Investor Psychology and Adaptive Risk Optimization Strategies in Volatile Emerging Financial Markets

Abstract

This paper examines investor psychology and adaptive risk-optimization strategies in volatile emerging financial markets. It synthesizes behavioral finance theories: loss aversion, overconfidence, herding, and limited attention with empirical evidence from emerging-market equities, fixed income, and local-currency instruments. We discuss how psychological drivers interact with market microstructure, liquidity constraints, and macro-financial shocks to produce amplified volatility and regime shifts. The study then evaluates adaptive risk-management frameworks that explicitly incorporate behavioral signals (sentiment indices, attention proxies, and retail flow data) into dynamic portfolio allocation and volatility forecasting. We compare model classes including shrinkage-based covariance estimators, adaptive minimum-variance and risk-parity approaches, and machine-learning and reinforcement-learning methods aimed at regime detection and real-time weight rebalancing. Empirical strategies for emerging markets, where data sparsity, non-stationarity, and higher transaction costs prevail, are emphasized. The paper proposes an integrative framework that (1) uses investor-psychology indicators to inform regime classification, (2) applies adaptive covariance targeting and shrinkage in low-data settings, and (3) employs cautious, behavioral-aware execution rules to limit adverse feedback loops from mechanical rebalancing. We conclude with policy and practitioner implications for improving market resilience, and outline avenues for future research on explainability, data governance, and field experiments in retail investor education.