Latent Variable Estimation in Bayesian Black-Litterman Models
Authors: Thomas Yuan-Lung Lin, Jerry Yao-Chieh Hu, Paul W. Chiou, Peter Lin
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, on 30-year Dow-Jones and 20-year sector ETF data, we improve Sharpe ratios by 50% and cut turnover by 55% relative to Markowitz and the index baselines. This work turns BL into a fully data-driven, view-free, and coherent Bayesian framework for portfolio optimization. |
| Researcher Affiliation | Collaboration | 1Department of Physics, National Taiwan University, Taipei, Taiwan 2Department of Computer Science, Northwestern University, Evanston, IL, USA 3Center for Foundation Models and Generative AI, Northwestern University, Evanston, IL, USA 4D Amore-Mc Kim School of Business, Northeastern University, Boston, MA, USA 5Gamma Paradigm Capital, New York, NY, USA 6Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA. |
| Pseudocode | No | The paper describes models and their mathematical formulations, theorems, and proofs. There are no explicit sections or blocks labeled 'Pseudocode' or 'Algorithm'. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code, nor does it provide a link to a code repository. The text only refers to 'Appendix A offers a practical guide for our models' which describes hyperparameter selection, not code availability. |
| Open Datasets | Yes | Dataset I: SPDR Sector ETFs. We collect adjusted daily closing prices and volume for 11 Sector ETFs (Table 4) from April 13, 2004 to February 22, 2024 (20 years). Dataset II: Dow Jones Index. We collect adjusted daily closing prices and volume for 41 stocks (Table 5) that have been part of the Dow Jones index from January 5, 1994 to February 22, 2024 (30 years). |
| Dataset Splits | Yes | On each monthly rebalance day, the model outputs a portfolio weight w SLP BL that maximizes the Sharpe ratio (Definition F.1), a standardized mean-variance optimization framework (Definition 2.1). In the model, the prior is set as traditional Markowitz model and the features are selected based on nine generic indicators (Table 3) derived from asset-specific data. We follow Appendix A for the choice of {Σ, Σ0, θ0, αF , βF , ΩF } except that, to avoid the issues of mismatch scale, we use price and indicators data to derive the regression parameters. ... We present the results for five pairs of traditional Markowitz model and our Black-Litterman model with varying rolling window lengths of historical returns: 50 days, 80 days, 100 days, 120 days, and 150 days. |
| Hardware Specification | No | The paper describes the methodology, empirical results, and backtesting procedures for financial models but does not mention any specific hardware (like CPU/GPU models, memory, or cloud instance types) used for running the experiments. |
| Software Dependencies | No | The paper describes various modeling techniques and references related work involving machine learning methods (e.g., GARCH, LSTM, SVM). However, it does not specify any software dependencies, programming languages, or library versions used for its own implementation of the models and experiments. |
| Experiment Setup | Yes | On each monthly rebalance day, the model outputs a portfolio weight w SLP BL that maximizes the Sharpe ratio (Definition F.1)... We present the results for five pairs of traditional Markowitz model and our Black-Litterman model with varying rolling window lengths of historical returns: 50 days, 80 days, 100 days, 120 days, and 150 days. ... Appendix A provides a practical guide to derive hyperparameter set {Σ, Σ0, θ0, αF , βF , ΩF } ... and {Σ, Σ0, θ0, P, α, β, ΩF , Ψ , ν , Ω0} ... Table 3: Common Indicators Used [with] Hyperparameters |