Analyzing the Relationship Between Inflation Volatility and Long-Term Investment Decision-Making Strategies

Posted: Dec 10, 2025

• Zoey Perez

Abstract

This research investigates the complex relationship between inflation volatility and long-term investment decision-making strategies through an innovative computational framework that integrates behavioral economics with machine learning approaches. Traditional financial models often treat inflation as a predictable variable, failing to account for the psychological and cognitive impacts of volatility on investor behavior. Our study introduces a novel methodology combining agent-based modeling with deep reinforcement learning to simulate how investors adapt their long-term strategies in response to varying inflation volatility regimes. We developed a sophisticated simulation environment that incorporates realistic market dynamics, investor heterogeneity, and multiple inflation scenarios ranging from stable to highly volatile conditions. The framework captures both rational economic responses and behavioral biases that emerge during periods of uncertainty. Our results demonstrate that inflation volatility significantly alters investment horizon preferences, asset allocation decisions, and risk management approaches in non-linear ways that conventional models cannot adequately capture. We identified distinct behavioral patterns where moderate volatility stimulates strategic diversification while extreme volatility triggers either excessive conservatism or irrational risk-taking, depending on investor characteristics. The findings provide new insights for financial institutions, policymakers, and individual investors seeking to develop more resilient long-term investment strategies in an increasingly volatile global economic environment. This research contributes to the emerging field of computational behavioral finance by bridging the gap between macroeconomic theory and micro-level investment decision processes.