The Impact of Investor Sentiment on Portfolio Optimization in the Tehran Stock Exchange and Cryptocurrency Markets - Nourollahi, Soroush; rezaei, Nader; Jafari, Ali; Pakmaram, Asgar (Business, Marketing, and Finance Open, 2026)
The present study was conducted with the aim of examining the impact of investor sentiment on portfolio optimization in the stock market and cryptocurrency market using the framework of Cumulative Prospect Theory (CPT). In terms of purpose, this research is applied-developmental, and in terms of nature, it is descriptive-analytical, conducted within the framework of the critical realism paradigm and employing a mixed-methods (qualitative–quantitative) approach. In the qualitative section, interviews were conducted with 12 financial experts to localize behavioral dimensions. In the quantitative section, behavioral data were collected through a questionnaire from 320 active investors and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Financial data consisted of daily time series of 10 selected assets (5 stocks from the Tehran Stock Exchange and 5 cryptocurrencies) over the period 2020–2025, which were modeled using GARCH model with Student’s t and GED distributions, as well as Student-t copula functions. Portfolio optimization was performed using the CPT approach and Monte Carlo simulation. The results indicated that investor sentiment (including loss aversion with a factor loading of 0.703, herding behavior with a factor loading of 0.731, and fear of missing out (FOMO) with a factor loading of 0.621) has a negative and significant effect on portfolio optimization. Path coefficients were significant for the mixed portfolio (−0.702, t = 20.104), stock portfolio (−0.671, t = 16.552), and cryptocurrency portfolio (−0.670, t = 18.002). The intensity of loss aversion in the cryptocurrency market (λ = 2.31) was higher than in the stock market (λ = 2.10). The GARCH models confirmed high volatility persistence (α + β close to 0.98) and heavy-tailed distributions in both markets. The Student-t copula indicated strong tail dependence within the crypto market (ρ = 0.68) and low correlation between the two markets. CPT-based optimization generated higher subjective value compared to the mean–variance approach (0.091 versus 0.068), but resulted in more conservative portfolios. Investor sentiment—particularly loss aversion and herding behavior—has a strong negative impact on portfolio optimization in both stock and cryptocurrency markets, and this effect is intensified in multi-market and highly volatile environments. The CPT model, through asymmetric modeling of gains and losses, demonstrates a strong capability in explaining actual investor behavior.