arXiv (All)
open access
Bayesian Parametric Portfolio Policies
Parametric portfolio policies map predictors into portfolio weights without modelling returns, making them a natural tool for factor timing. With many predictors, if the policy has more coefficients than observations the data cannot pin it down. We develop Bayesian Parametric Portfolio Policies (BPP
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- A Price-Based Framework for Stochastic Portfolio Theory#1199999
- From Economic Explanation to Performance Improvement in Machine-Learning Portfolios#1326040
- Self-Consistent Adjoint Policy Iteration for Constrained Dynamic Portfolio Choice#974544
- Selective Tie Learning: Mitigating Spurious Correlations in Preference Optimization#1450777
- Chronos Policy: Learning Robot Policies on Physical Time#1298425
Record · ID 1458262
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive