Short-Term Portfolio Optimization for Retail Investors in Iran Based on Real-Time Market Volatility and Liquidity Patterns

Abstract

Short-term portfolio management has become a central concern for retail investors in emerging markets, where volatility spikes and liquidity fluctuations can significantly influence investment outcomes. In Iran’s fast-evolving financial environment, short-term decision-making is shaped by rapid information flows, structural liquidity constraints, elevated market uncertainty, and heterogeneous investor behaviour. Despite the increasing involvement of retail participants in the Iranian capital market, limited academic work has examined how real-time volatility and liquidity patterns can be systematically incorporated into short-term portfolio optimization models tailored to their specific constraints. This study develops an integrated analytical framework that combines high-frequency volatility estimation, liquidity-adjusted return measures, and behavioural considerations to enhance short-term portfolio allocation for retail investors. Using real-time market microstructure data—including intraday price variance, bid–ask spreads, turnover velocity, and depth-of-market indicators—the research constructs a multi-stage optimization model that incorporates volatility-sensitive asset weights and liquidity-penalized return functions. The framework further evaluates the sensitivity of optimized portfolios to liquidity shocks, volatility clustering, and sudden price jumps commonly observed in emerging markets. Through empirical evaluation using Iran’s equity and fixed-income short-term instruments, the study demonstrates that portfolios incorporating liquidity-adjusted risk metrics outperform traditional variance-based models, particularly during periods of heightened uncertainty. The findings also reveal that retail investors’ allocation decisions can be substantially improved when real-time liquidity conditions and short-horizon volatility estimators are integrated into the optimization process. The contribution of this research is threefold: first, it offers a practical and data-driven short-term optimization model calibrated specifically for retail investors in Iran; second, it empirically highlights the importance of liquidity constraints and volatility dynamics in shaping short-term portfolio performance; and third, it provides a foundation for constructing adaptive allocation rules that reflect live market conditions. These insights hold implications for investor education, financial advisory practices, and the design of accessible portfolio optimization tools within emerging markets.