درخت حوزه‌های تخصصی

The purpose of this study is to evaluate the most effective strategic implica-tions of green accounting based on the function of sustainable reporting. In this study, theoretical screening based on similar studies was used to identify the components (strategic consequence of green accounting) and research propositions (themes of sustainable reporting function). Then, in order to determine the reliability of research components and propositions through the participation of 12 experts and experts in the field of accounting and financial management, Delphi analysis was used. In the quantitative part, the identified components and propositions in the form of matrix questionnaires were evaluated by interpretive analysis by 17 managers of the top 50 companies in 2009. The results showed that the proposition of sustainable responsibility as the most influential theme of the sustainable reporting function causes the effectiveness of the value consequence in green accounting. This result shows that by developing the dimensions of social responsibility in sustainable reporting, the level of inclusive values in the value functions of green accounting is strengthened and builds trust and confidence in the company's performance.

The purpose of this study is the development of IT-consulting in the system of foreign economic activity of the Black Sea region. The relevance of this study is due to the need to address the development of consulting services in the system of foreign economic activity of the agro-industrial complex in the region. It is established that today many companies are interested in reducing the risks associated with running their own business. This has led to a rapid growth of consulting companies. The most popular are IT consulting services, market research, financial consulting and auditing with the largest market share, while environmental consulting is only gaining momentum. Institutional priorities of IT-consulting services in the system of foreign economic development of agro-food production of the region are established. The mechanism of development of the system of regional IT-consulting for stimulation of foreign economic activity of agro-food sphere is offered. The basic elements of development and functioning of the international infrastructure of regulation and self-regulation of IT-consulting services are investigated. It is determined that in the long run the services will be developed on the basis of innovative concepts and management tools to ensure the strategic competitiveness of customers. The consumers served are metallurgy, energy, construction, telecommunications, chemical industry, food industry, trade and distribution, media companies and the financial sector. In general, the consulting industry in Ukraine has some space, and domestic consulting companies also have a significant share of reserves in the market of consulting services. The estimated value of exports of the Black Sea region according to the models in 2022 is projected at the level of 2989.242 million dollars, and in 2023 - at the level of 3146.893 million dollars. It is proved that for further intensification of foreign economic activity of enterprises two main problems should be solved: to overcome excessive dependence on export of plant products (and other products with low added value) and to intensify export capacities of Kherson region.

In this article, we will examine the financial issues related to multi-period routing and positioning and the related costs, and we will examine the related limitations. These decisions are made about location allocation, inventory and routing in a three-tier supply chain, including suppliers, warehouses and customers. We are looking for new ways to make location and routing decisions simultaneously and efficiently. In order to maximize the search space and achieve optimal results, exploratory and meta-heuristic methods have been used. The meta-heuristic technique is usually used to increase the performance of the hybrid technique. Therefore, this paper provides an overview of meta-heuristic methods and their combination to solve problems. It also examines the advantages and disadvantages of the proposed methods to solve these problems in order to provide more efficient methods.

This study examines empirically the effect of equity overvaluation on future stock price crash risk in companies with greater information asymmetry. Using the information asymmetry and crash risk indicators, the question that whether overvalued firms are more prone to future crash risk is investigated. Accordingly, the first purpose of this study is to investigate the relation between overvaluation and the future stock price crash risk. With the increase of information asymmetry between a firm and the market, managers have more abilities and opportunities to withhold bad news and accelerating the release of good news. As a result, it is expected that the information asymmetry between managers and investors increases future stock price crash risk. In so doing, we identify one main hypothesis and three subsidiary ones and the data of 111 listed companies of Tehran Stock Exchange for the period between 2009 and 2017 were analyzed and A panel data approach has been used to test of research hypotheses. According to the results, there is a positive and significant relationship between the overvaluation and the future stock price crash risk of companies which are operating in monopolistic markets and this relation intensified by the information asymmetry.

The purpose of the present study is to use the ensemble learning model to combine the predictions of Random Forest (RF), Long-Short Term Memory (LSTM), and Recurrent Neural Network (RNN) models for the Top and Bottom Prices of Bitcoin. To this aim, in the first stage, Bitcoin's top and bottom prices are predicted using three machine learning models. In the second stage, the outputs of the models are presented as feature variables to the Extreme Gradient Boosting (Xgboost) and Light Gradient Boosting Machine (LightGBM) models to predict the price tops and bottoms. Then, in the third stage, the outputs of the second stage are combined through the voting ensemble classifier pattern to predict the next top and bottom prices. The data of top and bottom Bitcoin prices in the 1-hour time frame from 1/1/2018 to the end of 6/30/2022 are used as target variables and 31 technical analysis indicators as feature variables for the three models in the first stage. 70% of the data is regarded as learning data, and the remaining 30% is considered for the second and third stages. In the second phase, 50% of the data is considered for learning the output of the previous stage and 50% for the test data. Finally, the prediction values are evaluated with real data for the three models and the proposed ensemble learning model. The results reveal the improvement of the performance, precision, and accuracy of the ensemble model compared to weak learning models.

The asymmetry of information between managers and shareholders significantly affects the company's investment decisions and exacerbates the problems of agency. It seems that these problems in developing markets such as Iran are more than developed markets. This research is an exploratory study, and it aims at reviewing the theory of agency in Iran. In the present study, based on the research paradigm, first, data acquisition strategy is selected based on a qualitative approach, and then qualitative study and analysis, quantitative approach, and factor analysis approach have been used to confirm the validity of the extracted model. Statistical analysis of research data shows that among the hypotheses of agency theory, risk aversion, supervision, and reward are revised dimensions in Iran like examples of developing countries.The results confirm the existence of assumptions; 1. Conflict of interests of manager and owner, 2. Information asymmetry, 3. Reverse choice, 4. Ethical risk, 5. Maximizing the expected utility, 6. Forcing and 7. Failure to confirm the assumption is the ability to predict the future and receive information. due to the existence of information asymmetry, higher investment risks of minority shareholders and transparency problems, the lack of ability to predict the future and receive information is necessarily confirmed.

Nowadays, many companies cannot see the digital investment that plays a main role in the IR 4.0. Therefore, this study is investigating the study of investment as plays a critical role in an analytical activity to assess the benefits and costs of an investment and can be used as an investment justification. Traditional investment appraisal uses a financial approach where the benefits and costs are quantified in a certain amount of value for money and then compared in value. Moreover, this study is revealed the fruitful outcomes because revealed the investment valuation method with NPV (Net Present Value) and ROV (Real Option Valuation). ROV is an alternative to financial valuation. Seeding from the same philosophy as Financial Option, ROV has advantages in handling the flexibility, risk, and volatility that may occur from an investment. Thus, ROV is considered more able to appreciate an investment that has these characteristics. Investment appraisal with ROV is better able to appreciate investment than traditional financial methods, as shown by ROV's NPV results in the case of marketing with Loyalty points through email communication as a digital investment that are greater than ordinary NPV. This is because ROV can appreciate flexibility in investments that have choices of investment plans in the future

What is clear is that judges usually judge cases based on their knowledge, experience, personality, and sentiment. Due to high pressures and stress, it may be difficult for them to carefully examine documents and evidence, which leads to more subjective judgments. Legal judgment prediction with artificial intelligence algorithms can benefit judicial bodies, legal experts, and litigants as well as judges. In this research, we are looking at predicting legal sentences in drug cases involving the purchase, possession, concealment, or transportation of illicit drugs, using machine learning methods, and the effect of sentiment and emotions in case texts on predicting the severity of whipping, fines, and imprisonment. So, the text documents of 6000 Persian drug-related cases were pre-processed and then the translation of the NRC Glossary of Emotions and sentiment was used to give each item a score for positive or negative sentiment and a score for emotion. Then machine learning methods were used for modeling. BERT, TFIDF+Adaboost, and Skipgram+LSTM+CNN methods had the highest accuracy, respectively. Also, evaluation criteria were analyzed in situations where sentiment scores, emotional scores, or both were used in the prediction process along with judicial texts. Finally, it was found that the use of sentiment and emotion scores improves the accuracy of legal judgment predictions for all three types of sentences and that sentiments have a greater impact on the accuracy of legal judgment predictions than emotions

Sports tourism is one of the most attractive types of tourism that occurs in different regions and countries. This  tourism type is effective in economic fields and extremely significant in cultural and sportsmanship contexts.  To this end, the current research aimed to identify and explain the effective components of sport tourism in the  sublimity of culture and sportsmanship, conducted through a mixed method. This research approach was  sequential exploratory in two qualitative-quantitative phases, in which “thematic analysis” and “survey”  methods were used, respectively. The first phase’s statistical population included experts in sport tourism, sport  management, culture management, and sportsmanship, with 14 individuals participating in semi-structured  interviews using a purposive sampling method. After determining the effective actors in this field, 32 experts  responded to questionnaires using a systematic sampling method in the study’s second phase. According to the  research findings of the first phase, the themes extracted from the semi-structured interviews comprised 39  fundamental themes, 15 organizing themes, and four global themes. Per the second phase of the study’s  findings, all themes were approved by the experts, all determinants of the role of sport tourism in the sublimity  of sports culture and sportsmanship. As a result, sports tourism, while creating macroeconomic benefits for  residents and visitors, significantly contributes to the sublimity of sports culture and sportsmanship, where this  category can strengthen the value and ethical attitudes in addition to creating recreational experiences