Generalized Exponentiated Gradient Algorithms and Their Application to On-Line Portfolio Selection

Stochastic gradient descent (SGD) and exponentiated gradient (EG) update methods are widely used in signal processing and machine learning. This study introduces a novel family of generalized Exponentiated Gradient updates (EGAB) derived from the alpha-beta (AB) divergence regularization. The EGAB f...

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Vydané v:IEEE access Ročník 12; s. 197000 - 197020
Hlavní autori: Cichocki, Andrzej, Cruces, Sergio, Sarmiento, Auxiliadora, Tanaka, Toshihisa
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Additives
Algorithms
Alpha-beta divergences
Cost function
Costs
Data processing
exponentiated gradient algorithms
Flexibility
Machine learning
Machine learning algorithms
on-line portfolio selection
Performance measurement
Portfolios
Probability distribution
Regularization
Signal processing algorithms
Vectors
ISSN:2169-3536, 2169-3536
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Abstract Stochastic gradient descent (SGD) and exponentiated gradient (EG) update methods are widely used in signal processing and machine learning. This study introduces a novel family of generalized Exponentiated Gradient updates (EGAB) derived from the alpha-beta (AB) divergence regularization. The EGAB framework provides enhanced flexibility for processing data with varying distributions, thanks to the tunable hyperparameters of the AB divergence. We explore the applicability of these updates in online portfolio selection (OLPS) for financial markets with the goal of developing algorithms that achieve high risk-adjusted returns, even under relatively high transaction costs. The proposed EGAB algorithms are developed using constrained gradient optimization with regularization terms, demonstrating their versatility in OLPS by unifying the directional search of various algorithms and enabling interpolation between them. Our analysis and extensive computer simulations reveal that EGAB updates outperform existing OLPS algorithms, delivering good results on several performance metrics, such as cumulative return, average excess return, Sharpe ratio, and Calmar ratio, especially when transaction costs are significant. In conclusion, this study introduces a new family of exponentiated gradient updates and demonstrates their flexibility and effectiveness through extensive simulations across a wide range of real-world financial datasets.
AbstractList Stochastic gradient descent (SGD) and exponentiated gradient (EG) update methods are widely used in signal processing and machine learning. This study introduces a novel family of generalized Exponentiated Gradient updates (EGAB) derived from the alpha-beta (AB) divergence regularization. The EGAB framework provides enhanced flexibility for processing data with varying distributions, thanks to the tunable hyperparameters of the AB divergence. We explore the applicability of these updates in online portfolio selection (OLPS) for financial markets with the goal of developing algorithms that achieve high risk-adjusted returns, even under relatively high transaction costs. The proposed EGAB algorithms are developed using constrained gradient optimization with regularization terms, demonstrating their versatility in OLPS by unifying the directional search of various algorithms and enabling interpolation between them. Our analysis and extensive computer simulations reveal that EGAB updates outperform existing OLPS algorithms, delivering good results on several performance metrics, such as cumulative return, average excess return, Sharpe ratio, and Calmar ratio, especially when transaction costs are significant. In conclusion, this study introduces a new family of exponentiated gradient updates and demonstrates their flexibility and effectiveness through extensive simulations across a wide range of real-world financial datasets.
Author Tanaka, Toshihisa
Sarmiento, Auxiliadora
Cruces, Sergio
Cichocki, Andrzej
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Snippet Stochastic gradient descent (SGD) and exponentiated gradient (EG) update methods are widely used in signal processing and machine learning. This study...
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SubjectTerms Additives
Algorithms
Alpha-beta divergences
Cost function
Costs
Data processing
exponentiated gradient algorithms
Flexibility
Machine learning
Machine learning algorithms
on-line portfolio selection
Performance measurement
Portfolios
Probability distribution
Regularization
Signal processing algorithms
Vectors
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Title Generalized Exponentiated Gradient Algorithms and Their Application to On-Line Portfolio Selection
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