4 Hedge Fund Styles -- 16.4 Illiquidity -- 16.4.1 Measures of Illiquidity and Return Smoothing -- 16.4.2 Illiquidity and Statistical Biases -- 16.4.3 Measuring Illiquidity Risk Premia -- 16.4.4 The Mean-Variance-Illiquidity Frontier -- 16.5 Hedge Fund Risks -- 16.5.1 Value at Risk and Risk Shifting -- 16.5.2 Linear Factor Models -- 16.5.3 Limitations of Hedge Fund Factor Models -- 16.5.4 Operational Risks -- 16.5.5 Risk Management -- 16.5.6 Hedge Fund Beta Replication -- 16.6 The Financial Crisis -- 16.6.1 Early Warning Signs of the Crisis -- 16.6.2 Winners and Losers -- 16.6.3 Post-Crisis Performance -- 16.6.4 Hedge Funds and Systemic Risk -- 16.7 Implementation Issues for Hedge Fund Investing -- 16.7.1 The Limits of Mean-Variance Optimization -- 16.7.2 Over Diversification -- 16.7.3 Investment Implications -- 16.7.4 An Integrated Hedge Fund Investment Process -- 16.7.5 Implications for Adaptive Markets -- 16.8 Discussion -- 17 What Happened to the Quants in August 2007? -- 17.1 The Data -- 17.1.1 Compustat Data -- 17.1.2 TAQ Transactions Data -- 17.2 Factor Portfolios -- 17.2.1 Factor Construction -- 17.2.2 Market Behaviour in 2007 -- 17.2.3 Evidence from Transactions Data -- 17.3 Measures of Market Liquidity -- 17.3.1 Market-making and Contrarian Profits -- 17.3.2 Market Liquidity: 1995-2007 -- 17.3.3 Market Liquidity in 2007 -- 17.3.4 Determining the Epicentre of the Quake -- 17.4 Discussion -- Part V. FINANCIAL INSTITUTIONS AND ADAPTATION -- 18 The Co-Evolution of Financial Markets and Technology -- 18.1 Historical Context -- 18.2 The Evolution of Technology and Finance -- 18.3 Timetable of Financial Evolution -- 18.4 The Eight Eras of Financial Evolution -- 18.4.1 1944-1951: Classical Financial Era -- 18.4.2 1952-1963: The Modern Portfolio Theory Era 18.4.3 1964-1972: The Alpha Beta Era -- 18.4.4 1973-1981: The Derivatives Era -- 18.4.5 1982-1988: The Automation Era -- 18.4.6 1989-1999: The Financial Globalization Era -- 18.4.7 2000-2009: The Algorithmic Trading Era -- 18.4.8 2010-2021: The Digital Assets Era -- 18.5 Discussion -- 19 The Role of Culture in Finance -- 19.1 What is Culture? -- 19.2 What Determines Corporate Values? -- 19.2.1 Values from the Top Down: Authority and Leadership -- 19.2.2 Values from the Bottom Up: Composition -- 19.2.3 Values from the Environment: Risk and Regulation -- 19.2.4 Values from Economists: Responding to Incentives -- 19.2.5 Values from Evolution: The Adaptive Markets Hypothesis -- 19.3 Examples from the Financial Industry -- 19.4 Regulatory Culture -- 19.5 The Importance of Feedback Loops -- 19.6 Behavioural Risk Management -- 19.7 Discussion -- 20 Regulation and Adaptive Markets -- 20.1 Measures of Systemic Risk -- 20.2 The Shadow Banking System -- 20.3 The Shadow Hedge Fund System -- 20.4 Behavioural Foundations of Systemic Risk -- 20.5 A Process for Regulatory Design and Reform -- 20.6 The Capital Markets Safety Board -- 20.7 Transparency and Fair Value Accounting -- 20.8 The Role of Technology and Education -- 20.9 The Role of Corporate Governance -- 20.10 Discussion -- Epilogue -- Appendix A: Notational Glossary -- Appendix B: Proofs and Additional Results -- B.1 Chapter 2: The Binary Choice Model -- B.1.1 Proof of Proposition 2.1 -- B.1.2 Proof of Corollary 2.1 -- B.1.3 Proof of Proposition 2.2 -- B.1.4 Proof of Proposition 2.3 -- B.1.5 Proof of Corollary 2.2 -- B.1.6 Proof of Proposition 2.4 -- B.1.7 Proof of Proposition 2.5 -- B.1.8 Proof of Corollary 2.4 -- B.1.9 Proof of Corollary 2.5 -- B.2 Chapter 3: Mutation -- B.2.1 Proof of Lemma B.1 -- B.2.2 Proof of Proposition 3.1 -- B.2.3 Proof of Proposition 3.2 -- B.2.4 Proof of Proposition 3.3 B.2.5 Proof of Proposition 3.4 |
| ctrlnum | (ZDB-30-PQE)EBC31131780 (ZDB-30-PAD)EBC31131780 (ZDB-89-EBL)EBL31131780 (OCoLC)1466901015 (DE-599)BVBBV049871734 |
| discipline | Wirtschaftswissenschaften |
| edition | 1st ed |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>00000nam a2200000zc 4500</leader><controlfield tag="001">BV049871734</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="007">cr|uuu---uuuuu</controlfield><controlfield tag="008">240918s2024 xx o|||| 00||| eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9780192885692</subfield><subfield code="9">978-0-19-288569-2</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZDB-30-PQE)EBC31131780</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZDB-30-PAD)EBC31131780</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZDB-89-EBL)EBL31131780</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1466901015</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV049871734</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-2070s</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">QK 640</subfield><subfield code="0">(DE-625)141673:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">QK 600</subfield><subfield code="0">(DE-625)141666:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Lo, Andrew W.</subfield><subfield code="e">Verfasser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">The Adaptive Markets Hypothesis</subfield><subfield code="b">An Evolutionary Approach to Understanding Financial System Dynamics</subfield></datafield><datafield tag="250" ind1=" " ind2=" "><subfield code="a">1st ed</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Oxford</subfield><subfield code="b">Oxford University Press, Incorporated</subfield><subfield code="c">2024</subfield></datafield><datafield tag="264" ind1=" " ind2="4"><subfield code="c">©2024</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (801 Seiten)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">Clarendon Lectures in Finance Series</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Description based on publisher supplied metadata and other sources</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Cover -- Title page -- Copyright page -- Acknowledgements -- Contents -- List of Figures -- List of Tables -- 1 Introduction and Roadmap -- 1.1 Behaviour and Rationality -- 1.2 'Anomalies' -- 1.3 Environment and Evolution -- 1.4 The Traditional Investment Paradigm -- 1.5 A New World Order -- 1.6 The Adaptive Markets Hypothesis -- 1.7 Practical Implications -- 1.8 Organizational Structure of the Book -- Part I. FOUNDATIONS -- 2 The Origin of Behaviour -- 2.1 The Binary Choice Model -- 2.1.1 The Role of Φ(xa,xb) -- 2.1.2 Individual Behaviour -- 2.1.3 Population Dynamics -- 2.1.4 Asymptotic Properties -- 2.2 Probability Matching -- 2.2.1 Exact Probability Matching -- 2.2.2 The General Case -- 2.2.3 Individually Optimal vs Growth-Optimal Behaviour -- 2.3 Risk Preferences -- 2.3.1 Growth-Optimal Risk Preferences -- 2.3.2 Risk Aversion -- 2.3.3 Loss Aversion -- 2.4 Idiosyncratic versus Systematic Risk -- 2.4.1 Idiosyncratic Risk -- 2.4.2 The General Case -- 2.5 Discussion -- 3 Mutation -- 3.1 Environments with Mutation -- 3.1.1 Asymptotic Population Dynamics -- 3.1.2 Extinction Probability -- 3.2 The Optimal Degree of Irrationality -- 3.2.1 An Example with Two Behaviours -- 3.3 Generalization and Simulation -- 3.3.1 Symmetric Regimes -- 3.3.2 Asymmetric Regimes -- 3.3.3 When Mutation Is Undesirable -- 3.3.4 Optimal Degree of Irrationality -- 3.4 Discussion -- 4 Group Selection -- 4.1 Environments with Factor Structure -- 4.2 Individual versus Group Optimality -- 4.3 Multinomial Choice with Multiple Factors -- 4.4 A Numerical Example -- 4.5 Discussion -- Part II. BEHAVIOUR -- 5 Probability Matching -- 5.1 The Binary Choice Game -- 5.2 Summary Statistics -- 5.3 A Model of Individual Behaviour -- 5.4 Initial Learning -- 5.5 Decision Autocorrelation -- 5.6 Probability Matching -- 5.7 Individual Differences -- 5.8 Discussion -- 6 Risk Aversion</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">6.1 Environments with Mixed Risks -- 6.2 Individual Preferences -- 6.3 Risk Aversion and Systematic Risk -- 6.4 Common Distributions of Relative Fecundity -- 6.5 Testable Implications -- 6.5.1 Biology and Behavioural Ecology -- 6.5.2 Financial Economics -- 6.5.3 The Equity Premium and Systematic Risk -- 6.6 Discussion -- 7 Cooperation -- 7.1 Environments with Interactions -- 7.1.1 Assumptions -- 7.1.2 Results -- 7.1.3 Evolutionary Optimality -- 7.1.4 Idiosyncratic Risk -- 7.1.5 Random Matching -- 7.1.6 Density Dependence -- 7.2 Behavioural Implications -- 7.2.1 Specialization -- 7.2.2 Sacrifice -- 7.2.3 Coordination -- 7.3 Discussion -- 8 Bounded Rationality and Intelligence -- 8.1 Environments with Intelligence -- 8.2 An Evolutionary Definition of Intelligence -- 8.3 Bounded Rationality -- 8.4 A Universal Measure of Intelligence and its Cost -- 8.5 Upper Bound on Correlation -- 8.6 Intelligence across Generations -- 8.6.1 No Inter-Generational Variation -- 8.6.2 Inter-Generational Variation -- 8.7 Discussion -- 9 Learning to be Bayesian -- 9.1 Environments with States -- 9.2 Bayesian Behaviours in Stationary Environments -- 9.3 What is Intelligence? Revisited -- 9.4 Finite Memory in Nonstationary Environments -- 9.5 Sampling with Limited Information -- 9.6 Discussion -- 10 The Madness of Mobs -- 10.1 Political Polarization -- 10.1.1 A Simple Example -- 10.1.2 The General Case -- 10.2 Bias and Discrimination -- 10.2.1 A Simple Example -- 10.2.2 Feedback Loops -- 10.2.3 Locally Evolutionarily Stable Strategies -- 10.2.4 Feedback Can Lead to Greater Bias -- 10.2.5 Path-Dependent Evolution -- 10.3 Practical Implications -- 10.4 Discussion -- Part III. NEURONS -- 11 Fear, Greed, and Financial Crises -- 11.1 A Brief History of the Brain -- 11.2 Fear -- 11.3 Greed -- 11.4 Risk -- 11.5 Rationality -- 11.6 Sentience -- 11.7 Interactions Among Components</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">11.8 Evolution at the Speed of Thought -- 11.9 Practical Implications -- 11.10 Discussion -- 12 The Psychophysiology of Trading -- 12.1 Measuring Emotional Response -- 12.2 Experimental Design -- 12.3 Results -- 12.4 Discussion -- 13 What Makes a Good Day Trader? -- 13.1 Risk-Taking and Emotion -- 13.1.1 Emotion, Personality, and Preferences -- 13.1.2 Emotional Response Metrics -- 13.2 Experimental Design -- 13.3 Results -- 13.3.1 Personality Traits and Trading Performance -- 13.3.2 Emotional States and Trading Performance -- 13.4 Discussion -- Part IV. FINANCIAL MARKET DYNAMICS -- 14 A Computational View of Market Efficiency -- 14.1 The Model -- 14.2 A Computational Definition of Market Efficiency -- 14.3 Market Evolution -- 14.3.1 Market Spikes -- 14.3.2 High-Memory Strategies Feed off Low-Memory Strategies -- 14.4 A Financial Turing Test -- 14.5 Experimental Design -- 14.6 Synthetic Processes and Results -- 14.6.1 Random Permutation -- 14.6.2 A Variant -- 14.6.3 AR(1) -- 14.6.4 Comparison of Random Permutation and AR(1) Results -- 14.6.5 Learning -- 14.7 Discussion -- 15 Maximizing Relative versus Absolute Wealth -- 15.1 The Kelly Criterion -- 15.2 Maximizing Relative Wealth -- 15.2.1 One-Period Results -- 15.2.2 Multi-Period Results -- 15.2.3 Infinite Horizon -- 15.3 A Numerical Example -- 15.3.1 Maximizing One-Period Relative Wealth -- 15.3.2 Maximizing Multi-Period Relative Wealth -- 15.4 Testable Implications -- 15.5 Discussion -- 16 Hedge Funds: The Galápagos Islands of Finance -- 16.1 Hedge Fund Characteristics -- 16.1.1 Fees -- 16.1.2 Leverage -- 16.1.3 Share Restrictions -- 16.1.4 Fund Flows and Capital Formation -- 16.2 An Overview of Hedge Fund Return Data -- 16.2.1 Data Sources -- 16.2.2 Biases -- 16.2.3 Entries and Exits -- 16.2.4 Hedge Fund Indexes -- 16.3 Investment Performance -- 16.3.1 Basic Performance Studies</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">16.3.2 Performance Persistence -- 16.3.3 Timing Ability -- 16.3.4 Hedge Fund Styles -- 16.4 Illiquidity -- 16.4.1 Measures of Illiquidity and Return Smoothing -- 16.4.2 Illiquidity and Statistical Biases -- 16.4.3 Measuring Illiquidity Risk Premia -- 16.4.4 The Mean-Variance-Illiquidity Frontier -- 16.5 Hedge Fund Risks -- 16.5.1 Value at Risk and Risk Shifting -- 16.5.2 Linear Factor Models -- 16.5.3 Limitations of Hedge Fund Factor Models -- 16.5.4 Operational Risks -- 16.5.5 Risk Management -- 16.5.6 Hedge Fund Beta Replication -- 16.6 The Financial Crisis -- 16.6.1 Early Warning Signs of the Crisis -- 16.6.2 Winners and Losers -- 16.6.3 Post-Crisis Performance -- 16.6.4 Hedge Funds and Systemic Risk -- 16.7 Implementation Issues for Hedge Fund Investing -- 16.7.1 The Limits of Mean-Variance Optimization -- 16.7.2 Over Diversification -- 16.7.3 Investment Implications -- 16.7.4 An Integrated Hedge Fund Investment Process -- 16.7.5 Implications for Adaptive Markets -- 16.8 Discussion -- 17 What Happened to the Quants in August 2007? -- 17.1 The Data -- 17.1.1 Compustat Data -- 17.1.2 TAQ Transactions Data -- 17.2 Factor Portfolios -- 17.2.1 Factor Construction -- 17.2.2 Market Behaviour in 2007 -- 17.2.3 Evidence from Transactions Data -- 17.3 Measures of Market Liquidity -- 17.3.1 Market-making and Contrarian Profits -- 17.3.2 Market Liquidity: 1995-2007 -- 17.3.3 Market Liquidity in 2007 -- 17.3.4 Determining the Epicentre of the Quake -- 17.4 Discussion -- Part V. FINANCIAL INSTITUTIONS AND ADAPTATION -- 18 The Co-Evolution of Financial Markets and Technology -- 18.1 Historical Context -- 18.2 The Evolution of Technology and Finance -- 18.3 Timetable of Financial Evolution -- 18.4 The Eight Eras of Financial Evolution -- 18.4.1 1944-1951: Classical Financial Era -- 18.4.2 1952-1963: The Modern Portfolio Theory Era</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">18.4.3 1964-1972: The Alpha Beta Era -- 18.4.4 1973-1981: The Derivatives Era -- 18.4.5 1982-1988: The Automation Era -- 18.4.6 1989-1999: The Financial Globalization Era -- 18.4.7 2000-2009: The Algorithmic Trading Era -- 18.4.8 2010-2021: The Digital Assets Era -- 18.5 Discussion -- 19 The Role of Culture in Finance -- 19.1 What is Culture? -- 19.2 What Determines Corporate Values? -- 19.2.1 Values from the Top Down: Authority and Leadership -- 19.2.2 Values from the Bottom Up: Composition -- 19.2.3 Values from the Environment: Risk and Regulation -- 19.2.4 Values from Economists: Responding to Incentives -- 19.2.5 Values from Evolution: The Adaptive Markets Hypothesis -- 19.3 Examples from the Financial Industry -- 19.4 Regulatory Culture -- 19.5 The Importance of Feedback Loops -- 19.6 Behavioural Risk Management -- 19.7 Discussion -- 20 Regulation and Adaptive Markets -- 20.1 Measures of Systemic Risk -- 20.2 The Shadow Banking System -- 20.3 The Shadow Hedge Fund System -- 20.4 Behavioural Foundations of Systemic Risk -- 20.5 A Process for Regulatory Design and Reform -- 20.6 The Capital Markets Safety Board -- 20.7 Transparency and Fair Value Accounting -- 20.8 The Role of Technology and Education -- 20.9 The Role of Corporate Governance -- 20.10 Discussion -- Epilogue -- Appendix A: Notational Glossary -- Appendix B: Proofs and Additional Results -- B.1 Chapter 2: The Binary Choice Model -- B.1.1 Proof of Proposition 2.1 -- B.1.2 Proof of Corollary 2.1 -- B.1.3 Proof of Proposition 2.2 -- B.1.4 Proof of Proposition 2.3 -- B.1.5 Proof of Corollary 2.2 -- B.1.6 Proof of Proposition 2.4 -- B.1.7 Proof of Proposition 2.5 -- B.1.8 Proof of Corollary 2.4 -- B.1.9 Proof of Corollary 2.5 -- B.2 Chapter 3: Mutation -- B.2.1 Proof of Lemma B.1 -- B.2.2 Proof of Proposition 3.1 -- B.2.3 Proof of Proposition 3.2 -- B.2.4 Proof of Proposition 3.3</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">B.2.5 Proof of Proposition 3.4</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The Adaptive Markets Hypothesis is a formal and systematic exposition. 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| id | DE-604.BV049871734 |
| illustrated | Not Illustrated |
| indexdate | 2025-01-27T14:00:23Z |
| institution | BVB |
| isbn | 9780192885692 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-035211209 |
| oclc_num | 1466901015 |
| open_access_boolean | |
| owner | DE-2070s |
| owner_facet | DE-2070s |
| physical | 1 Online-Ressource (801 Seiten) |
| psigel | ZDB-30-PQE ZDB-30-PQE HWR_PDA_PQE |
| publishDate | 2024 |
| publishDateSearch | 2024 |
| publishDateSort | 2024 |
| publisher | Oxford University Press, Incorporated |
| record_format | marc |
| series2 | Clarendon Lectures in Finance Series |
| spelling | Lo, Andrew W. Verfasser aut The Adaptive Markets Hypothesis An Evolutionary Approach to Understanding Financial System Dynamics 1st ed Oxford Oxford University Press, Incorporated 2024 ©2024 1 Online-Ressource (801 Seiten) txt rdacontent c rdamedia cr rdacarrier Clarendon Lectures in Finance Series Description based on publisher supplied metadata and other sources Cover -- Title page -- Copyright page -- Acknowledgements -- Contents -- List of Figures -- List of Tables -- 1 Introduction and Roadmap -- 1.1 Behaviour and Rationality -- 1.2 'Anomalies' -- 1.3 Environment and Evolution -- 1.4 The Traditional Investment Paradigm -- 1.5 A New World Order -- 1.6 The Adaptive Markets Hypothesis -- 1.7 Practical Implications -- 1.8 Organizational Structure of the Book -- Part I. FOUNDATIONS -- 2 The Origin of Behaviour -- 2.1 The Binary Choice Model -- 2.1.1 The Role of Φ(xa,xb) -- 2.1.2 Individual Behaviour -- 2.1.3 Population Dynamics -- 2.1.4 Asymptotic Properties -- 2.2 Probability Matching -- 2.2.1 Exact Probability Matching -- 2.2.2 The General Case -- 2.2.3 Individually Optimal vs Growth-Optimal Behaviour -- 2.3 Risk Preferences -- 2.3.1 Growth-Optimal Risk Preferences -- 2.3.2 Risk Aversion -- 2.3.3 Loss Aversion -- 2.4 Idiosyncratic versus Systematic Risk -- 2.4.1 Idiosyncratic Risk -- 2.4.2 The General Case -- 2.5 Discussion -- 3 Mutation -- 3.1 Environments with Mutation -- 3.1.1 Asymptotic Population Dynamics -- 3.1.2 Extinction Probability -- 3.2 The Optimal Degree of Irrationality -- 3.2.1 An Example with Two Behaviours -- 3.3 Generalization and Simulation -- 3.3.1 Symmetric Regimes -- 3.3.2 Asymmetric Regimes -- 3.3.3 When Mutation Is Undesirable -- 3.3.4 Optimal Degree of Irrationality -- 3.4 Discussion -- 4 Group Selection -- 4.1 Environments with Factor Structure -- 4.2 Individual versus Group Optimality -- 4.3 Multinomial Choice with Multiple Factors -- 4.4 A Numerical Example -- 4.5 Discussion -- Part II. BEHAVIOUR -- 5 Probability Matching -- 5.1 The Binary Choice Game -- 5.2 Summary Statistics -- 5.3 A Model of Individual Behaviour -- 5.4 Initial Learning -- 5.5 Decision Autocorrelation -- 5.6 Probability Matching -- 5.7 Individual Differences -- 5.8 Discussion -- 6 Risk Aversion 6.1 Environments with Mixed Risks -- 6.2 Individual Preferences -- 6.3 Risk Aversion and Systematic Risk -- 6.4 Common Distributions of Relative Fecundity -- 6.5 Testable Implications -- 6.5.1 Biology and Behavioural Ecology -- 6.5.2 Financial Economics -- 6.5.3 The Equity Premium and Systematic Risk -- 6.6 Discussion -- 7 Cooperation -- 7.1 Environments with Interactions -- 7.1.1 Assumptions -- 7.1.2 Results -- 7.1.3 Evolutionary Optimality -- 7.1.4 Idiosyncratic Risk -- 7.1.5 Random Matching -- 7.1.6 Density Dependence -- 7.2 Behavioural Implications -- 7.2.1 Specialization -- 7.2.2 Sacrifice -- 7.2.3 Coordination -- 7.3 Discussion -- 8 Bounded Rationality and Intelligence -- 8.1 Environments with Intelligence -- 8.2 An Evolutionary Definition of Intelligence -- 8.3 Bounded Rationality -- 8.4 A Universal Measure of Intelligence and its Cost -- 8.5 Upper Bound on Correlation -- 8.6 Intelligence across Generations -- 8.6.1 No Inter-Generational Variation -- 8.6.2 Inter-Generational Variation -- 8.7 Discussion -- 9 Learning to be Bayesian -- 9.1 Environments with States -- 9.2 Bayesian Behaviours in Stationary Environments -- 9.3 What is Intelligence? Revisited -- 9.4 Finite Memory in Nonstationary Environments -- 9.5 Sampling with Limited Information -- 9.6 Discussion -- 10 The Madness of Mobs -- 10.1 Political Polarization -- 10.1.1 A Simple Example -- 10.1.2 The General Case -- 10.2 Bias and Discrimination -- 10.2.1 A Simple Example -- 10.2.2 Feedback Loops -- 10.2.3 Locally Evolutionarily Stable Strategies -- 10.2.4 Feedback Can Lead to Greater Bias -- 10.2.5 Path-Dependent Evolution -- 10.3 Practical Implications -- 10.4 Discussion -- Part III. NEURONS -- 11 Fear, Greed, and Financial Crises -- 11.1 A Brief History of the Brain -- 11.2 Fear -- 11.3 Greed -- 11.4 Risk -- 11.5 Rationality -- 11.6 Sentience -- 11.7 Interactions Among Components 11.8 Evolution at the Speed of Thought -- 11.9 Practical Implications -- 11.10 Discussion -- 12 The Psychophysiology of Trading -- 12.1 Measuring Emotional Response -- 12.2 Experimental Design -- 12.3 Results -- 12.4 Discussion -- 13 What Makes a Good Day Trader? -- 13.1 Risk-Taking and Emotion -- 13.1.1 Emotion, Personality, and Preferences -- 13.1.2 Emotional Response Metrics -- 13.2 Experimental Design -- 13.3 Results -- 13.3.1 Personality Traits and Trading Performance -- 13.3.2 Emotional States and Trading Performance -- 13.4 Discussion -- Part IV. FINANCIAL MARKET DYNAMICS -- 14 A Computational View of Market Efficiency -- 14.1 The Model -- 14.2 A Computational Definition of Market Efficiency -- 14.3 Market Evolution -- 14.3.1 Market Spikes -- 14.3.2 High-Memory Strategies Feed off Low-Memory Strategies -- 14.4 A Financial Turing Test -- 14.5 Experimental Design -- 14.6 Synthetic Processes and Results -- 14.6.1 Random Permutation -- 14.6.2 A Variant -- 14.6.3 AR(1) -- 14.6.4 Comparison of Random Permutation and AR(1) Results -- 14.6.5 Learning -- 14.7 Discussion -- 15 Maximizing Relative versus Absolute Wealth -- 15.1 The Kelly Criterion -- 15.2 Maximizing Relative Wealth -- 15.2.1 One-Period Results -- 15.2.2 Multi-Period Results -- 15.2.3 Infinite Horizon -- 15.3 A Numerical Example -- 15.3.1 Maximizing One-Period Relative Wealth -- 15.3.2 Maximizing Multi-Period Relative Wealth -- 15.4 Testable Implications -- 15.5 Discussion -- 16 Hedge Funds: The Galápagos Islands of Finance -- 16.1 Hedge Fund Characteristics -- 16.1.1 Fees -- 16.1.2 Leverage -- 16.1.3 Share Restrictions -- 16.1.4 Fund Flows and Capital Formation -- 16.2 An Overview of Hedge Fund Return Data -- 16.2.1 Data Sources -- 16.2.2 Biases -- 16.2.3 Entries and Exits -- 16.2.4 Hedge Fund Indexes -- 16.3 Investment Performance -- 16.3.1 Basic Performance Studies 16.3.2 Performance Persistence -- 16.3.3 Timing Ability -- 16.3.4 Hedge Fund Styles -- 16.4 Illiquidity -- 16.4.1 Measures of Illiquidity and Return Smoothing -- 16.4.2 Illiquidity and Statistical Biases -- 16.4.3 Measuring Illiquidity Risk Premia -- 16.4.4 The Mean-Variance-Illiquidity Frontier -- 16.5 Hedge Fund Risks -- 16.5.1 Value at Risk and Risk Shifting -- 16.5.2 Linear Factor Models -- 16.5.3 Limitations of Hedge Fund Factor Models -- 16.5.4 Operational Risks -- 16.5.5 Risk Management -- 16.5.6 Hedge Fund Beta Replication -- 16.6 The Financial Crisis -- 16.6.1 Early Warning Signs of the Crisis -- 16.6.2 Winners and Losers -- 16.6.3 Post-Crisis Performance -- 16.6.4 Hedge Funds and Systemic Risk -- 16.7 Implementation Issues for Hedge Fund Investing -- 16.7.1 The Limits of Mean-Variance Optimization -- 16.7.2 Over Diversification -- 16.7.3 Investment Implications -- 16.7.4 An Integrated Hedge Fund Investment Process -- 16.7.5 Implications for Adaptive Markets -- 16.8 Discussion -- 17 What Happened to the Quants in August 2007? -- 17.1 The Data -- 17.1.1 Compustat Data -- 17.1.2 TAQ Transactions Data -- 17.2 Factor Portfolios -- 17.2.1 Factor Construction -- 17.2.2 Market Behaviour in 2007 -- 17.2.3 Evidence from Transactions Data -- 17.3 Measures of Market Liquidity -- 17.3.1 Market-making and Contrarian Profits -- 17.3.2 Market Liquidity: 1995-2007 -- 17.3.3 Market Liquidity in 2007 -- 17.3.4 Determining the Epicentre of the Quake -- 17.4 Discussion -- Part V. FINANCIAL INSTITUTIONS AND ADAPTATION -- 18 The Co-Evolution of Financial Markets and Technology -- 18.1 Historical Context -- 18.2 The Evolution of Technology and Finance -- 18.3 Timetable of Financial Evolution -- 18.4 The Eight Eras of Financial Evolution -- 18.4.1 1944-1951: Classical Financial Era -- 18.4.2 1952-1963: The Modern Portfolio Theory Era 18.4.3 1964-1972: The Alpha Beta Era -- 18.4.4 1973-1981: The Derivatives Era -- 18.4.5 1982-1988: The Automation Era -- 18.4.6 1989-1999: The Financial Globalization Era -- 18.4.7 2000-2009: The Algorithmic Trading Era -- 18.4.8 2010-2021: The Digital Assets Era -- 18.5 Discussion -- 19 The Role of Culture in Finance -- 19.1 What is Culture? -- 19.2 What Determines Corporate Values? -- 19.2.1 Values from the Top Down: Authority and Leadership -- 19.2.2 Values from the Bottom Up: Composition -- 19.2.3 Values from the Environment: Risk and Regulation -- 19.2.4 Values from Economists: Responding to Incentives -- 19.2.5 Values from Evolution: The Adaptive Markets Hypothesis -- 19.3 Examples from the Financial Industry -- 19.4 Regulatory Culture -- 19.5 The Importance of Feedback Loops -- 19.6 Behavioural Risk Management -- 19.7 Discussion -- 20 Regulation and Adaptive Markets -- 20.1 Measures of Systemic Risk -- 20.2 The Shadow Banking System -- 20.3 The Shadow Hedge Fund System -- 20.4 Behavioural Foundations of Systemic Risk -- 20.5 A Process for Regulatory Design and Reform -- 20.6 The Capital Markets Safety Board -- 20.7 Transparency and Fair Value Accounting -- 20.8 The Role of Technology and Education -- 20.9 The Role of Corporate Governance -- 20.10 Discussion -- Epilogue -- Appendix A: Notational Glossary -- Appendix B: Proofs and Additional Results -- B.1 Chapter 2: The Binary Choice Model -- B.1.1 Proof of Proposition 2.1 -- B.1.2 Proof of Corollary 2.1 -- B.1.3 Proof of Proposition 2.2 -- B.1.4 Proof of Proposition 2.3 -- B.1.5 Proof of Corollary 2.2 -- B.1.6 Proof of Proposition 2.4 -- B.1.7 Proof of Proposition 2.5 -- B.1.8 Proof of Corollary 2.4 -- B.1.9 Proof of Corollary 2.5 -- B.2 Chapter 3: Mutation -- B.2.1 Proof of Lemma B.1 -- B.2.2 Proof of Proposition 3.1 -- B.2.3 Proof of Proposition 3.2 -- B.2.4 Proof of Proposition 3.3 B.2.5 Proof of Proposition 3.4 The Adaptive Markets Hypothesis is a formal and systematic exposition. Lo and Zhang develop the mathematical foundations of the simple yet powerful evolutionary model and show that the most fundamental economic behaviours that we take for granted emerge solely through natural selection Neuroökonomik (DE-588)7613972-4 gnd rswk-swf Kreditmarkt (DE-588)4073788-3 gnd rswk-swf Anlageverhalten (DE-588)4214003-1 gnd rswk-swf Marktverhalten (DE-588)4135561-1 gnd rswk-swf Volatilität (DE-588)4268390-7 gnd rswk-swf Wirtschaftliches Verhalten (DE-588)4197971-0 gnd rswk-swf Marktpsychologie (DE-588)4140888-3 gnd rswk-swf Anlageverhalten (DE-588)4214003-1 s Kreditmarkt (DE-588)4073788-3 s Marktpsychologie (DE-588)4140888-3 s Marktverhalten (DE-588)4135561-1 s Neuroökonomik (DE-588)7613972-4 s Volatilität (DE-588)4268390-7 s Wirtschaftliches Verhalten (DE-588)4197971-0 s DE-604 Zhang, Ruixun Sonstige oth Erscheint auch als Druck-Ausgabe Lo, Andrew W. The Adaptive Markets Hypothesis Oxford : Oxford University Press, Incorporated,c2024 9780199681143 |
| spellingShingle | Lo, Andrew W. The Adaptive Markets Hypothesis An Evolutionary Approach to Understanding Financial System Dynamics Cover -- Title page -- Copyright page -- Acknowledgements -- Contents -- List of Figures -- List of Tables -- 1 Introduction and Roadmap -- 1.1 Behaviour and Rationality -- 1.2 'Anomalies' -- 1.3 Environment and Evolution -- 1.4 The Traditional Investment Paradigm -- 1.5 A New World Order -- 1.6 The Adaptive Markets Hypothesis -- 1.7 Practical Implications -- 1.8 Organizational Structure of the Book -- Part I. FOUNDATIONS -- 2 The Origin of Behaviour -- 2.1 The Binary Choice Model -- 2.1.1 The Role of Φ(xa,xb) -- 2.1.2 Individual Behaviour -- 2.1.3 Population Dynamics -- 2.1.4 Asymptotic Properties -- 2.2 Probability Matching -- 2.2.1 Exact Probability Matching -- 2.2.2 The General Case -- 2.2.3 Individually Optimal vs Growth-Optimal Behaviour -- 2.3 Risk Preferences -- 2.3.1 Growth-Optimal Risk Preferences -- 2.3.2 Risk Aversion -- 2.3.3 Loss Aversion -- 2.4 Idiosyncratic versus Systematic Risk -- 2.4.1 Idiosyncratic Risk -- 2.4.2 The General Case -- 2.5 Discussion -- 3 Mutation -- 3.1 Environments with Mutation -- 3.1.1 Asymptotic Population Dynamics -- 3.1.2 Extinction Probability -- 3.2 The Optimal Degree of Irrationality -- 3.2.1 An Example with Two Behaviours -- 3.3 Generalization and Simulation -- 3.3.1 Symmetric Regimes -- 3.3.2 Asymmetric Regimes -- 3.3.3 When Mutation Is Undesirable -- 3.3.4 Optimal Degree of Irrationality -- 3.4 Discussion -- 4 Group Selection -- 4.1 Environments with Factor Structure -- 4.2 Individual versus Group Optimality -- 4.3 Multinomial Choice with Multiple Factors -- 4.4 A Numerical Example -- 4.5 Discussion -- Part II. BEHAVIOUR -- 5 Probability Matching -- 5.1 The Binary Choice Game -- 5.2 Summary Statistics -- 5.3 A Model of Individual Behaviour -- 5.4 Initial Learning -- 5.5 Decision Autocorrelation -- 5.6 Probability Matching -- 5.7 Individual Differences -- 5.8 Discussion -- 6 Risk Aversion 6.1 Environments with Mixed Risks -- 6.2 Individual Preferences -- 6.3 Risk Aversion and Systematic Risk -- 6.4 Common Distributions of Relative Fecundity -- 6.5 Testable Implications -- 6.5.1 Biology and Behavioural Ecology -- 6.5.2 Financial Economics -- 6.5.3 The Equity Premium and Systematic Risk -- 6.6 Discussion -- 7 Cooperation -- 7.1 Environments with Interactions -- 7.1.1 Assumptions -- 7.1.2 Results -- 7.1.3 Evolutionary Optimality -- 7.1.4 Idiosyncratic Risk -- 7.1.5 Random Matching -- 7.1.6 Density Dependence -- 7.2 Behavioural Implications -- 7.2.1 Specialization -- 7.2.2 Sacrifice -- 7.2.3 Coordination -- 7.3 Discussion -- 8 Bounded Rationality and Intelligence -- 8.1 Environments with Intelligence -- 8.2 An Evolutionary Definition of Intelligence -- 8.3 Bounded Rationality -- 8.4 A Universal Measure of Intelligence and its Cost -- 8.5 Upper Bound on Correlation -- 8.6 Intelligence across Generations -- 8.6.1 No Inter-Generational Variation -- 8.6.2 Inter-Generational Variation -- 8.7 Discussion -- 9 Learning to be Bayesian -- 9.1 Environments with States -- 9.2 Bayesian Behaviours in Stationary Environments -- 9.3 What is Intelligence? Revisited -- 9.4 Finite Memory in Nonstationary Environments -- 9.5 Sampling with Limited Information -- 9.6 Discussion -- 10 The Madness of Mobs -- 10.1 Political Polarization -- 10.1.1 A Simple Example -- 10.1.2 The General Case -- 10.2 Bias and Discrimination -- 10.2.1 A Simple Example -- 10.2.2 Feedback Loops -- 10.2.3 Locally Evolutionarily Stable Strategies -- 10.2.4 Feedback Can Lead to Greater Bias -- 10.2.5 Path-Dependent Evolution -- 10.3 Practical Implications -- 10.4 Discussion -- Part III. NEURONS -- 11 Fear, Greed, and Financial Crises -- 11.1 A Brief History of the Brain -- 11.2 Fear -- 11.3 Greed -- 11.4 Risk -- 11.5 Rationality -- 11.6 Sentience -- 11.7 Interactions Among Components 11.8 Evolution at the Speed of Thought -- 11.9 Practical Implications -- 11.10 Discussion -- 12 The Psychophysiology of Trading -- 12.1 Measuring Emotional Response -- 12.2 Experimental Design -- 12.3 Results -- 12.4 Discussion -- 13 What Makes a Good Day Trader? -- 13.1 Risk-Taking and Emotion -- 13.1.1 Emotion, Personality, and Preferences -- 13.1.2 Emotional Response Metrics -- 13.2 Experimental Design -- 13.3 Results -- 13.3.1 Personality Traits and Trading Performance -- 13.3.2 Emotional States and Trading Performance -- 13.4 Discussion -- Part IV. FINANCIAL MARKET DYNAMICS -- 14 A Computational View of Market Efficiency -- 14.1 The Model -- 14.2 A Computational Definition of Market Efficiency -- 14.3 Market Evolution -- 14.3.1 Market Spikes -- 14.3.2 High-Memory Strategies Feed off Low-Memory Strategies -- 14.4 A Financial Turing Test -- 14.5 Experimental Design -- 14.6 Synthetic Processes and Results -- 14.6.1 Random Permutation -- 14.6.2 A Variant -- 14.6.3 AR(1) -- 14.6.4 Comparison of Random Permutation and AR(1) Results -- 14.6.5 Learning -- 14.7 Discussion -- 15 Maximizing Relative versus Absolute Wealth -- 15.1 The Kelly Criterion -- 15.2 Maximizing Relative Wealth -- 15.2.1 One-Period Results -- 15.2.2 Multi-Period Results -- 15.2.3 Infinite Horizon -- 15.3 A Numerical Example -- 15.3.1 Maximizing One-Period Relative Wealth -- 15.3.2 Maximizing Multi-Period Relative Wealth -- 15.4 Testable Implications -- 15.5 Discussion -- 16 Hedge Funds: The Galápagos Islands of Finance -- 16.1 Hedge Fund Characteristics -- 16.1.1 Fees -- 16.1.2 Leverage -- 16.1.3 Share Restrictions -- 16.1.4 Fund Flows and Capital Formation -- 16.2 An Overview of Hedge Fund Return Data -- 16.2.1 Data Sources -- 16.2.2 Biases -- 16.2.3 Entries and Exits -- 16.2.4 Hedge Fund Indexes -- 16.3 Investment Performance -- 16.3.1 Basic Performance Studies 16.3.2 Performance Persistence -- 16.3.3 Timing Ability -- 16.3.4 Hedge Fund Styles -- 16.4 Illiquidity -- 16.4.1 Measures of Illiquidity and Return Smoothing -- 16.4.2 Illiquidity and Statistical Biases -- 16.4.3 Measuring Illiquidity Risk Premia -- 16.4.4 The Mean-Variance-Illiquidity Frontier -- 16.5 Hedge Fund Risks -- 16.5.1 Value at Risk and Risk Shifting -- 16.5.2 Linear Factor Models -- 16.5.3 Limitations of Hedge Fund Factor Models -- 16.5.4 Operational Risks -- 16.5.5 Risk Management -- 16.5.6 Hedge Fund Beta Replication -- 16.6 The Financial Crisis -- 16.6.1 Early Warning Signs of the Crisis -- 16.6.2 Winners and Losers -- 16.6.3 Post-Crisis Performance -- 16.6.4 Hedge Funds and Systemic Risk -- 16.7 Implementation Issues for Hedge Fund Investing -- 16.7.1 The Limits of Mean-Variance Optimization -- 16.7.2 Over Diversification -- 16.7.3 Investment Implications -- 16.7.4 An Integrated Hedge Fund Investment Process -- 16.7.5 Implications for Adaptive Markets -- 16.8 Discussion -- 17 What Happened to the Quants in August 2007? -- 17.1 The Data -- 17.1.1 Compustat Data -- 17.1.2 TAQ Transactions Data -- 17.2 Factor Portfolios -- 17.2.1 Factor Construction -- 17.2.2 Market Behaviour in 2007 -- 17.2.3 Evidence from Transactions Data -- 17.3 Measures of Market Liquidity -- 17.3.1 Market-making and Contrarian Profits -- 17.3.2 Market Liquidity: 1995-2007 -- 17.3.3 Market Liquidity in 2007 -- 17.3.4 Determining the Epicentre of the Quake -- 17.4 Discussion -- Part V. FINANCIAL INSTITUTIONS AND ADAPTATION -- 18 The Co-Evolution of Financial Markets and Technology -- 18.1 Historical Context -- 18.2 The Evolution of Technology and Finance -- 18.3 Timetable of Financial Evolution -- 18.4 The Eight Eras of Financial Evolution -- 18.4.1 1944-1951: Classical Financial Era -- 18.4.2 1952-1963: The Modern Portfolio Theory Era 18.4.3 1964-1972: The Alpha Beta Era -- 18.4.4 1973-1981: The Derivatives Era -- 18.4.5 1982-1988: The Automation Era -- 18.4.6 1989-1999: The Financial Globalization Era -- 18.4.7 2000-2009: The Algorithmic Trading Era -- 18.4.8 2010-2021: The Digital Assets Era -- 18.5 Discussion -- 19 The Role of Culture in Finance -- 19.1 What is Culture? -- 19.2 What Determines Corporate Values? -- 19.2.1 Values from the Top Down: Authority and Leadership -- 19.2.2 Values from the Bottom Up: Composition -- 19.2.3 Values from the Environment: Risk and Regulation -- 19.2.4 Values from Economists: Responding to Incentives -- 19.2.5 Values from Evolution: The Adaptive Markets Hypothesis -- 19.3 Examples from the Financial Industry -- 19.4 Regulatory Culture -- 19.5 The Importance of Feedback Loops -- 19.6 Behavioural Risk Management -- 19.7 Discussion -- 20 Regulation and Adaptive Markets -- 20.1 Measures of Systemic Risk -- 20.2 The Shadow Banking System -- 20.3 The Shadow Hedge Fund System -- 20.4 Behavioural Foundations of Systemic Risk -- 20.5 A Process for Regulatory Design and Reform -- 20.6 The Capital Markets Safety Board -- 20.7 Transparency and Fair Value Accounting -- 20.8 The Role of Technology and Education -- 20.9 The Role of Corporate Governance -- 20.10 Discussion -- Epilogue -- Appendix A: Notational Glossary -- Appendix B: Proofs and Additional Results -- B.1 Chapter 2: The Binary Choice Model -- B.1.1 Proof of Proposition 2.1 -- B.1.2 Proof of Corollary 2.1 -- B.1.3 Proof of Proposition 2.2 -- B.1.4 Proof of Proposition 2.3 -- B.1.5 Proof of Corollary 2.2 -- B.1.6 Proof of Proposition 2.4 -- B.1.7 Proof of Proposition 2.5 -- B.1.8 Proof of Corollary 2.4 -- B.1.9 Proof of Corollary 2.5 -- B.2 Chapter 3: Mutation -- B.2.1 Proof of Lemma B.1 -- B.2.2 Proof of Proposition 3.1 -- B.2.3 Proof of Proposition 3.2 -- B.2.4 Proof of Proposition 3.3 B.2.5 Proof of Proposition 3.4 Neuroökonomik (DE-588)7613972-4 gnd Kreditmarkt (DE-588)4073788-3 gnd Anlageverhalten (DE-588)4214003-1 gnd Marktverhalten (DE-588)4135561-1 gnd Volatilität (DE-588)4268390-7 gnd Wirtschaftliches Verhalten (DE-588)4197971-0 gnd Marktpsychologie (DE-588)4140888-3 gnd |
| subject_GND | (DE-588)7613972-4 (DE-588)4073788-3 (DE-588)4214003-1 (DE-588)4135561-1 (DE-588)4268390-7 (DE-588)4197971-0 (DE-588)4140888-3 |
| title | The Adaptive Markets Hypothesis An Evolutionary Approach to Understanding Financial System Dynamics |
| title_auth | The Adaptive Markets Hypothesis An Evolutionary Approach to Understanding Financial System Dynamics |
| title_exact_search | The Adaptive Markets Hypothesis An Evolutionary Approach to Understanding Financial System Dynamics |
| title_full | The Adaptive Markets Hypothesis An Evolutionary Approach to Understanding Financial System Dynamics |
| title_fullStr | The Adaptive Markets Hypothesis An Evolutionary Approach to Understanding Financial System Dynamics |
| title_full_unstemmed | The Adaptive Markets Hypothesis An Evolutionary Approach to Understanding Financial System Dynamics |
| title_short | The Adaptive Markets Hypothesis |
| title_sort | the adaptive markets hypothesis an evolutionary approach to understanding financial system dynamics |
| title_sub | An Evolutionary Approach to Understanding Financial System Dynamics |
| topic | Neuroökonomik (DE-588)7613972-4 gnd Kreditmarkt (DE-588)4073788-3 gnd Anlageverhalten (DE-588)4214003-1 gnd Marktverhalten (DE-588)4135561-1 gnd Volatilität (DE-588)4268390-7 gnd Wirtschaftliches Verhalten (DE-588)4197971-0 gnd Marktpsychologie (DE-588)4140888-3 gnd |
| topic_facet | Neuroökonomik Kreditmarkt Anlageverhalten Marktverhalten Volatilität Wirtschaftliches Verhalten Marktpsychologie |
| work_keys_str_mv | AT loandreww theadaptivemarketshypothesisanevolutionaryapproachtounderstandingfinancialsystemdynamics AT zhangruixun theadaptivemarketshypothesisanevolutionaryapproachtounderstandingfinancialsystemdynamics |