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The current research was conducted with the aim of providing a conceptual framework for the development of relations with suppliers in Abadan Oil Refining Company. The research method is qualitative and based on the data theory of the foundation. To collect data, semi-structured interview was used and data analysis was done by Strauss and Corbin method and paradigm model. The statistical population was experts and managers with experience in Abadan Oil Refining Company, from which a sample of 21 people was selected after reaching theoretical saturation. Sampling has been done theoretically and using targeted (judgmental) techniques. The results of the analysis of the data obtained from the interviews, during the process of open, central and selective coding, led to the creation of models of the development model of the relationship with the suppliers of consumer products in the Abadan Oil Refining Company based on the data theory of the foundation, which ultimately provided a model for the development model. Communication with suppliers was identified. The findings of this research indicate that for the success of the development model of the relationship with the suppliers of consumer products, company managers can pay attention to the identified categories and subcategories and if they match the conditions of the company, from this category. use In addition, this research shows the need to pay attention to the development patterns of relationships with suppliers.

In the multifaceted landscape of entrepreneurship, this research delves into the intricate relationship between entrepreneurial orientation and entrepreneurial intention, focusing specifically on the final-year female undergraduates in Nigerian federal universities. Grounded in the Human Capital Theory (HCT), this study uncovers profound insights into the multifaceted relationships between various entrepreneurial traits and their influence on entrepreneurial intentions. The findings reveal a nuanced relationship between traits such as autonomy, competitive aggressiveness, risk-taking, self-esteem, and the sense of inclusion with entrepreneurial intentions. Autonomy and competitive aggressiveness emerge as significant influencers, while risk-taking displays a non-significant association. Self-esteem holds profound implications, while the sense of inclusion exerts a more subdued impact on entrepreneurial intentions. Theoretical implications challenge and expand existing frameworks, notably HCT. While HCT posits that investments in education and training foster entrepreneurial traits, this study suggests a more intricate relationship. The non-significant association of certain traits, despite their emphasis in HCT, underscores the need for a nuanced understanding, possibly integrating other theoretical frameworks. From a practical standpoint, these findings guide policymakers and educators in designing targeted interventions. Specialized training programs, workshops, and courses emphasizing autonomy, competitive aggressiveness, and self-esteem can be developed. Integrating competitive elements, like business plan competitions, can nurture students' competitive drive. Policy recommendations include tailored entrepreneurial education, fostering inclusive environments, promoting a risk-taking culture, supporting longitudinal studies, and enhancing collaboration between educational institutions and industry.

Industrial investment in Iran, especially in the wake of sanctions’ intensification in the 2010s, decreased significantly. The government implemented fiscal policy which has been associated with uncertainty. In this paper, using a dynamic panel model and generalized method of moments, we examined the effect of fiscal policy uncertainty on investment in 24 industrial groups in Iran over the period 2002-2020. The results indicated that the growth of fiscal level shock,resulted in a low but positive effect on investment directly. However,the coefficient of cross effect between the growth of fiscal level shock and the fiscal policy uncertainty undermined the positive effect on industrial investment. The government has had two effects on industrial investment indirectly through the demand side shock. By creating demand, it has had a positive effect on industrial investment with the cross effect between the growth of the fiscal level shock and industrial sales. However, on the other hand, the growth of the fiscal level shock has been associated with the creation and growth of fiscal policy uncertainty, so that the cross effect between the growth of fiscal policy uncertainty and the growth of industrial sales on industrial investment shows a high negative coefficient. The results of these two effects suggest that due to high uncertainty,the indirect government effect on investment in 24 industrial groups is negative, which happens through the demand-side shock. Moreover, the growth of fiscal policy uncertainty, sanctions, and interest rates, respectively, have had the most adverse effect on investment in 24 industrial groups in Iran.

This paper addresses the absence of a suitable criterion for measuring information asymmetry between managers forecasting earnings and analysts forecasting earnings through statistical methods. Besides, this paper aims to provide a model of information asymmetry, emphasizing the role of financial and managerial criteria. This is applied qualitative and quantitative research (mixed method). The library method is used to prepare and formulate theoretical bases. In addition, the field method is used for collecting data to measure and identify indices and modeling. Factor analysis was used to analyze the data, following identifying the dimensions and variables of financial and managerial criteria of information symmetry to eliminate extraneous factors and classify. The following five main dimensions were determined, including corporate profit forecast, corporate governance, capital market, capital return, and management characteristics of the company. Then, the modeling was done using fuzzy mathematics through triangular numbers, Mamdani implication, and center of gravity methods. The final results of the study of the company listed on the Tehran Stock Exchange show that the level of information symmetry in the range of zero to 100 equals 55.1, to predict the company's profit is 48.54; corporate governance is 56.95; the capital market is 1/59; capital return is 61.07, and managerial characteristics of the company are 67.84. Finally, we examined the factors affecting the information asymmetry obtained from fuzzy neural networks. The findings show a higher prediction accuracy of fuzzy neural network methods than other related prediction methods.

This research investigated the development of a stock trading strategy for companies on the New York Stock Exchange (NYSE), a prominent global market. Data was acquired from established libraries and the Yahoo Finance database. The model employed technical analysis indicators and oscillators as input features. Machine learning classification algorithms were used to design trading strategies, and the optimal model was identified based on statistical performance metrics. Accuracy, recall, and F-measure were utilized to evaluate the classification algorithms. Additionally, advanced statistical methods and various software tools were implemented, including Python, Spyder, SPSS, and Excel. The Kruskal-Wallis test was employed to assess the statistical differences between the designed strategies. A sample of 41 actively traded NYSE companies across diverse sectors such as financial services, healthcare, technology, communication services, consumer cyclicals, consumer staples, and energy were chosen using a filter-based approach on June 28th, 2021. The selection criteria included a market capitalization exceeding $200 billion and an average daily trading volume surpassing 1 million shares. Evaluation metrics revealed that the designed random forest trading strategy achieved a good fit with the data and exhibited statistically significant differences from other strategies based on classification learning algorithm.

In this research, due to the importance of investing and especially investing in the stock market, we predicted the stock price return on the stock exchange through the Auto-Regressive Integrated Moving Average (ARIMA) and Recursive Neural Network (RNN) of long-short term memory (LSTM). Then, to reduce the risk of decision-making, we compared the predictive power of these two models to determine a better model. The research variable is the stock price of the top 20 (in market cap) companies on the stock exchange for the period of the 11th Feb 2015 to 22th Jan 2022. We considered the data of the last 10 days as experimental data and the previous data as educational data. Initially, we calculated the mean and standard deviation of the prediction error of both models; these criteria had less value for the LSTM recursive neural network model than the ARIMA model. To measure the significance of this difference in predictive power, we used Harvey, Liborne, and New Bold tests. The results showed that in predicting the stock prices of the top 20 companies of the stock exchange, the predictive power of the LSTM recursive neural network model was statistically and significantly higher than the ARIMA model which means better predition of stock prices and higher return for investors. In the end, it is believed that the LSTM model may have the best predictive ability, but it is greatly affected by the data processing.