The Resource Online Algorithms for the Portfolio Selection Problem, Robert Dochow ; With a foreword by Prof. Dr.-Ing. Günter Schmidt

Online Algorithms for the Portfolio Selection Problem, Robert Dochow ; With a foreword by Prof. Dr.-Ing. Günter Schmidt

Label
Online Algorithms for the Portfolio Selection Problem
Title
Online Algorithms for the Portfolio Selection Problem
Statement of responsibility
Robert Dochow ; With a foreword by Prof. Dr.-Ing. Günter Schmidt
Creator
Author
Subject
Language
eng
Summary
"Robert Dochow mathematically derives a simplified classification structure of selected types of the portfolio selection problem. He proposes two new competitive online algorithms with risk management, which he evaluates analytically. The author empirically evaluates online algorithms by a comprehensive statistical analysis. Concrete results are that follow-the-loser algorithms show the most promising performance when the objective is the maximization of return on investment and risk-adjusted performance. In addition, when the objective is the minimization of risk, the two new algorithms with risk management show excellent performance. A prototype of a software tool for automated evaluation of algorithms for portfolio selection is given."--Publisher's description
Member of
Cataloging source
CNCGM
http://library.link/vocab/creatorName
Dochow, Robert
Dewey number
332.6/015181
Dissertation year
2015
Granting institution
Saarland University, Saarbrücken
Illustrations
illustrations
Index
no index present
Intended audience
Specialized
LC call number
HG4529.5
LC item number
.D63 2016
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
  • theses
Series statement
Research
http://library.link/vocab/subjectName
  • Portfolio management
  • Algorithms
  • Portfolio management
  • Algorithms
Target audience
specialized
Label
Online Algorithms for the Portfolio Selection Problem, Robert Dochow ; With a foreword by Prof. Dr.-Ing. Günter Schmidt
Link
https://ezproxy.lib.ou.edu/login?url=http://link.springer.com/10.1007/978-3-658-13528-7
Instantiates
Publication
Copyright
Bibliography note
Includes bibliographical references (pages 175-185)
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • 1.
  • Introduction
  • 1.1.
  • Preliminaries
  • 1.2.
  • Motivation and Research Questions
  • 1.3.
  • Structure of the Thesis
  • 2.1.3.
  • Asset Prices, Conversion Rates and Return Factors
  • 2.2.
  • Selected Portfolio Selection Problems
  • 2.2.1.
  • General Portfolio Selection Problem
  • 2.2.2.
  • Constant Rebalancing Problem
  • 2.2.3.
  • Semi-Portfolio Selection Problem
  • 2.
  • 2.2.4.
  • Semi-Constant Rebalancing Problem
  • 2.2.5.
  • Buy-and-Hold Problem
  • 2.2.6.
  • Conversion Problem
  • 2.3.
  • Standard Working Models
  • 2.3.1.
  • Portfolio Selection Problem
  • Portfolio Selection Problems
  • 2.3.2.
  • Conversion Problem
  • 2.4.
  • Conclusions
  • 2.1.
  • Preliminaries
  • 2.1.1.
  • Online and Offline Algorithms
  • 2.1.2.
  • Mathematical Programming
  • 3.1.3.
  • Time Complexity
  • 3.2.
  • Selected Performance Measures
  • 3.2.1.
  • Measures of Return on Investment
  • 3.2.2.
  • Measures of Risk
  • 3.2.3.
  • Measures of Risk-adjusted Performance
  • 3.
  • 3.3.
  • Selected Benchmarks
  • 3.3.1.
  • Offline Benchmarks: Buy-and-Hold
  • 3.3.2.
  • Offline Benchmarks: Constant Rebalancing
  • 3.3.3.
  • Offline Benchmarks
  • 3.4.
  • Statistical Analysis
  • Performance Evaluation
  • 3.4.1.
  • Selected Statistical Measures
  • 3.4.2.
  • Hypothesis Testing
  • 3.4.3.
  • Selected Sampling Techniques
  • 3.5.
  • Competitive Analysis
  • 3.5.1.
  • Competitive Ratio
  • 3.1.
  • 3.5.2.
  • Performance Ratio
  • 3.5.3.
  • Comparative Ratio
  • 3.5.4.
  • Average-Case Competitive Ratio
  • 3.5.5.
  • Concept of Universality
  • 3.5.6.
  • Competitive Ratio as Performance Measure
  • Preliminaries
  • 3.6.
  • Conclusions
  • 3.1.1.
  • Problem Statement
  • 3.1.2.
  • Efficient Markets Hypothesis
  • 4.1.3.
  • Information and Algorithms
  • 4.2.
  • Follow-the-Winner Algorithms
  • 4.2.1.
  • Successive Constant Rebalanced Algorithm
  • 4.2.2.
  • Universal Portfolio Algorithm
  • 4.2.3.
  • Exponential Gradient Algorithm
  • 4.
  • 4.2.4.
  • Online Newton Step Algorithm
  • 4.3.
  • Follow-the-Loser Algorithms
  • 4.3.1.
  • Anti Correlation Algorithm
  • 4.3.2.
  • Passive Aggressive Mean Reversion Algorithm
  • 4.3.3.
  • Confidence Weighted Mean Reversion Algorithm
  • Selected Algorithms from the Literature
  • 4.3.4.
  • Online Moving Average Mean Reversion Algorithm
  • 4.3.5.
  • Robust Median Reversion Algorithm
  • 4.4.
  • Conclusions
  • 4.1.
  • Preliminaries
  • 4.1.1.
  • Virtual Market
  • 4.1.2.
  • Projection onto a Simplex
  • 5.1.3.
  • Risk-adjusted Portfolio Selection Algorithm
  • 5.1.4.
  • Combined Risk-adjusted Portfolio Selection Algorithm
  • 5.2.
  • Comparison of Competitiveness
  • 5.3.
  • Numerical Results
  • 5.4.
  • Conclusions
  • 5.
  • Proposed Algorithms with Risk Management
  • 5.1.
  • Preliminaries
  • 5.1.1.
  • Worst-Case Logarithmic Wealth Ratio
  • 5.1.2.
  • Universal Portfolio Algorithm
  • 6.1.3.
  • Dataset and Description
  • 6.2.
  • Test Design
  • 6.3.
  • Numerical Results: Expected Performance
  • 6.4.
  • Numerical Results: Beating the Benchmark
  • 6.5.
  • Conclusions
  • 6.
  • Empirical Testing of Algorithms
  • 6.1.
  • Preliminaries
  • 6.1.1.
  • Algorithms and Parameters
  • 6.1.2.
  • Related Work
  • 7.2.2.
  • Running Algorithms
  • 7.2.3.
  • Measuring Performance
  • 7.3.
  • Conclusions
  • 7.
  • A Software Tool for Testing Algorithms
  • 7.1.
  • Preliminaries
  • 7.2.
  • Primary Functions
  • 7.2.1.
  • Executing Sampling
  • 8.4.
  • Concluding Remarks
  • A.
  • Proofs
  • A.1.
  • Bounds on the Number of Allocations
  • A.2.
  • Asymptotic Behavior of the Number of Allocations
  • B.
  • Numerical Results
  • 8.
  • B.1.
  • Numerical Results: Expected Performance
  • B.2.
  • Numerical Results: Beating the Benchmark
  • Conclusions and Future Work
  • 8.1.
  • Portfolio Selection Problems
  • 8.2.
  • Online Algorithms with Risk Management
  • 8.3.
  • Empirical Testing
Dimensions
unknown
Extent
1 online resource (xxvi, 185 pages)
Form of item
online
Isbn
9783658135287
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Note
SpringerLink
Other physical details
illustrations (black and white).
Specific material designation
remote
Stock number
927141
System control number
  • (OCoLC)987665165
  • (OCoLC)ocn987665165
Label
Online Algorithms for the Portfolio Selection Problem, Robert Dochow ; With a foreword by Prof. Dr.-Ing. Günter Schmidt
Link
https://ezproxy.lib.ou.edu/login?url=http://link.springer.com/10.1007/978-3-658-13528-7
Publication
Copyright
Bibliography note
Includes bibliographical references (pages 175-185)
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • 1.
  • Introduction
  • 1.1.
  • Preliminaries
  • 1.2.
  • Motivation and Research Questions
  • 1.3.
  • Structure of the Thesis
  • 2.1.3.
  • Asset Prices, Conversion Rates and Return Factors
  • 2.2.
  • Selected Portfolio Selection Problems
  • 2.2.1.
  • General Portfolio Selection Problem
  • 2.2.2.
  • Constant Rebalancing Problem
  • 2.2.3.
  • Semi-Portfolio Selection Problem
  • 2.
  • 2.2.4.
  • Semi-Constant Rebalancing Problem
  • 2.2.5.
  • Buy-and-Hold Problem
  • 2.2.6.
  • Conversion Problem
  • 2.3.
  • Standard Working Models
  • 2.3.1.
  • Portfolio Selection Problem
  • Portfolio Selection Problems
  • 2.3.2.
  • Conversion Problem
  • 2.4.
  • Conclusions
  • 2.1.
  • Preliminaries
  • 2.1.1.
  • Online and Offline Algorithms
  • 2.1.2.
  • Mathematical Programming
  • 3.1.3.
  • Time Complexity
  • 3.2.
  • Selected Performance Measures
  • 3.2.1.
  • Measures of Return on Investment
  • 3.2.2.
  • Measures of Risk
  • 3.2.3.
  • Measures of Risk-adjusted Performance
  • 3.
  • 3.3.
  • Selected Benchmarks
  • 3.3.1.
  • Offline Benchmarks: Buy-and-Hold
  • 3.3.2.
  • Offline Benchmarks: Constant Rebalancing
  • 3.3.3.
  • Offline Benchmarks
  • 3.4.
  • Statistical Analysis
  • Performance Evaluation
  • 3.4.1.
  • Selected Statistical Measures
  • 3.4.2.
  • Hypothesis Testing
  • 3.4.3.
  • Selected Sampling Techniques
  • 3.5.
  • Competitive Analysis
  • 3.5.1.
  • Competitive Ratio
  • 3.1.
  • 3.5.2.
  • Performance Ratio
  • 3.5.3.
  • Comparative Ratio
  • 3.5.4.
  • Average-Case Competitive Ratio
  • 3.5.5.
  • Concept of Universality
  • 3.5.6.
  • Competitive Ratio as Performance Measure
  • Preliminaries
  • 3.6.
  • Conclusions
  • 3.1.1.
  • Problem Statement
  • 3.1.2.
  • Efficient Markets Hypothesis
  • 4.1.3.
  • Information and Algorithms
  • 4.2.
  • Follow-the-Winner Algorithms
  • 4.2.1.
  • Successive Constant Rebalanced Algorithm
  • 4.2.2.
  • Universal Portfolio Algorithm
  • 4.2.3.
  • Exponential Gradient Algorithm
  • 4.
  • 4.2.4.
  • Online Newton Step Algorithm
  • 4.3.
  • Follow-the-Loser Algorithms
  • 4.3.1.
  • Anti Correlation Algorithm
  • 4.3.2.
  • Passive Aggressive Mean Reversion Algorithm
  • 4.3.3.
  • Confidence Weighted Mean Reversion Algorithm
  • Selected Algorithms from the Literature
  • 4.3.4.
  • Online Moving Average Mean Reversion Algorithm
  • 4.3.5.
  • Robust Median Reversion Algorithm
  • 4.4.
  • Conclusions
  • 4.1.
  • Preliminaries
  • 4.1.1.
  • Virtual Market
  • 4.1.2.
  • Projection onto a Simplex
  • 5.1.3.
  • Risk-adjusted Portfolio Selection Algorithm
  • 5.1.4.
  • Combined Risk-adjusted Portfolio Selection Algorithm
  • 5.2.
  • Comparison of Competitiveness
  • 5.3.
  • Numerical Results
  • 5.4.
  • Conclusions
  • 5.
  • Proposed Algorithms with Risk Management
  • 5.1.
  • Preliminaries
  • 5.1.1.
  • Worst-Case Logarithmic Wealth Ratio
  • 5.1.2.
  • Universal Portfolio Algorithm
  • 6.1.3.
  • Dataset and Description
  • 6.2.
  • Test Design
  • 6.3.
  • Numerical Results: Expected Performance
  • 6.4.
  • Numerical Results: Beating the Benchmark
  • 6.5.
  • Conclusions
  • 6.
  • Empirical Testing of Algorithms
  • 6.1.
  • Preliminaries
  • 6.1.1.
  • Algorithms and Parameters
  • 6.1.2.
  • Related Work
  • 7.2.2.
  • Running Algorithms
  • 7.2.3.
  • Measuring Performance
  • 7.3.
  • Conclusions
  • 7.
  • A Software Tool for Testing Algorithms
  • 7.1.
  • Preliminaries
  • 7.2.
  • Primary Functions
  • 7.2.1.
  • Executing Sampling
  • 8.4.
  • Concluding Remarks
  • A.
  • Proofs
  • A.1.
  • Bounds on the Number of Allocations
  • A.2.
  • Asymptotic Behavior of the Number of Allocations
  • B.
  • Numerical Results
  • 8.
  • B.1.
  • Numerical Results: Expected Performance
  • B.2.
  • Numerical Results: Beating the Benchmark
  • Conclusions and Future Work
  • 8.1.
  • Portfolio Selection Problems
  • 8.2.
  • Online Algorithms with Risk Management
  • 8.3.
  • Empirical Testing
Dimensions
unknown
Extent
1 online resource (xxvi, 185 pages)
Form of item
online
Isbn
9783658135287
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Note
SpringerLink
Other physical details
illustrations (black and white).
Specific material designation
remote
Stock number
927141
System control number
  • (OCoLC)987665165
  • (OCoLC)ocn987665165

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