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Kei-Keung Hung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (leaves 127-128). / Abstract also in Chinese. / Abstract --- p.i / Acknowledgments --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Static Portfolio Techniques --- p.1 / Chapter 1.2 --- Neural Network Approach --- p.2 / Chapter 1.3 --- Contributions of this Thesis --- p.3 / Chapter 1.4 --- Application of this Research --- p.4 / Chapter 1.5 --- Organization of this Thesis --- p.4 / Chapter 2 --- Literature Review --- p.6 / Chapter 2.1 --- Standard Markowian Portfolio Optimization (SMPO) and Sharpe Ratio --- p.6 / Chapter 2.2 --- Downside Risk --- p.9 / Chapter 2.3 --- Augmented Lagrangian Method --- p.10 / Chapter 2.4 --- Adaptive Supervised Learning Decision (ASLD) System --- p.13 / Chapter I --- Static Portfolio Optimization Techniques --- p.19 / Chapter 3 --- Modified Portfolio Sharpe Ratio Maximization (MPSRM) --- p.20 / Chapter 3.1 --- Experiment Setting --- p.21 / Chapter 3.2 --- Downside Risk and Upside Volatility --- p.22 / Chapter 3.3 --- Investment Diversification --- p.24 / Chapter 3.4 --- Analysis of the Parameters H and B of MPSRM --- p.27 / Chapter 3.5 --- Risk Minimization with Control of Expected Return --- p.30 / Chapter 3.6 --- Return Maximization with Control of Expected Downside Risk --- p.32 / Chapter 4 --- Variations of Modified Portfolio Sharpe Ratio Maximization --- p.34 / Chapter 4.1 --- Soft-max Version of Modified Portfolio Sharpe Ratio Maximization (SMP- SRM) --- p.35 / Chapter 4.1.1 --- Applying Soft-max Technique to Modified Portfolio Sharpe Ratio Maximization (SMPSRM) --- p.35 / Chapter 4.1.2 --- Risk Minimization with Control of Expected Return --- p.37 / Chapter 4.1.3 --- Return Maximization with Control of Expected Downside Risk --- p.38 / Chapter 4.2 --- Soft-max Version of MPSRM with Entropy-like Regularization Term (SMPSRM-E) --- p.39 / Chapter 4.2.1 --- Using Entropy-like Regularization term in Soft-max version of Modified Portfolio Sharpe Ratio Maximization (SMPSRM-E) --- p.39 / Chapter 4.2.2 --- Risk Minimization with Control of Expected Return --- p.41 / Chapter 4.2.3 --- Return Maximization with Control of Expected Downside Risk --- p.43 / Chapter 4.3 --- Analysis of Parameters in SMPSRM and SMPSRM-E --- p.44 / Chapter II --- Neural Network Approach --- p.48 / Chapter 5 --- Investment on a Foreign Exchange Market using ASLD system --- p.49 / Chapter 5.1 --- Investment on A Foreign Exchange Portfolio --- p.49 / Chapter 5.2 --- Two Important Issues Revisited --- p.51 / Chapter 6 --- Investment on Stock market using ASLD System --- p.54 / Chapter 6.1 --- Investment on Hong Kong Hang Seng Index --- p.54 / Chapter 6.1.1 --- Performance of the Original ASLD System --- p.54 / Chapter 6.1.2 --- Performances After Adding Several Heuristic Strategies --- p.55 / Chapter 6.2 --- Investment on Six Different Stock Indexes --- p.61 / Chapter 6.2.1 --- Structure and Operation of the New System --- p.62 / Chapter 6.2.2 --- Experimental Results --- p.63 / Chapter III --- Combination of Static Portfolio Optimization techniques with Neural Network Approach --- p.67 / Chapter 7 --- Combining the ASLD system with Different Portfolio Optimizations --- p.68 / Chapter 7.1 --- Structure and Operation of the New System --- p.69 / Chapter 7.2 --- Combined with the Standard Markowian Portfolio Optimization (SMPO) --- p.70 / Chapter 7.3 --- Combined with the Modified Portfolio Sharpe Ratio Maximization (MP- SRM) --- p.72 / Chapter 7.4 --- Combined with the MPSRM ´ؤ Risk Minimization with Control of Ex- pected Return --- p.74 / Chapter 7.5 --- Combined with the MPSRM ´ؤ Return Maximization with Control of Expected Downside Risk --- p.76 / Chapter 7.6 --- Combined with the Soft-max Version of MPSRM (SMPSRM) --- p.77 / Chapter 7.7 --- Combined with the SMPSRM - Risk Minimization with Control of Ex- pected Return --- p.79 / Chapter 7.8 --- Combined with the SMPSRM ´ؤ Return Maximization with Control of Expected Downside Risk --- p.80 / Chapter 7.9 --- Combined with the Soft-max Version of MPSRM with Entropy-like Reg- ularization Term (SMPSRM-E) --- p.82 / Chapter 7.10 --- Combined with the SMPSRM-E ´ؤ Risk Minimization with Control of Expected Return --- p.84 / Chapter 7.11 --- Combined with the SMPSRM-E ´ؤ Return Maximization with Control of Expected Downside Risk --- p.86 / Chapter IV --- Software Developed --- p.93 / Chapter 8 --- Windows Application Developed --- p.94 / Chapter 8.1 --- Decision on Platform and Programming Language --- p.94 / Chapter 8.2 --- System Design --- p.96 / Chapter 8.3 --- Operation of our program --- p.97 / Chapter 9 --- Conclusion --- p.103 / Chapter A --- Algorithm for Portfolio Sharpe Ratio Maximization (PSRM) --- p.105 / Chapter B --- Algorithm for Improved Portfolio Sharpe Ratio Maximization (ISRM) --- p.107 / Chapter C --- Proof of Regularization Term Y --- p.109 / Chapter D --- Algorithm for Modified Portfolio Sharpe Ratio Maximization (MP- SRM) --- p.111 / Chapter E --- Algorithm for MPSRM with Control of Expected Return --- p.113 / Chapter F --- Algorithm for MPSRM with Control of Expected Downside Risk --- p.115 / Chapter G --- Algorithm for SMPSRM with Control of Expected Return --- p.117 / Chapter H --- Algorithm for SMPSRM with Control of Expected Downside Risk --- p.119 / Chapter I --- Proof of Entropy-like Regularization Term --- p.121 / Chapter J --- Algorithm for SMPSRM-E with Control of Expected Return --- p.123 / Chapter K --- Algorithm for SMPSRM-E with Control of Expected Downside Riskl25 Bibliography --- p.127

Tsue, Wing Yeung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (leaves 94-100). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.vii / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Reinforcement Learning (RL) --- p.7 / Chapter 2.1 --- Objective of RL --- p.8 / Chapter 2.2 --- Algorithms in RL --- p.9 / Chapter 2.2.1 --- Dynamic Programming --- p.9 / Chapter 2.2.2 --- Monte Carlo Methods --- p.11 / Chapter 2.2.3 --- Temporal-Difference Learning and Q-Learning --- p.12 / Chapter 2.3 --- Example: Maze --- p.13 / Chapter 2.4 --- Artificial Neural Network to Approximate Q-Function --- p.14 / Chapter 2.5 --- Literatures on Trading a Single Asset by RL --- p.16 / Chapter 2.6 --- Literatures on Portfolio Management by RL --- p.19 / Chapter 2.7 --- Summary --- p.20 / Chapter 3 --- Portfolio Management (PM) --- p.21 / Chapter 3.1 --- Buy-and-Hold Strategy --- p.22 / Chapter 3.2 --- Mean-Variance Analysis --- p.23 / Chapter 3.3 --- Constant Rebalancing Algorithm --- p.24 / Chapter 3.4 --- Universal Portfolio Algorithm --- p.25 / Chapter 3.5 --- ANTI COR Algorithm --- p.26 / Chapter 4 --- PM on RL Traders --- p.30 / Chapter 4.1 --- Implementation of Single-Asset RL Traders --- p.32 / Chapter 4.1.1 --- State Formation --- p.32 / Chapter 4.1.2 --- Actions and Immediate Reward --- p.38 / Chapter 4.1.3 --- Update --- p.38 / Chapter 4.2 --- Experiments --- p.41 / Chapter 4.3 --- Discussion --- p.47 / Chapter 5 --- RL-Bascd Portfolio Management (RLPM) --- p.49 / Chapter 5.1 --- Overview --- p.52 / Chapter 5.2 --- Two-Asset RL System --- p.54 / Chapter 5.2.1 --- State Formation --- p.55 / Chapter 5.2.2 --- Action --- p.61 / Chapter 5.2.3 --- Update Rule --- p.64 / Chapter 5.3 --- Portfolio Construction --- p.67 / Chapter 5.4 --- Choice of Window Size w --- p.70 / Chapter 5.5 --- Empirical Results --- p.73 / Chapter 5.5.1 --- "Effect of Window Size w on 1 Layer of RLPMw, and 2 Layers of RLPMW" --- p.76 / Chapter 5.5.2 --- Comparing RLPM to Other Strategies --- p.80 / Chapter 5.5.3 --- Effect of Transaction Cost A on RLPMw --- p.83 / Chapter 6 --- Conclusion --- p.89 / Bibliography --- p.94