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Today, numerous studies have investigated the role and effects of intangible assets on companies' performance. The existence of extensive literature regarding the use of intangible assets as a competitive advantage, in addition to the importance of performance dimensions affected by these assets, is the focus of the attention of various researchers. Regarding examining the performance dimensions of banks, the CAMEL model can be a suitable basis for evaluating the effects of intellectual capital. Although there are many models for calculating intellectual capital, the added value of the intellectual coefficient is still one of the most common methods. Therefore, in the current research, the effects of intellectual capital (based on the calculation model of the coefficient of added value of intellectual capital) on the performance of banks (based on the CAMEL model) have been investigated. Also, the shape of the function (linear or nonlinear) has been investigated in this research. The results show that the value added of the intellectual capital coefficient in the ninth quantile has a significant relationship with the variable of capital adequacy. The form of the relationship is nonlinear and inverted U. This variable affects the quality of assets in the seventh to ninth quantiles, management in the eighth and ninth quantiles, income in the first and second and sixth to ninth quantiles, and liquidity in the first to fourth quantiles. The shape of the function is U, inverse U, and inverse U, respectively.

The development of the nation’s economy is supported by the growing diversity and enthusiasm of interest among academics, particularly in the transforming procedures with an entrepreneurial orientation. The study investigates changing entrepreneurial-oriented practices and the performance of SMEs utilizing a cross-sectional survey amongst the four top grocery registered SMEs operating in Ikorodu local government division of Lagos State, Nigeria. The tool for gathering data is a questionnaire with two sections: bio-data and research questions. While 100 respondents formed the sample size proportionally selected from the study focus. Pearson correlation is the statistical tool for data analysis. The study reveals that creativeness and alleviation of business loss have a highly significant impact on SMEs performance; with pro-activeness having a weak, but positive relevance on SMEs performance. In conclusion, to further improve and optimize entrepreneurial orientation practices and adapt to future threatening changes, firms need to strengthen the desire for healthy competition, identifying and targeting the weaknesses of their competitors. It is recommended that more innovativeness should be sought after as guiding principles for capability and adaptability to handle the complexity of the ever-changing business surrounding, while involvement in risk-taking should be adopted with inevitable zeal and willingness to attain organizational growth and improved performance. Pro-activeness should be given adequate consideration to maintain and surpass existing performance as opportunities come but once. Despite study limitations, future researchers may need to enlarge the sample size and create awareness of research conducted on SMEs generally. Keywords: Change, Entrepreneurial orientation, Performance, Practices, SMEs.

The positive impacts of fiscal policy could be undermined when accompanied by uncertainty. We examined the effect of fiscal policy uncertainty on economic activities in the provinces of Iran. It includes production, investment, unemployment, and economic participation of the active workforce in these provinces, taking into account the effects of economic sanctions imposed on the economy. We employed two types of shocks: fiscal level shock (representing fiscal policy) and its volatility shock (as fiscal policy uncertainty), which derived from a specified fiscal reaction function. We estimated a Panel VAR model using provincial data from 2003 to 2020. The results of the impulse response function indicated that following the impulse in the fiscal policy uncertainty, the response shows an increase in the unemployment rate in the short run, a decrease in the capital investment, and an increase in the inflation rate in the short and medium terms. In the medium and long term, the response indicates a decrease in GDP growth and a reduction in the economic participation rate of the active workforce.

Embedded options are virtually new instruments identical to options in many aspects except their non-tradable nature. Testing the efficiency of the Variance Gamma and Black-Scholes-Merton model on these instruments would provide a vision of transitioning from the classical model with its deficiency to more intricate models. Considering the complicated nature of the Variance Gamma stochastic process to price options, the Fast Fourier Transform (FFT) method is used in conjunction with the Nelder-Mead Simplex method to calibrate models. This research uses the Fast Fourier Transform (FFT) to price four embedded options with the ticker symbols Hefars912, Heghadir912, Heksho208, and Hetrol911 under the two models. The result approves that the Variance Gamma process is more efficient than the Black-Scholes-Merton model in pricing embedded options. Consequently, the variance gamma process would generate fewer errors in pricing those options that can be used in a practical sense.

This study aims to develop a working capital management model for companies listed on the Tehran Stock Exchange. The proposed model determines the expected level of working capital for a company, enabling it to create the highest possible value. Additionally, this model can be used to assess the efficiency of working capital management. The discrepancy between the actual level of working capital and the expected level serves as an indicator of inefficiency in working capital management. Initially, based on theoretical foundations and expert opinions, 28 variables affecting working capital were selected. Then, using the operational working capital index, the research models were estimated using multiple regression and genetic algorithm techniques for data from 156 companies over the period from 2011 to 2022. Influential variables were identified and filtered. Finally, suitable working capital management models were identified based on two criteria: (1) the strong correlation between the errors of the fitted models and the working capital efficiency of the company, and (2) the model’s accuracy in identifying companies prone to excess or shortage of working capital. In total, after estimating 119 different models using regression and genetic algorithm methods, four suitable working capital management models were determined. The regression method resulted in models with an average accuracy of 77.27% and 79.54% for the dependent variable of working capital and the cash conversion cycle, respectively. The genetic algorithm method resulted in models with an average accuracy of 89.03% and 82.08%. The final model, with the cash conversion cycle as the dependent variable, was identified as the best model. It includes the variables of the previous year's cash conversion cycle, company-specific risk, gross profit margin, trade credit, growth opportunities, operating cycle, economic policy uncertainty, and exchange rate changes.

This research investigates the relationship between Economic Policy Uncertainty (EPU), credit risk, and lending decisions using the Generalized Method of Moments (GMMs)over the period from2019 to 2023 for12banks listed on the Tehran Stock Exchange. The study employs three regression models to analyze the dynamics of Non-Performing Loans (NPLs), Loan-To-Deposit Ratios(LTDRs), and Return onAssets (ROAs)within the banking sector. Findings reveal a significant persistence in NPLs, indicating that banks with higher past NPLs face ongoing challenges that adversely affect their financial health. A notable negative relationship between Leverage(Lev) and Non-Performing Loan Ratio (NPLR)suggests that more leveraged banks may implement effective risk management strategies, reducing their exposure toNPLs. Additionally, capital adequacy emerges as a critical factor, with higher capital ratios correlating with lower NPLs. The analysis of LTDR indicates thatLevand capital adequacy significantly influence lending practices, while a marginally significant relationship between EPU and LTDR suggests external uncertainties may slightly impact lending decisions. Model results further demonstrate strong persistence in profitability, with historical ROA positively predicting current ROA. Overall, this study underscores the importance of effective risk management practices in banking and highlights ongoing challenges posedbyNPLs, particularly for larger institutions. Recommendations include prioritizing capital buffers and monitoring lending practices to mitigate risks while fostering sustainable profitability growth. Future research should explore additional variables to elucidate the complexities of banking performance metrics

Enterprise risk management (ERM) represents a new paradigm that supports organizations in identifying, evaluating, and managing risks. Several factors encourage different organizations, especially banks, to design and apply ERM; among them, the possibility of financial problems and related costs, financial performance decline, growth opportunities, and independence of the board of directors can be pointed out. In addition, applying a suitable risk management strategy is a competitive advantage for supporting companies. Following the previous comprehensive studies, the present study was done by integrating a meta-composition approach and multivariate fuzzy network analysis. In this research, to identify effective factors on ERM based on empirical evidence and selection of studies, description and classification of selected articles, analysis of the content of selected research articles, and finally, the importance and refinement of the identified factors based on the Delphi expert opinion polling technique, and multivariate fuzzy network analysis was discussed. The study aimed to identify and rank the factors affecting the effectiveness of ERM of the firms accepted in selected banks in Iran. While reviewing studies, a semi-structured interview was conducted using an Exploratory-Descriptive Qualitative (EDQ) research design to determine the factors that affect ERM effectiveness. The interviewed experts were comprised of 20 university professors, CEOs, financial analysts of investment and brokerage companies, and senior auditors with accounting and management education. The literature review and the results of the interviews indicate five main themes that classify the factors affecting ERM effectiveness in firms. In this study, key factors were identified, and then the fuzzy Delphi technique was employed to rank and find the weight of the factors. The results showed that corporate governance, financial indicators, environmental indicators, company characteristics, and management indicators effectively enhance ERM's effectiveness. Accordingly, capital market analysts and investment companies should take a broader perspective and make decisions based on companies' financial risks instead of paying attention to companies' profitability and stock price changes. The results demonstrated that five factors determine ERM effectiveness: 1. corporate governance indicators (monitoring the board of directors and ownership structure), 2. Financial indicators (return on assets (ROA), earnings volatility, merger and acquisition (M&A) activities, financial deficiency, and capital opacity), 3. Environmental indicators (performance excellence, industry competition, audit firm credibility, environmental uncertainty, and industry), 4. Firm characteristics (financial leverage, firm size, and growth opportunities), and 5. Management indicators (management career and business diversity).

Conventional gasoline price can affect the government and society as a strategic commodity in the community. Conventional gasoline price fluctuations have economic, political, social, cultural, and environmental effects. Thus, the prediction of its volatility is essential but there is not any study to examine the price fluctuations. This study aims to hybridize and propose different Garch models based on two distributions and various algorithms in machine learning, such as random forest, ridge regression, Support Vector Regression (SVR), and elastic-net for predicting weekly gasoline price volatility. The results depict Garch and GJRgarch models based on t-student distribution can predict volatility. The combination of ridge regression and GJRgarch model can better predict volatility for the seven-step-ahead. The RMSE scale has been used to compare results that the scale value is 0.01475 in the hybrid method. In fact, combining the ridge regression with t-student-GJRgarch model has the slightest error prediction or the most accuracy among different Garch models and machine learning algorithms