
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 Andrew W. Lo, Ruixun Zhang. Also Issued In Print: 2024. Includes Bibliographical References And Index. Electronic Reproduction. Ann Arbor, Mi Available Via World Wide Web.
How can the principles of evolutionary biology explain the complex, often irrational dynamics of financial markets? Andrew W. Lo and Ruixun Zhang propose the Adaptive Markets Hypothesis (AMH) as a framework that reconciles the Efficient Market Hypothesis with behavioral economics. By treating market participants as biological organisms subject to natural selection, the authors argue that financial behavior is an adaptive process rather than a static equilibrium, providing a rigorous mathematical foundation for understanding market cycles and crises.
What You Will Find
Scope Limits
Experts recognize this work as a significant contribution to the field of financial economics, bridging the gap between biology and market theory. Readers frequently note the high level of mathematical density and academic rigor, making it a challenging but rewarding text for advanced students and researchers in quantitative finance.
Page Count:
0
Publication Date:
1900-01-01
Publisher:
Oxford University Press,
ISBN-10:
019176728X
ISBN-13:
9780191767289
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