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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.

EXTENDED ABSTRACT INTRODUCTION The number of revealed corruption-related crimes is one of the major challenges of Iran which has been significantly increased in recent years. According to official reports, although only two cases of embezzlement were reported in 1990s and 2000s with a total value up to $ 800 million, administrative corruption has significantly increased in 2010s with 13 large corruption cases and a total value up to $ 14 billion and a growth of over 1500% compared to 1990s and 2000s (The Iranian Students News Agency[1] (ISNA), 2017). The spread of such amount of corruption in administrative and bureaucratic system of the country can have irreparable economic and social consequences. The statistic investigations have shown that macroeconomic indicators including economic growth, employment, unemployment, poverty, inflation and income distribution have become less favorable in 2010 compared to previous decades (Statistical Center of Iran, Iran Statistical Yearbook, 2017). In this regard, the following questions can be raised: is there a relationship between corruption and the indicators of income distribution and poverty in the country? Since the volume of international and unilateral sanctions on Iran has increased in the 2010s, does such a widespread growth in the volume and value of corruption cases in Iran have a relationship with sanctions?   METHODOLOGY Therefore, the purpose of the present study is to investigate the effect of sanctions on causal relationship between corruption, income inequality and poverty in Iran during 1984 to 2020. For this purpose, the indices of per capita income, poverty line, administrative corruption and control of corruption, Atkinson and Gini have been utilized to investigate their interactions through Toda-Yamamoto Causality Test.   FINDINGS Findings of research show that:   sanctions have affected the causality of per capita income on administrative corruption. sanctions have affected the causality of corruption control on per capita income. sanctions have affected the causality of income distribution on per capita income. sanctions had no effect on causality of per capita income, on Atkinson Index and vice versa. sanctions had no effect on causality of per between poverty line, on administrative corruption and vice versa. sanctions had an effect on causality of per between poverty line, on corruption control and vice versa. sanctions had a significant positive effect on poverty line, but had no significant effect on GINI index. In other words, sanctions have affected the causality of income distribution on poverty line. sanctions had no effect on causality of poverty line, on Atkinson Index and vice versa. sanctions had no effect on causality of GINI index, on administrative corruption and vice versa. sanctions had no effect on causality of GINI index, on corruption control and vice versa. sanctions had a significant positive effect on administrative corruption, but had no significant effect on Atkinson index. In other words, sanctions have affected the causality of income distribution on administrative corruption. sanctions had a significant positive effect on Atkinson index, but had no significant effect on corruption control. In other words, sanctions have affected the causality of corruption control on income distribution.     CONCLUSION The purpose of the present study was to investigate the effect of sanctions on causal relationship between corruption, income inequality and poverty in Iran during1984 to 2020. For this purpose, the indices of per capita income p, poverty line, Atkinson, GINI, administrative corruption and corruption control were investigated. In general, the following results were obtained from the present study:   Income distribution is not an effective variable for poverty in Iran. Corruption is an effective variable for causality of poverty in Iran and its significance level is higher under sanctions condition. Corruption and poverty cannot properly explain the income distribution in Iran. However, the corruption control can be the cause of income distribution and poverty line is a proper representative for the cause of income distribution under sanctions conditions. Income distribution is a strong variable for causality of corruption in Iran. Poverty can properly explain the causality of corruption in Iran under sanctions condition, but is not the cause of corruption under normal condition.   According to the obtained results, it seems that sanctions condition is an effective variable for the relationship between variables of income distribution, corruption and poverty. However, the effective factors of income distribution need further investigations in future.  

This study examines the political and economic factors influencing the decision of investors in the stock exchange. For this purpose, first, by using the Meta Synthesis method, the factors affecting investment decisions were extracted from previous researches and then, using Delphi technique, the identified features were evaluated and approved by experts. Then, using DEMATEL method, the relationships between the factors were analyzed, and finally, the sequence and rank of each factor were determined based on the Interpretive Structural Modeling method. Based on the results obtained from the DEMATEL method, "Foreign political news and revolutionaries" and "International economic revolutionaries" are the most influential factor on other factors and "Predicting the country's economic situation in the future" is the most influential factor among other factors. Also, the results of the Interpretive Structural model approach show that Political factors such as the "Comments of political officials", "Iran's political relations with other countries" and "Internal Security and Stability" have the greatest impact on investment decisions.

The stock market is one of the main components of the economy, and various factors cause fluctuations in it, one of which is the effect of investors' behavior. Therefore, present study seeks to answer the question of whether the feelings and sentiments of investors might intensify the fluctuations in the Tehran Stock Exchange or not. To answer this question, at first, in order to quantify sentiments, as non-abstract variables, the Equity Market Sentiment Index (EMSI) was used that investors are classified in 5 categories of completely risk-averse, risk-averse, neutral-risk, risk-taking and completely risk-taking. Using GARCHi-in-Mean, results indicate that the sentiments of investors will result in greater fluctuations in the Tehran Stock Exchange. Hence, if fluctuation is considered an indicator of market risk, the excitement associated with an abnormal rise in volumes will increase that risk.

Portfolio optimization which means choosing the right stocks based on the highest return and lowest risk, is one of the most effective steps in making optimal investment decisions. Deciding which stock is in a better position compared to other stocks and deserves to be selected and placed in one's investment portfolio and how to allocate capital between these stocks, are complex issues. Theoretically, the issue of choosing a portfolio in the case of minimizing risk in the case of fixed returns can be solved by using mathematical formulas and through a quadratic equation; but in practice and in the real world, due to the large number of choices in capital markets, the mathematical approach used to solve this model, requires extensive calculations and planning. Considering that the behavior of the stock market does not follow a linear pattern, the common linear methods cannot be used and useful in describing this behavior. In this research, portfolio optimization using the gray wolf algorithm and the Markowitz model based on CO-GARCH modeling has been investigated. The statistical population of the current research included the information of 698 companies from the companies admitted to the Tehran Stock Exchange for the period of 2011 to 2020. First, the optimal investment model is presented based on the gray wolf algorithm, and After extracting the optimal model, the efficiency of the gray wolf algorithm is compared with the Markowitz model based on CO-GARCH modeling.

As we are looking for knowledge of stock future returns in portfolio optimization, we are practically faced with two principal concepts: Uncertainty and Information about variables. This paper attempts to introduce a pragmatic bi-objective investment model based on uncertainty, instead of probability space and information theory, instead of variance and other moments as a risk measure for portfolio optimization. Not only is uncertainty space expected to be more in line with investment theory, but also, applying and learning this approach seems more straightforward and practical for novice investors. The proposed model simultaneously maximizes the uncertain mean of stock returns and minimizes uncertain entropy as a measure of portfolio risk. The uncertain zigzag distribution has been used for variables to avoid the complexity of fitting distributions for data. This uncertain mean-entropy portfolio optimization (UMEPO) has been solved by three meta-heuristic methods of multi-objective optimization: NSGA-II, MOPS, and MOICA. Finally, it was observed that the optimal portfolio obtained from the proposed model has a higher return and a lower entropy as a risk measure compared to the same model in the probability space.