Enhancing Portfolio Performance through Financial Time-Series Decomposition-Based Variational Encoder-Decoder Data Augmentation

The objective of portfolio diversification is to reduce risk and potentially enhance returns by spreading investments across different asset classes. Existing portfolio diversification models have traditionally been trained on historical financial time series data. However, several issues arise with...

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Vydáno v:Symmetry (Basel) Ročník 16; číslo 3; s. 283
Hlavní autoři: Kalina, Bayartsetseg, Lee, Ju-Hong
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.03.2024
Témata:
Algorithms
Analysis
Data augmentation
Data mining
Decision making
Decomposition
Diversification
Encoders-Decoders
Evaluation
Financial markets
Investment analysis
Investment policy
Investments
Machine learning
Optimization techniques
Portfolio management
Portfolio performance
Risk management
Securities markets
Time series
Trends
Uncertainty
Variables
ISSN:2073-8994, 2073-8994
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Shrnutí:The objective of portfolio diversification is to reduce risk and potentially enhance returns by spreading investments across different asset classes. Existing portfolio diversification models have traditionally been trained on historical financial time series data. However, several issues arise with historical financial time series data, making it challenging to train models effectively to achieve the portfolio diversification objective: an insufficient amount of training data and the uncertainty deficiency problem, wherein the uncertainty that existed in the past is not visible in the present. Insufficient datasets, characterized by small data size, result in information asymmetry and compromise portfolio performance. This limitation underscores the importance of adopting a pattern-centric data augmentation approach, capable of unveiling hidden patterns and structures within the financial time series data. To address these challenges, this paper introduces the financial time series decomposition-based variational encoder-decoder (FED) method to augment financial time series data, overcoming the limitations of insufficient training data and providing a more realistic and dynamic simulation of the financial market environment. By decomposing the data into distinct components, such as trend, dispersion, and residual, FED leverages pattern-centric data augmentation within the financial time series data. In the environment generated using the FED method, this paper proposes a two-class portfolio diversification, called FED2Port. It integrates stochastic elements into the reward function, enabling a reinforcement learning algorithm to learn from a comprehensive spectrum of financial market uncertainties. The experimental results demonstrate that the proposed model significantly enhances portfolio performance.
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ISSN:2073-8994
2073-8994
DOI:10.3390/sym16030283