Portfolio Management with Heuristic OptimizationSpringer Science & Business Media, 2005年12月12日 - 223 頁 Portfolio Management with Heuristic Optimization consist of two parts. The first part (Foundations) deals with the foundations of portfolio optimization, its assumptions, approaches and the limitations when "traditional" optimization techniques are to be applied. In addition, the basic concepts of several heuristic optimization techniques are presented along with examples of how to implement them for financial optimization problems. The second part (Applications and Contributions) consists of five chapters, covering different problems in financial optimization: the effects of (linear, proportional and combined) transaction costs together with integer constraints and limitations on the initital endowment to be invested; the diversification in small portfolios; the effect of cardinality constraints on the Markowitz efficient line; the effects (and hidden risks) of Value-at-Risk when used the relevant risk constraint; the problem factor selection for the Arbitrage Pricing Theory. |
內容
Portfolio Management | 1 |
112 Modern Portfolio Theory MPT | 6 |
113 Risk Reconsidered | 17 |
12 Implications of the MPT and Beyond | 24 |
122 Capital Asset Pricing Model | 26 |
123 Alternative Versions of the CAPM | 29 |
124 The Arbitrage Pricing Theory | 32 |
13 Limitations of the MPT | 33 |
432 Computational Study for the Modified Update Rule | 114 |
433 Financial Results | 116 |
44 Conclusion | 121 |
Cardinality Constraints for Markowitz Efficient Lines | 122 |
512 The Problem of Optimization | 124 |
52 A Hybrid Local Search Algorithm | 127 |
522 Variants | 131 |
523 Considerations behind the Algorithm | 132 |
14 Summary | 37 |
Heuristic Optimization | 38 |
212 Techniques for Hard Optimization Problems | 40 |
22 Heuristic Optimization Techniques | 51 |
222 Characteristics of Heuristic Optimization Methods | 52 |
23 Some Selected Methods | 55 |
232 Evolution Based and Genetic Methods | 57 |
233 Ant Systems and Ant Colony Optimization | 59 |
234 Memetic Algorithms | 61 |
24 Heuristic Optimization at Work | 63 |
242 Tuning the Heuristics Parameters | 67 |
243 Results | 72 |
25 Conclusion | 75 |
Transaction Costs and Integer Constraints | 77 |
32 The Problem | 78 |
322 The Heuristic | 80 |
323 The Data | 82 |
33 Results from the Empirical Study | 84 |
332 Simple Transaction Costs | 86 |
333 Compound Transaction Costs | 92 |
34 Consequences for Portfolio Management | 95 |
35 Conclusions | 99 |
Diversification in Small Portfolios | 100 |
42 The Model | 101 |
422 Ant Systems | 103 |
423 The Algorithm | 104 |
43 The Empirical Study | 111 |
53 The Computational Study | 134 |
532 Evaluation of the Suggested Algorithm | 135 |
533 Contribution of Evolutionary Strategies | 139 |
54 Financial Implications | 141 |
55 Conclusion | 143 |
The Hidden Risk of Value at Risk | 144 |
62 Risk Constraints and Distribution Assumptions | 147 |
622 The Bond Market Investor | 150 |
63 A Modified Version of Memetic Algorithms | 152 |
632 The Elitist Principle | 154 |
633 Computational Study | 158 |
64 Results for Stock Portfolios | 162 |
The Resulting Stock Portfolios | 165 |
65 Results for Bond Portfolios | 171 |
652 The Hidden Risks in Optimized Bond Portfolios | 174 |
66 Conclusion | 179 |
Finding Relevant Risk Factors in Asset Pricing | 180 |
72 The Selection of Suitable Factors | 183 |
722 Memetic Algorithms | 185 |
73 Computational Study | 186 |
732 Main Results for the Selection of Factors | 187 |
733 Alternative Models | 194 |
Concluding Remarks | 197 |
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alternative applied approach Arbitrage Pricing Theory Asset Pricing asset weights average candidate solutions CAPM cardinality constraint chapter combination computational study correlation covariances DAX data set decision variables deviations different assets diversification Economics effect elitist empirical distribution empirical study estimated expected return factors Financial fixed costs FTSE data set global optimum Hence heuristic optimization higher included assets initial endowment integer constraint investment investor Journal of Finance Maringer Memetic Algorithms methods Modern Portfolio Theory non-negativity normal distribution number of assets number of different number of iterations objective function optimal portfolio optimization problem optimization process parameters pheromone population Portfolio Management portfolio optimization portfolio selection portfolio structure proportional costs randomly reliable reported solutions respective risk constraint risk measures risk premium safe asset selection problem Sharpe Ratio Simulated Annealing small number stocks tion transaction costs Value at Risk values VaRemp variance VaRnorm volatility бр
熱門章節
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