On-Line Portfolio Selection

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  • Producent: Apple
  • Oprawa: Twarda
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Opis: On-Line Portfolio Selection - Steven Chu Hong Hoi, Bin Li

With the aim to sequentially determine optimal allocations across a set of assets, Online Portfolio Selection (OLPS) has significantly reshaped the financial investment landscape. Online Portfolio Selection: Principles and Algorithms supplies a comprehensive survey of existing OLPS principles and presents a collection of innovative strategies that leverage machine learning techniques for financial investment. The book presents four new algorithms based on machine learning techniques that were designed by the authors, as well as a new back-test system they developed for evaluating trading strategy effectiveness. The book uses simulations with real market data to illustrate the trading strategies in action and to provide readers with the confidence to deploy the strategies themselves. The book is presented in five sections that: * Introduce OLPS and formulate OLPS as a sequential decision task * Present key OLPS principles, including benchmarks, follow the winner, follow the loser, pattern matching, and meta-learning * Detail four innovative OLPS algorithms based on cutting-edge machine learning techniques * Provide a toolbox for evaluating the OLPS algorithms and present empirical studies comparing the proposed algorithms with the state of the art * Investigate possible future directions Complete with a back-test system that uses historical data to evaluate the performance of trading strategies, as well as MATLAB(R) code for the back-test systems, this book is an ideal resource for graduate students in finance, computer science, and statistics. It is also suitable for researchers and engineers interested in computational investment. Readers are encouraged to visit the authors' website for updates: http://olps.stevenhoi.org. "Ever since access to financial data, storage capacity, and computing power stopped acting as barriers to entry, institutional-quality asset allocation solutions have become widely available to individual investors and financial advisors. Coupled with easy access to inexpensive building blocks like Exchange-Traded Funds, this dynamic has brought the spectre of digital disruption to the asset management industry. In Online Portfolio Selection, Li and Hoi do an excellent job explaining what's actually under the hood of the "robo-advisor" applications. Unlike many books on related financial technology subjects, they don't leave the reader with only high-level rhetoric on machine learning and financial technology, but instead roll up their sleeves and delve into the nuts and bolts of the various algorithms that power this irreversible trend. A must-read." -Guy Weyns, PhD., Partner, NGEN Capital, London "This is an excellent book showing a comprehensive menu of state-of-the-art online machine-learning algorithms in online portfolio selection and trading. It explains clearly how different algorithms can perform based on data-driven patterns that are exploited using intensive computational methods. It is a must-read for serious quantitative traders." Lim Kian Guan, PhD., OUB Chair Professor of Quantitative Finance, Singapore Management UniversityI: INTRODUCTION Introduction Background What Is Online Portfolio Selection? Methodology Book Overview Problem Formulation Problem Settings Transaction Costs and Margin Buying Models Evaluation Summary II: Principles Benchmarks Buy-and-Hold Strategy Best Stock Strategy Constant Rebalanced Portfolios Follow the Winner Universal Portfolios Exponential Gradient Follow the Leader Follow the Regularized Leader Summary Follow the Loser Mean Reversion Anticorrelation Summary Pattern Matching Sample Selection Techniques Portfolio Optimization Techniques Combinations Summary Meta-Learning Aggregating Algorithms Fast Universalization Online Gradient and Newton Updates Follow the Leading History Summary III: Algorithms Correlation-Driven Nonparametric Learning Preliminaries Formulations Algorithms Analysis Summary Passive-Aggressive Mean Reversion Preliminaries Formulations Algorithms Analysis Summary Confidence-Weighted Mean Reversion Preliminaries Formulations Algorithms Analysis Summary Online Moving Average Reversion Preliminaries Formulations Algorithms Analysis Summary IV: Empirical Studies Implementations The OLPS Platform Data Setups Performance Metrics Summary Empirical Results Experiment 1: Evaluation of Cumulative Wealth Experiment 2: Evaluation of Risk and Risk-Adjusted Return Experiment 3: Evaluation of Parameter Sensitivity Experiment 4: Evaluation of Practical Issues Experiment 5: Evaluation of Computational Time Experiment 6: Descriptive Analysis of Assets and Portfolios Summary Threats to Validity On Model Assumptions On Mean Reversion Assumptions On Theoretical Analysis On Back-Tests Summary V: Conclusion Conclusions Future Directions Appendix A: OLPS: A Toolbox for Online Portfolio Selection Introduction Framework and Interfaces Strategies Summary Appendix B: Proofs and Derivations Proof of CORN Derivations of PAMR Derivations of CWMR Derivation of OLMAR Appendix C: Supplementary Data and Portfolio Statistics Bibliography Index


Szczegóły: On-Line Portfolio Selection - Steven Chu Hong Hoi, Bin Li

Tytuł: On-Line Portfolio Selection
Autor: Steven Chu Hong Hoi, Bin Li
Producent: Apple
ISBN: 9781482249637
Rok produkcji: 2015
Ilość stron: 232
Oprawa: Twarda
Waga: 0.49 kg


Recenzje: On-Line Portfolio Selection - Steven Chu Hong Hoi, Bin Li

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Przypomnij hasło
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On-Line Portfolio Selection

,

  • Producent: Apple
  • Oprawa: Twarda

With the aim to sequentially determine optimal allocations across a set of assets, Online Portfolio Selection (OLPS) has significantly reshaped the financial investment landscape. Online Portfolio Selection: Principles and Algorithms supplies a comprehensive survey of existing OLPS principles and presents a collection of innovative strategies that leverage machine learning techniques for financial investment. The book presents four new algorithms based on machine learning techniques that were designed by the authors, as well as a new back-test system they developed for evaluating trading strategy effectiveness. The book uses simulations with real market data to illustrate the trading strategies in action and to provide readers with the confidence to deploy the strategies themselves. The book is presented in five sections that: * Introduce OLPS and formulate OLPS as a sequential decision task * Present key OLPS principles, including benchmarks, follow the winner, follow the loser, pattern matching, and meta-learning * Detail four innovative OLPS algorithms based on cutting-edge machine learning techniques * Provide a toolbox for evaluating the OLPS algorithms and present empirical studies comparing the proposed algorithms with the state of the art * Investigate possible future directions Complete with a back-test system that uses historical data to evaluate the performance of trading strategies, as well as MATLAB(R) code for the back-test systems, this book is an ideal resource for graduate students in finance, computer science, and statistics. It is also suitable for researchers and engineers interested in computational investment. Readers are encouraged to visit the authors' website for updates: http://olps.stevenhoi.org. "Ever since access to financial data, storage capacity, and computing power stopped acting as barriers to entry, institutional-quality asset allocation solutions have become widely available to individual investors and financial advisors. Coupled with easy access to inexpensive building blocks like Exchange-Traded Funds, this dynamic has brought the spectre of digital disruption to the asset management industry. In Online Portfolio Selection, Li and Hoi do an excellent job explaining what's actually under the hood of the "robo-advisor" applications. Unlike many books on related financial technology subjects, they don't leave the reader with only high-level rhetoric on machine learning and financial technology, but instead roll up their sleeves and delve into the nuts and bolts of the various algorithms that power this irreversible trend. A must-read." -Guy Weyns, PhD., Partner, NGEN Capital, London "This is an excellent book showing a comprehensive menu of state-of-the-art online machine-learning algorithms in online portfolio selection and trading. It explains clearly how different algorithms can perform based on data-driven patterns that are exploited using intensive computational methods. It is a must-read for serious quantitative traders." Lim Kian Guan, PhD., OUB Chair Professor of Quantitative Finance, Singapore Management UniversityI: INTRODUCTION Introduction Background What Is Online Portfolio Selection? Methodology Book Overview Problem Formulation Problem Settings Transaction Costs and Margin Buying Models Evaluation Summary II: Principles Benchmarks Buy-and-Hold Strategy Best Stock Strategy Constant Rebalanced Portfolios Follow the Winner Universal Portfolios Exponential Gradient Follow the Leader Follow the Regularized Leader Summary Follow the Loser Mean Reversion Anticorrelation Summary Pattern Matching Sample Selection Techniques Portfolio Optimization Techniques Combinations Summary Meta-Learning Aggregating Algorithms Fast Universalization Online Gradient and Newton Updates Follow the Leading History Summary III: Algorithms Correlation-Driven Nonparametric Learning Preliminaries Formulations Algorithms Analysis Summary Passive-Aggressive Mean Reversion Preliminaries Formulations Algorithms Analysis Summary Confidence-Weighted Mean Reversion Preliminaries Formulations Algorithms Analysis Summary Online Moving Average Reversion Preliminaries Formulations Algorithms Analysis Summary IV: Empirical Studies Implementations The OLPS Platform Data Setups Performance Metrics Summary Empirical Results Experiment 1: Evaluation of Cumulative Wealth Experiment 2: Evaluation of Risk and Risk-Adjusted Return Experiment 3: Evaluation of Parameter Sensitivity Experiment 4: Evaluation of Practical Issues Experiment 5: Evaluation of Computational Time Experiment 6: Descriptive Analysis of Assets and Portfolios Summary Threats to Validity On Model Assumptions On Mean Reversion Assumptions On Theoretical Analysis On Back-Tests Summary V: Conclusion Conclusions Future Directions Appendix A: OLPS: A Toolbox for Online Portfolio Selection Introduction Framework and Interfaces Strategies Summary Appendix B: Proofs and Derivations Proof of CORN Derivations of PAMR Derivations of CWMR Derivation of OLMAR Appendix C: Supplementary Data and Portfolio Statistics Bibliography Index

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Cena 498,00 PLN
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Wysyłka: Niedostępna
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Szczegóły: On-Line Portfolio Selection - Steven Chu Hong Hoi, Bin Li

Tytuł: On-Line Portfolio Selection
Autor: Steven Chu Hong Hoi, Bin Li
Producent: Apple
ISBN: 9781482249637
Rok produkcji: 2015
Ilość stron: 232
Oprawa: Twarda
Waga: 0.49 kg


Recenzje: On-Line Portfolio Selection - Steven Chu Hong Hoi, Bin Li

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