Research Article
Mostafa Sharif; Parisa Shahnazari-Shahrezaei; Meysam Doaei
Abstract
This paper introduces a two-stage stochastic optimization model for portfolio selection, designed to address decision-making uncertainties in the context of the Iranian stock market. The model accounts for a range of disruption scenarios—including economic sanctions, oil price fluctuations, political ...
Read More
This paper introduces a two-stage stochastic optimization model for portfolio selection, designed to address decision-making uncertainties in the context of the Iranian stock market. The model accounts for a range of disruption scenarios—including economic sanctions, oil price fluctuations, political instability, and currency devaluation—enabling dynamic portfolio adjustments to optimize risk-adjusted returns. To manage extreme downside risks, it employs Conditional Value-at-Risk (CVaR) as the risk measure, while simultaneously aiming to maximize expected returns. Compared to traditional mean-variance portfolio optimization, the proposed model demonstrates clear advantages by adapting to uncertain market conditions through scenario-based rebalancing. Sensitivity analysis highlights the model’s responsiveness to critical parameters such as risk aversion, scenario probabilities, and adjustment costs, offering valuable insights into their impact on portfolio performance. The results show that the two-stage model delivers stronger risk management and improved return outcomes than static approaches. Nevertheless, limitations exist, particularly regarding the reliance on accurate scenario probabilities and the assumption of fixed adjustment costs, which may affect real-world applicability. Future research could enhance the model by applying machine learning to refine probability estimates, extending its use to other emerging markets, and integrating more flexible and dynamic cost structures for asset reallocation. The proposed model provides a robust framework for managing investment portfolios in volatile and uncertain environments.
Research Article
Farshid Mehrdoust; Arezou Karimi
Abstract
Precise modeling of financial asset volatility is significant for robust risk management and derivative pricing. Recent scholarly investigations have demonstrated a significant interest in employing stochastic processes with short-term memory for this purpose. Consequently, rigorous ...
Read More
Precise modeling of financial asset volatility is significant for robust risk management and derivative pricing. Recent scholarly investigations have demonstrated a significant interest in employing stochastic processes with short-term memory for this purpose. Consequently, rigorous examination of the existence and uniqueness of solutions for these processes assumes critical importance. This study commences with the precise definition of a fractional operator for $H \in(0, \frac{1}{2})$. Subsequently, the finiteness of the second-order moment of the Itô-Skorokhod integral is meticulously investigated, utilizing the aforementioned operator, specifically within the range of $H \in(0, \frac{1}{2})$. Ultimately, leveraging this moment and rigorously applying Lipschitz and linear growth conditions, and through the application of Gronwall's inequality, the existence and uniqueness of solutions for stochastic differential equations with short-term memory are definitively established.
Research Article
Ehsan Zanganeh; Nafiseh Keshtgar
Abstract
Over recent decades, Iran’s economy has faced significant challenges, including international sanctions, severe exchange rate fluctuations, and high inflation rates, all of which have the potential to drastically alter the trajectory of economic growth. This study investigates the dynamic impacts ...
Read More
Over recent decades, Iran’s economy has faced significant challenges, including international sanctions, severe exchange rate fluctuations, and high inflation rates, all of which have the potential to drastically alter the trajectory of economic growth. This study investigates the dynamic impacts of exchange rate volatility, financial development, trade openness, and inflation on Iran's economic growth over the monthly period from 2011 to 2024, using a Time-Varying Parameter Vector Autoregressive (TVP-VAR) model. This nonlinear approach is adopted due to the limitations of linear models in capturing such complex dynamics. The findings reveal that both exchange rate volatility and financial development exert a negative and statistically significant impact on economic growth, whereas trade openness contributes positively over the long term. Inflation is also found to have a detrimental long-run effect on growth. In the short run, economic growth responds asymmetrically to these variables across different time periods. These results underscore the necessity for policymakers to account for such asymmetric effects when designing and implementing economic policies, especially in contexts affected by currency shocks and sanctions.
Research Article
Yones Esmaeelzade Aghdam; Hamid Mesgarani; Ali Heidarvand
Abstract
Predicting the price of Ethereum remains a significant challenge due to the extreme volatility and nonlinear dynamics inherent in the cryptocurrency market. This study proposes a novel hybrid model that integrates a Gated Recurrent Unit (GRU) with a Transformer Encoder to effectively capture both short-term ...
Read More
Predicting the price of Ethereum remains a significant challenge due to the extreme volatility and nonlinear dynamics inherent in the cryptocurrency market. This study proposes a novel hybrid model that integrates a Gated Recurrent Unit (GRU) with a Transformer Encoder to effectively capture both short-term and long-term temporal dependencies for enhanced Ethereum price forecasting. The model was trained on daily historical data from 2017 to 2023. The dataset, sourced from Yahoo Finance, includes Ethereums open, high, and low prices, along with its trading volume. Additionally, Bitcoins closing price and two technical indicators, On-Balance Volume (OBV) and Average True Range (ATR), were incorporated. Pearson and Spearman correlation analyses confirmed strong interdependencies among the selected features. The model underwent training for 90 epochs, utilizing the Mean Squared Error (MSE) as the loss function and the Adam optimizer. Under identical experimental conditions, the proposed hybrid model significantly outperformed several baseline architectures, including standalone GRU, Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Transformer Encoder, and CNN–GRU hybrid models. Specifically, the model achieved a Mean Absolute Error (MAE) of 0.007199 (equivalent to $34.03), which is considerably lower than Ethereums average daily price fluctuation of $74.73. These findings demonstrate that the GRU–Transformer Encoder hybrid model is highly effective in extracting intricate patterns from volatile financial time series. Consequently, it can serve as a practical and robust tool for market trend analysis and risk management.
Research Article
Reenu Yadav; Wajahat Ali; Monika Arora
Abstract
This study develops an optimization-based framework for gamification in financial technology (FinTech) to enhance customer loyalty and advance Sustainable Development Goals (SDGs). Data were collected from 33 empirical studies conducted between 2015 and 2025 and analyzed through meta-analysis using the ...
Read More
This study develops an optimization-based framework for gamification in financial technology (FinTech) to enhance customer loyalty and advance Sustainable Development Goals (SDGs). Data were collected from 33 empirical studies conducted between 2015 and 2025 and analyzed through meta-analysis using the Sidik-Jonkman method. The findings demonstrate that gamification features in FinTech platforms significantly strengthen customer loyalty (β = 5.066, p < 0.001), highlighting the central role of engagement mechanisms such as points, rewards, and leaderboards. The residual heterogeneity estimates (τ² = 0.002; I² = 0.604%) reveal low variability across studies, confirming the efficiency and reliability of optimization-driven gamification strategies. Moreover, results indicate that FinTech innovations contribute positively to sustainable development outcomes, with optimization-based gamification mediating between FinTech adoption and SDG achievements. The absence of publication bias (Egger's test: z = -0.543, p = 0.587) further reinforces the robustness of the findings. This study provides strong empirical evidence that integrating gamification with optimization techniques fosters customer loyalty and aligns digital finance practices with global sustainability priorities. It advances theoretical understanding of how digital engagement tools interact with optimization methods to produce measurable social and economic outcomes. It also offers actionable insights for researchers, policymakers, and industry practitioners. The framework opens new pathways for designing customer-centric FinTech solutions that drive business growth and contribute to realizing SDGs.
