---
title: Emerging Market Debt Diversification
url: https://www.ml-quant.com/papers/ssrn/4475729/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: SSRN 4475729
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4475729
featured: 2023-06-14
citations: unknown
topic: Portfolio & Allocation
---


# Emerging Market Debt Diversification

Examining diversification of bonds on Banja Luka Stock Exchange using Markowitz's portfolio selection.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4475729
- Identifier: SSRN 4475729
- Released: 2023-06-11
- First featured: Quant Letter No. 4 (2023-06-14): https://www.ml-quant.com/issues/2023-06-14/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Portfolio & Allocation

## Related

- [Combining Reinforcement Learning and Barrier Functions for Adaptive Risk Management in Portfolio Optimization](https://www.ml-quant.com/papers/arxiv/2306.07013/): Reinforcement learning and barrier functions used in portfolio management framework
- [Green portfolio optimization: A scenario analysis and stress testing based novel approach for sustainable investing in the paradigm Indian markets](https://www.ml-quant.com/papers/arxiv/2305.16712/): Article discusses construction of environment-friendly green portfolio using ESG ratings and modern portfolio theory.
- [A Comparative Analysis of Portfolio Optimization Using Mean-Variance, Hierarchical Risk Parity, and Reinforcement Learning Approaches on the Indian Stock Market](https://www.ml-quant.com/papers/arxiv/2305.17523/): Comparing portfolio optimization approaches on stock data
- [Machine Learning for Socially Responsible Portfolio Optimisation](https://www.ml-quant.com/papers/doi/10-1145-3596947-3596966/): MV model amended to consider ESG scores for socially responsible investing.
- [Fast Empirical Scenarios](https://www.ml-quant.com/papers/arxiv/2307.03927/): Two new algorithms are introduced to extract key scenarios from large, complex data, showing potential in portfolio optimization.
- [Portfolio Optimization: A Comparative Study](https://www.ml-quant.com/papers/arxiv/2307.05048/): A study comparing three portfolio design methods found that the mean-variance portfolio is best for risk-adjusted returns, while autoencoder portfolios have the highest annual returns.