Modelling Financial Portfolio Weight Distributions via Rank-Size Laws
This project explores the application of rank-size laws to model the distribution of asset weights in financial portfolios. Rank-size laws, such as Zipf's Law and Pareto distributions, describe how the size of entities inversely relates to their rank within a set. Portfolio optimization models, like the renowned Markowitz model, help investors select assets that align with specific investment objectives. By applying these rank-size laws to optimal portfolio weights, we aim to uncover underlying patterns in asset allocation generated by optimization models. This approach will facilitate the integration of machine learning techniques into explainable models for financial investment allocation.
The project is a collaboration between LSBU Business School, Business and Economics Research Insight (Dr. Valerio Ficcadenti); Sapienza University of Rome – Department of Social Sciences and Economics (Prof. Roy Cerqueti); and Roma Tre University – Department of Business Studies (Prof. Francesco Cesarone and Mr. Alessio Di Paolo).
In this section
- Ways of Machine Seeing
- Multi-Access Edge Computing
- Digitalisation and Justice
- Transnational Narratives
- Characterisation of Cortical Bone using Ultra-fast Ultrasound
- Modelling Financial Portfolio Weight Distributions via Rank-Size Laws