Action-State Dependent Dynamic Model Selection

ArXiv ID: 2307.04754 “View on arXiv”

Authors: Unknown

Abstract

A model among many may only be best under certain states of the world. Switching from a model to another can also be costly. Finding a procedure to dynamically choose a model in these circumstances requires to solve a complex estimation procedure and a dynamic programming problem. A Reinforcement learning algorithm is used to approximate and estimate from the data the optimal solution to this dynamic programming problem. The algorithm is shown to consistently estimate the optimal policy that may choose different models based on a set of covariates. A typical example is the one of switching between different portfolio models under rebalancing costs, using macroeconomic information. Using a set of macroeconomic variables and price data, an empirical application to the aforementioned portfolio problem shows superior performance to choosing the best portfolio model with hindsight.

Keywords: Reinforcement Learning, Portfolio Optimization, Dynamic Programming, Model Switching, Macroeconomic Variables

Complexity vs Empirical Score

• Math Complexity: 8.0/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper involves advanced stochastic dynamic programming, consistency proofs, and reinforcement learning algorithms, indicating high mathematical density. It also includes an empirical application with macroeconomic variables and price data, demonstrating backtest-ready implementation with performance comparisons.

  flowchart TD
    A["Research Goal: Dynamic Model Selection<br>Under Switching Costs"] --> B["Data Inputs: Macroeconomic Variables & Asset Prices"]
    B --> C["Methodology: RL Algorithm for<br>Dynamic Programming Approximation"]
    C --> D["Computational Process:<br>Estimate Optimal State-Dependent Policy"]
    D --> E["Outcome: Superior Performance vs<br>Hindsight Optimal Model Selection"]