Generative Network for Probabilistic Portfolio Management
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Solution Overview
Problem
Machine learning algorithms face challenges in providing accurate predictions for future asset-price trends, especially in uncertain environments, where they struggle to model nonlinear interactions and dependencies between assets effectively.
Innovation Solution
A computer-implemented method and system that utilizes a generative adversarial network (GAN) to model future market uncertainty, allowing for diversified portfolio combinations with risk-adjusted returns by training a deep-learning neural network on real-time series data to learn nonlinear market behavior and dependencies between assets, enabling the generation of realistic future trends and optimization of portfolio diversification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional machine learning algorithms are used to predict asset-price trends, then the algorithms are simple to implement, but they fail to accurately model nonlinear interactions and dependencies between assets in uncertain environments
Solution Approach 1:
The patent introduces latent variables as intermediaries that capture unobserved market factors and nonlinear dependencies. These latent variables serve as mediators between observed asset prices and future price trends, enabling the model to capture complex market dynamics that traditional algorithms miss. The variational autoencoder architecture learns these latent representations automatically from historical data.
Solution Approach 2:
The patent transitions from traditional single-dimensional time series analysis to multi-dimensional modeling by incorporating latent variable spaces. This dimensional expansion allows the model to capture nonlinear interactions and dependencies that exist in higher-dimensional feature spaces, significantly improving prediction accuracy for asset-price trends.
2Reliability
If deep-learning neural networks are trained on real-time series data to model future market uncertainty, then the model captures nonlinear market behavior, but the training data requirements and computational resources increase
Solution Approach 1:
The patent performs preliminary dimensionality reduction and feature extraction through the variational autoencoder before training the predictive model. By pre-processing the data to learn efficient latent representations, the system reduces the volume of training data needed while maintaining the ability to capture nonlinear market behavior and uncertainty.
Solution Approach 2:
The patent transforms the high-dimensional raw market data into a lower-dimensional latent space representation. This parameter transformation maintains the essential market dynamics and uncertainty characteristics while reducing the data volume required for training, making the deep-learning approach more feasible.
3Productivity
If portfolio optimization is performed on modeled probability distributions, then optimal risk-return tradeoffs are achieved, but the complexity of estimating expected risks and returns increases
Solution Approach 1:
The patent extracts the essential risk and return characteristics from complex market data by focusing on the learned probability distributions in the latent space. By taking out only the critical features needed for optimization, the system achieves efficient portfolio optimization without being overwhelmed by the full complexity of raw market data.
Solution Approach 2:
The patent replaces traditional mechanical portfolio optimization methods with a data-driven approach based on learned probability distributions from the variational autoencoder. This substitution allows for more accurate risk-return estimation by leveraging the nonlinear patterns captured in the latent representations, improving optimization efficiency.
Data Source
AI summary
A deep-learning neural network can be trained to model a probability distribution of the asset-price trends for a future time period using a training data set, which can include asset-price trends of a plurality of assets over a past time period and a latent vector sampled from a prior distribution associated with the asset-price trends of a plurality of assets. The training data set can represent a time series data. A portfolio optimization can be executed on the modeled probability distribution to estimate expected risks and returns for different portfolio diversification options.