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Cost functions and model combination for VaR-based asset allocation using neural networks.

N Chapados¹, Y Bengio

¹Department of Computer Science and Operations Research, Université de Montréal, Montréal, QC H3C 3J7, Canada. chapados@iro.umontreal.ca

IEEE Transactions on Neural Networks

|February 6, 2008

Summary

This study presents a novel asset-allocation framework using neural networks to control portfolio value-at-risk. Both tested neural network approaches significantly outperformed market benchmarks.

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Area of Science:

Quantitative Finance
Computational Finance
Machine Learning in Finance

Background:

Traditional asset allocation often relies on mean-variance optimization.
Controlling portfolio risk, specifically value-at-risk (VaR), is crucial for investment strategies.
Neural networks offer advanced capabilities for financial modeling and decision-making.

Purpose of the Study:

To introduce a new asset-allocation framework centered on actively managing portfolio value-at-risk (VaR).
To compare two distinct neural network paradigms for portfolio allocation within this VaR-controlled framework.
To evaluate the effectiveness of soft input variable selection and model combination methods.

Main Methods:

Developed an asset-allocation framework based on active control of portfolio value-at-risk (VaR).
Implemented two neural network allocation paradigms: 1) forecasting asset behavior with a mean-variance allocator, and 2) direct portfolio allocation by the network.
Utilized soft input variable selection and model combination (committee) methods for hyperparameter tuning.

Main Results:

Both neural network paradigms, when integrated into the VaR-controlled framework, significantly outperformed the benchmark market.
Soft input variable selection demonstrated considerable utility in the allocation process.
Model combination methods effectively systematized hyperparameter selection during neural network training.

Conclusions:

The proposed asset-allocation framework effectively controls portfolio value-at-risk.
Neural networks, applied either for forecasting or direct allocation, enhance portfolio performance beyond traditional methods.
The combination of advanced techniques like soft variable selection and model committees improves the robustness and performance of AI-driven investment strategies.