Dynamic Factor Models with Forward-Looking Views

Paper: arXiv 2509.11528

Authors: Anas Abdelhakmi, Andrew E. B. Lim

Abstract

Prediction models calibrated using historical data may forecast poorly if the dynamics of the present and future differ from observations in the past. For this reason, predictions can be improved if information like forward looking views about the state of the system are used to refine the forecast. We develop an approach for combining a dynamic factor model for risky asset prices calibrated on historical data, and noisy expert views of future values of the factors/covariates in the model, and study the implications for dynamic portfolio choice. By exploiting the graphical structure linking factors, asset prices, and views, we derive closed-form expressions for the dynamics of the factor and price processes after conditioning on the views. For linear factor models, the price process becomes a time-inhomogeneous affine process with a new covariate formed from the views. We establish a novel theoretical connection between the conditional factor process and a process we call a Mean-Reverting Bridge (MrB), an extension of the classical Brownian bridge. We derive the investor’s optimal portfolio strategy and show that views influence both the myopic mean-variance term and the intertemporal hedge. The optimal dynamic portfolio when the long-run mean of the expected return is uncertain and learned online from data is also derived. More generally, our framework offers a generalizable approach for embedding forward-looking information about covariates in a dynamic factor model.

Complexity vs Empirical Score

• Math Complexity: 8.0/10
• Empirical Rigor: 2.0/10
• Quadrant: Lab Rats — theoretically deep, empirically untested

Why this score: The paper is mathematically dense, featuring advanced derivations for time-inhomogeneous affine processes, novel stochastic processes (Mean-Reverting Bridge), and multi-period stochastic control, indicating high complexity. Empirical rigor is low as the summary and excerpt focus entirely on theoretical derivations, model formulation, and literature review, with no mention of backtesting, datasets, statistical metrics, or implementation details.

Research Flowchart

  flowchart TD
  A["Research Goal: Develop a framework to incorporate<br>forward-looking views into dynamic factor models<br>to improve asset price predictions and portfolio choice."] --> B["Key Methodology: Integrate Expert Views<br>with a Dynamic Factor Model"]
  
  B --> C["Data & Inputs<br>Historical Data & Expert Views"]
  C --> D["Computational Process<br>Derive closed-form conditional dynamics"]
  D --> E["Computational Process<br>Identify connection to Mean-Reverting Bridge<br>(MrB) process"]
  E --> F["Computational Process<br>Derive optimal dynamic portfolio strategy<br>incorporating learning of long-run means"]
  
  F --> G["Key Findings & Outcomes<br>1. Price process becomes time-inhomogeneous affine<br>2. Views impact myopic and hedge portfolio terms<br>3. MrB provides novel theoretical connection<br>4. Framework generalizable to other models"]

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7. Perturbation Analysis for Investment Portfolios Under Partial Information with Expert Opinions (2017) — link
8. Debiased E xpert F orecasts in C ontinuous- T ime A sset A llocation (2020)

1 of 39 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.