Advances in Mathematical Finance and Application (AMFA)

This study investigates the bibliometric analysis on asset allocation for portfolio optimization using machine learning algorithms. The primary objective is to identify and analyze the scientific literature through bibliometric analysis to uncover key themes, authors, sources, highly-cited articles, and countries involved in portfolio management research. To achieve this, 304 articles indexed in Scopus and Web of Science from 1990 to 2023 were analyzed. Using RStudio software, the study highlights various models employed in this field, along with tables, graphs, maps, and key performance metrics related to article production and citation impact. The findings reveal an upward trend in the use of machine learning for optimal portfolio management, asset allocation, and risk management since 2016. Additionally, the United States and China emerged as leading contributors to this literature. The results provide practical insights for market participants, especially those in fintech and finance sectors, to identify optimal machine learning solutions for decision-making processes. These findings also guide students in focusing their research efforts on underexplored areas within this domain.

One of the main concerns that have been emphasized in recent decades is environment. This research aims to investigate impact of green social capital on financial performance with an emphasis on green innovation and green competitive advantage. The research method is based on the objective, applied, and in terms of the implementation method, it is a descriptive survey. The statistical population includes the employees and managers of small and medium-sized manufacturing businesses of Tehran in Iran, and a sample of 277 people was selected using the available sampling method and Cochran formula. To collect the data of the research, a questionnaire tool was used and after confirming the validity and reliability, it was distributed among the sample size in summer 2024. Structural equation modeling and Smart PLS software were used to test the research hypotheses. The results indicate that green social capital, green innovation and green competitive advantage have a positive and significant impact on financial performance. Also by the use of Sobel's test, it was determined green innovation and green competitive advantage play a mediating role in the impact of green social capital on financial performance. These findings may provide policy-makers with crucial information for better environment performance and financial development, which helps address the conflict between stakeholders and companies, may be applicable in Iran and other countries as well.

Some companies manipulate financial statements to users and commit fraud. Therefore, effort to detect fraud is essential. Meanwhile, data mining techniques have increasingly popular. This article aimed to use an advanced model to detect fraudulent financial statements and compare it with the other methods. Crowd optimization algorithms have been considered to solve many optimization problems, but so far, they have not been used in fraud detection research to determine the optimal value of SVM parameters and optimize ANN architecture. In this research, for the first time, the PSO algorithm was used as one of the best innovative optimization algorithms for these optimizations due to its memory and high convergence speed, as well as having solutions for exiting from local optimal points and cooperation and information sharing between particles to detect fraud. For this purpose, the financial statements of companies admitted to the stock exchange from 2017 to 2023 were reviewed. The findings showed that the SVM-PSO method, with 89.86%accuracy, compared to the ANN-PSO method, with 80.43%accuracy, and the LR method, with 69.57%accuracy, performs better in identifying suspected fraudulent financial statements. Combining the PSO algorithm with the SVM method has proven superior to other methods due to SVM's high ability to reduce false negatives and PSO's ability to fine-tune its parameters. This combination can be used for high-accuracy financial statement fraud detection.

In recent years, sustainability has garnered significant attention as a critical issue, emphasizing the integration and balance of environmental, economic, and social dimensions. These three pillars collectively form the foundation of supply chain sustainability, offering the potential to harmonize the interests of diverse stakeholder groups. This study aims to explore the impact of corporate sustainability performance on the asymmetric market response to non-continuing profit or loss items transactions or events that do not recur in a company's regular operations among companies listed on the Tehran Stock Exchange. The research analysed a sample of 110 companies over the period from 2016 to 2022. This applied study employed a post-event methodology, utilizing a panel data approach to test the research hypotheses. Statistical analysis was conducted using Eviews software. The findings revealed an asymmetric market response to positive and negative non-continuing items in the profit or loss statements. Specifically, the market responded negatively to positive non-continuing items and positively to negative ones. This asymmetric reaction can be attributed to the unique characteristics of non-continuing items, as market mechanisms differentiate between positive and negative events based on their distinct impacts on the company’s profitability and operations. Furthermore, the evidence suggests that enhanced corporate sustainability performance mitigates the market's asymmetric evaluation of these non-continuing items. By improving sustainability practices, companies can reduce market misjudgements, thereby aligning stakeholder perceptions more closely with the firm's overall strategic direction and operational realities.

This article introduces an integrated approach for addressing uncertainty and improving human-oriented risk control by combining differential equation modeling with neural and fuzzy logic enhancements. Differential equation modeling provides a structured mathematical foundation for capturing price dynamics over time, while neural and fuzzy logic components adaptively adjust the model to account for nonlinear behaviors and uncertain market signals. The proposed framework is applied within volatility-aware trading strategies, comparing fixed-exposure and downside-scaled momentum approaches. Using daily data from five major digital currencies spanning 2016 to 2024, the model demonstrates improved prediction accuracy and controlled exposure under volatile conditions. While the adaptive strategy offers reduced drawdowns and more stable weight distributions, it does not universally outperform in return-to-risk metrics. However, the integrated system consistently shows better alignment with market risk regimes, particularly in directional accuracy, confidence calibration, and drawdown control enhancing its practical viability for real-world deployment.

This study explores whether combining Empirical Mode Decomposition (EMD) with machine learning models Artificial Neural Networks (ANN), Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM)—can improve the accuracy of Bitcoin price volatility (VBTC) predictions. Utilizing daily Bitcoin price data from September 2011 to December 2024, the research, conducted using R software, compares the performance of hybrid models (EMD-ANN, EMD-RNN, EMD-LSTM) against standalone machine learning models and traditional time series methods like ARIMA. The results demonstrate that hybrid models significantly outperform their non-hybrid counterparts, with the EMD-RNN model achieving the highest accuracy, reducing Mean Absolute Error (MAE) by 95.76% and Root Mean Squared Error (RMSE) by 96.35%. The decomposition of VBTC into Intrinsic Mode Functions (IMFs) revealed distinct short-term and long-term volatility components, providing deeper insights into market behavior. The findings highlight the superiority of integrating EMD with machine learning for volatility forecasting, offering enhanced predictive accuracy and robustness. This research underscores the potential of advanced analytical techniques in improving risk management and investment strategies in highly volatile cryptocurrency markets.