Information Theory-Based Approach to Portfolio Optimization

Dear Colleagues,

Portfolio optimization has been the main quantitative approach for constructing portfolios that exploit diversification among various financial assets and managing risk of investments. There has been extensive research on incorporating various risk measures, adding practical constraints, enhancing portfolio robustness, and solving multi-stage investment problems. These quantitative models have become more important in recent years due to the rise in data-driven methods and automated services. The expansion of data analysis and machine learning in particular is presenting advanced approaches to portfolio construction. For example, alternative data allow portfolios to consider non-traditional information that improves asset modeling and factor-based allocations. Further, machine learning models such as neural networks are being used to build portfolio models that better capture inherent characteristics of assets for forming efficient portfolios from a large dataset. Data-based approaches have also led to further use of information theory, entropy, and network theory for measuring asset risk, analyzing market dynamics, and understanding complex financial systems for making allocation decisions.

This Special Issue aims to present these advancements in portfolio optimization and investment management based on machine learning, big data analysis, information theory, and maximum entropy methods.

Prof. Dr. Jang Ho Kim
Guest Editor

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