Bayesian Sparse Index-Tracking Portfolio Optimization
Paper#Finance, Portfolio Optimization, Bayesian Methods🔬 Research|Analyzed: Jan 3, 2026 20:10•
Published: Dec 26, 2025 18:46
•1 min read
•ArXivAnalysis
This paper addresses the practical challenges of building and rebalancing index-tracking portfolios, focusing on uncertainty quantification and implementability. It uses a Bayesian approach with a sparsity-inducing prior to control portfolio size and turnover, crucial for real-world applications. The use of Markov Chain Monte Carlo (MCMC) methods for uncertainty quantification and the development of rebalancing rules based on posterior samples are significant contributions. The case study on the S&P 500 index provides practical validation.
Key Takeaways
- •Applies Bayesian methods with a sparsity-inducing prior for index-tracking portfolio construction.
- •Employs MCMC for uncertainty quantification of portfolio weights and tracking error.
- •Develops rebalancing rules based on posterior samples to manage turnover and portfolio size.
- •Provides a case study on the S&P 500 index to demonstrate the approach.
Reference / Citation
View Original"The paper proposes rules for rebalancing that gate trades through magnitude-based thresholds and posterior activation probabilities, thereby trading off expected tracking error against turnover and portfolio size."