Analyzing the Crowding-Out Effect of Investment Herding on Consumption: An Optimal Control Theory Approach

ArXiv ID: 2507.10052 “View on arXiv”

Authors: Huisheng Wang, H. Vicky Zhao

Abstract

Investment herding, a phenomenon where households mimic the decisions of others rather than relying on their own analysis, has significant effects on financial markets and household behavior. Excessive investment herding may reduce investments and lead to a depletion of household consumption, which is called the crowding-out effect. While existing research has qualitatively examined the impact of investment herding on consumption, quantitative studies in this area remain limited. In this work, we investigate the optimal investment and consumption decisions of households under the impact of investment herding. We formulate an optimization problem to model how investment herding influences household decisions over time. Based on the optimal control theory, we solve for the analytical solutions of optimal investment and consumption decisions. We theoretically analyze the impact of investment herding on household consumption decisions and demonstrate the existence of the crowding-out effect. We further explore how parameters, such as interest rate, excess return rate, and volatility, influence the crowding-out effect. Finally, we conduct a real data test to validate our theoretical analysis of the crowding-out effect. This study is crucial to understanding the impact of investment herding on household consumption and offering valuable insights for policymakers seeking to stimulate consumption and mitigate the negative effects of investment herding on economic growth.

Keywords: Investment herding, Crowding-out effect, Optimal control theory, Consumption optimization, Household behavior

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 3.0/10
• Quadrant: Lab Rats
• Why: The paper employs advanced optimal control theory to derive analytical solutions, which is mathematically dense. However, the empirical validation is limited to a single real data test described in the abstract, lacking detailed backtesting procedures, dataset specifics, or code, which reduces its empirical rigor.

  flowchart TD
    A["Research Goal:<br>Quantify Crowding-Out Effect of<br>Investment Herding on Consumption"] --> B["Methodology: Formulate Optimization Problem<br>Using Optimal Control Theory"]
    B --> C["Data/Inputs:<br>Real Data & Key Parameters<br>Interest Rate, Excess Return, Volatility"]
    C --> D["Computational Process:<br>Solve Analytical Solutions for<br>Optimal Investment & Consumption Paths"]
    D --> E{"Key Outcomes"}
    E --> F["Theoretical Finding:<br>Existence of Crowding-Out Effect<br>Investment Herding Reduces Consumption"]
    E --> G["Parametric Analysis:<br>Impact of Interest Rate,<br>Excess Return, Volatility"]
    E --> H["Empirical Validation:<br>Real Data Test Confirms<br>Theoretical Analysis"]