This study was an attempt to examine the using intelligent systems to manage risks and reduce financial risks using artificial intelligence in large companies. The data collected from the data is collected from the stock organization and the stock Securities of Iran. Moreover, the data is collected from 17 companies for ten years and the data was collected through the variance formula and then the results were examined using the SSPS method. Variance formula is σ^2 = (∑(x_i − μ^2))/n. The data is completely obtained from a reliable and correct source, which is related to the Department of Statistics of Iran’s Stocks and Securities. After categorizing and classifying the collected information and data, it is processed and analyzed, and the results and findings of this study are stated. In order to examine the research questions, appropriate tests (Shefe tests) were used to analyze the data. The analysis of data revealed that The findings of this study reveal that Shatran, Arya, Shebandar, Famili are profitable among other stocks. Other stocks are loss because of high cost of raw materials and sanction of Iran. Moreover, companies could not export their products. Plus, the purchase power of the people in the country decreases because of the inflation of the country, so the companies could not buy anything. On the one hand, liquidity power of people, on the other hand, less export lead to the decrease of cash flow, liquidity power of companies and firms and could not sell their products. It can be concluded that those stocks are loss and have high risk.
This study examines and reports on using intelligent systems to manage risks and reduce financial risks using artificial intelligence in large companies.
Taking advantage of all internal and environmental data and analyzing a huge amount of data on the web in real-time shows the growth or decline of the organization in terms of access to the target indicators in the program or budget. Every moment of the situation has the influence of the management on the indicators, but the traditional monitoring is only yearly through limited indicators that provide a non-analytical report. In terms of power, intelligent systems analysis analyzes more than several million terabytes of data per hundredth of a second. We don’t even have an analysis of 100 megabytes of data, and the accuracy and quality of the data analysis are poor, while in traditional monitoring, no intelligent systems and manual systems are comparable. (Ghasemi, A., 2022)
Different types of risks threaten financial and credit institutions. Therefore, managers of organizations should identify and manage existing risks. Financial risk is a risk that directly affects the profitability of financial and credit institutions. Financial risks include balance sheet structure risks, income and profitability structure, capital adequacy, credit risk, liquidity risk, interest rate risk, market risk, and exchange rate risks. This research has evaluated and presented a suitable model for identifying, measuring, and controlling financial risks in financial and credit institutions. The current research is divided according to the execution method, with the correlation method, and based on post-event data. The research data was selected by non-probability and purposeful sampling methods and its volume was selected based on the judgment of the researcher. These data were extracted from the management of 39 regions of Mellat Bank daily during the years 2005 to 2007. Investigations using the structural equation model have shown that the effect of facilities and investment on liquidity risk, the effect of variable currency assets and liabilities on currency risk, and the effect of the variable effect of interest-sensitive assets and liabilities on interest risk are significant. (Khodaei, M. & Taghavei, M., 2010)
Risk management using artificial intelligence means using algorithms, models, and techniques of artificial intelligence to identify, evaluate, and reduce financial risks. This method, using data analysis and intelligent algorithms, helps organizations to prevent financial risks in a preventive manner and react quickly in case of risk. In addition, by using artificial intelligence in risk management, suspicious transactions can be automatically identified and blocked in real-time. This method reduces financial risks, improves the performance of the organization, and increases the trust of customers and shareholders in the organization.
Drawing on the definition coined by the Financial Stability Board (FSB), 2 artificial intelligence is the set of theories and algorithms that allow computer systems to perform tasks that typically require human intelligence (e.g. visual perception, voice recognition, or interpretation of a text taking into account its context) and that in some cases augment these skills. Most current artificial intelligence applications belong to the field of machine learning. This consists of a computer concluding a statistical analysis of data, in a process that improves automatically as more and more information is fed into the algorithm. Artificial intelligence is not a new development; indeed the first academic studies date back to the 1950s. However, it has gained popularity recently, owing mainly to three factors: the growing volume of digital data available; increased data storage and computational processing capacity and its lower cost; and the progress made in the algorithms used. Thanks to these changes, better use can now be made of the capacities of artificial intelligence, and in consequence, its use is growing significantly, not only in the financial sector but also in the economy overall. However, this level of development differs between regions. According to figures published by the European Commission in December 2018, 3, private investment in artificial intelligence amounts to more than ?.5 billion in Asia and more than ?2 billion in North America, whereas in Europe it is less than ?.5 billion. This is partly due to the privileged position enjoyed by the Asian and US technology giants for developing artificial intelligence tools, as they have both the infrastructure and the necessary data at their disposal. Accordingly, these corporations make intensive internal use of this technology and have also become leading global suppliers of artificial intelligence applications. (Fernandez, A., 2019)
According to Fernandez the use of artificial intelligence tools has escalated recently in all sectors of the economy owing, among other factors, to the growing volume of digital data and higher computational capacity. Major benefits may be reaped from applying these tools to the provision of financial services, not only for financial institutions but also for society as a whole. This article described some of those benefits, and also some of the main uses being made of these tools, both by financial institutions and central banks. It also indicated the main limitations of the technology and its possible implications for the corrected functioning of the financial system. (Fernandez, A., 2019)
Melnychenko noted this study contributed to the literature on financial security by highlighting the relevance of the perceptions and resulting professional judgment of stakeholders. Assessing a company’s financial security using only economic indicators—as suggested in the existing literature—would be inaccurate when undertaking a comprehensive study of financial security. Specifically, indices and indicators based on financial or managerial reporting calculated at any particular point in time, provide only a superficial understanding—and may even distort the overall picture. It has also been suggested that expert assessment is the most objective method, although it has disadvantages related to individual cognitive limitations. These limitations are not particular to artificial intelligence, which could assess an enterprise’s financial security in a less biased way. However, by only imitating human behavior, it is not able to perceive and evaluate with intuition the dynamics of the company’s development and holistically assess the financial condition—despite the possibility of learning and forecasting—because artificial intelligence is not able to think and predict, which, in an enterprise, is the most important skill of a manager. Therefore, the risk of developing artificial intelligence to assess a firm’s financial security lies in a biased assessment of the enterprise’s activities in general—and its financial security in particular. (Melnychenko, O., 2020)
Mashur and the authors examine that financial risk management avoids losses and maximizes profits, which is vital to most businesses. As the task relies heavily on information-driven decision-making, machine learning is a promising source for new methods and technologies. In recent years, we have seen increasing adoption of machine learning methods for various risk management tasks. Machine-learning researchers, however, often struggle to navigate the vast and complex domain knowledge and the fast-evolving literature. This paper fills this gap, by providing a systematic survey of the rapidly growing literature on machine learning research for financial risk management. The contributions of the paper are four-fold: First, we present taxonomy of financial-risk-management tasks and connect them with relevant machine learning methods. Secondly, we highlight significant publications in the past decade. Thirdly, we identify major challenges being faced by researchers in this area. And finally, we point out emerging trends and promising research directions. (Luo, W., Mashur, A., Nayyar, A., & Robles-Kelly, A., 2020)
Siddiqui analyzed that today’s dynamic financial landscape, effective risk management is crucial for ensuring the stability and success of financial institutions. With the rapid advancement of technology, Artificial Intelligence (AI) has emerged as a powerful tool that can revolutionize financial risk management practices. This article explores the opportunities and challenges associated with leveraging AI in financial risk management. By focusing on finance professionals and risk managers, the researchers delved into the practical applications of AI, its potential benefits, and the key considerations to bear in mind when adopting AI-driven solutions for risk management. (Siddiqui, W., 2023)
Mahalakshmi and the author investigated Artificial intelligence (AI) and Machine learning (ML) have provided the financial industry as a whole a method to address customer expectations for more innovative, more convenient, and safer ways to access, expand, employ, save, and invest their funds. In addition, AI assists the financial industry in streamlining and improving procedures from investing decisions to quantifiable trading and financial risk management.
As a result, Automated-based solutions are expected to emerge as a game-changer with significant implications for increasing financial access to the people. AI and ML help managers emphasize the business’s key and strategic aspects and spend less time on repetitive tasks. Most essential operations in finance, such as risk assessment, stock trading, and credit lending to loan applicants, are being taken over by AI. Further, AI is a robust technology that is widely used in the financial services industry. ML and AI have a lot of potential to make a positive difference in the financial services industry. They revolutionize financial services and the way they are delivered to clientele. It could lead to better-informed and tailored products and services and enhanced internal procedure productivity, cybersecurity, and risk lessening. The faster response aided by AI delivers higher customer satisfaction. Moreover, AI and ML transform the means financial institutions generate and use data insights, resulting in new kinds of business model innovation, reforming competitive ecosystems and personnel, presenting new risk dynamics, and posing new challenges to businesses and policymakers.
This research paper focuses on qualitative research, primarily exploring how AI and machine learning have been deployed in various financial service industries and their impact on employees, finance professionals, and corporate organizations to create competitive intelligence. (Mahalakshmi, V., Kulkarni, N., Pradeep Kumar, K. V., Suresh Kumar, K., Nidhi Sree, D., & Durga, S., 2022)
According to Ehtesham Rasi and the authors wanted to investigate the rating of the actual customers of banks based on credit risk using multiple criteria decision-making and artificial intelligence hyperbolic regression. This is an applied research. The statistical population of the study includes the credit customers of Agriculture Bank in the western branches of Mazandaran province, Iran from 2012 to 2016. A total of 100 cases have been evaluated. The AHP method has been used in the case of elites’ comments on the prioritized seven key factors using the corresponding weighting matrix. Artificial intelligence hyperbolic regression has been employed to model the classes of creditworthy and non-creditworthy customers and to predict the appropriate model based on the evidence in the customer’s credit file. Using the AHP method, the rating includes customer revenue, credit in the market, customers’ jobs, the duration of the relationship with the bank, the type of collateral, the value of collateral, and the average account balance to facilitate the credit risk of the actual customers, respectively. Using artificial intelligence hyperbolic regression, prioritization is based on the amount of credit in the market, customer revenue, the value of collateral, the duration of the relationship with the bank, the type of collateral, and the customers’ jobs. (Arad, M., Ehtesham Rasi, R., & Karamipour, M., 2020)
The book was written by Metawa and Kabir Hassan presents a collection of high-quality contributions to state-of-the-art Artificial Intelligence and Big Data analysis related to financial risk management applications. In one place, it brings together the latest thinking on an emerging topic and includes principles, reviews, examples, and research directions. The book presents numerous specific use cases throughout, showing practical applications of the concepts discussed. It looks at technologies such as eye movement analysis, data mining, or mobile apps and examines how these technologies are applied by financial institutions, and how this affects both the institutions and the market. This work introduces students and aspiring practitioners to the subject of risk management in a structured manner. It is primarily aimed at researchers and students in finance and intelligent big data applications, such as intelligent information systems, smart economics and finance applications, and the Internet of Things in a marketing environment. (Kabir Hassan, M., Metawa, N., & Metawa, S., 2022)
Based on Božić AI has the potential to revolutionize risk management across various industries, including finance, healthcare, cyber security, and others. By enabling faster and more accurate risk assessments, identifying emerging risks, and supporting predictive modeling, AI can help organizations better manage risks and make more informed decisions.
However, it is important to note that AI is not a silver bullet solution and comes with its own set of risks and challenges, including bias, lack of transparency, and security concerns. To ensure the safe and effective use of AI in risk management, it is crucial to address these issues by implementing best practices such as ethical AI development, rigorous data management, and ongoing monitoring and evaluation. The use of AI in risk management is a promising area with the potential to transform how organizations approach risk management and make critical decisions. As technology continues to evolve, it will be important for organizations to stay up-to-date on the latest developments and best practices in AI to ensure the safe and effective use of this technology in risk management. (Božić, V., 2023)
Kumar and the authors studied looks at how various forms of AI can manage financial risk management. The impetus for this change is the revolutionary impact that financial technology has had on business operations, necessitating a complete overhaul of the financial sector. Financial risk management must be reorganized because conventional approaches have become inefficient. Artificial intelligence methods are primarily practical and have aided in the quick, cheap, and effective management of financial risks in businesses and financial institutions. This paper aims to provide an overview of the current state of artificial intelligence (AI) techniques applied to the field of financial risk management and to indicate potential future directions for research and development in this area. The data for the study was gathered by reading an assortment of articles, books, and reports regarding the implementation of AI in financial risk management. Methods: The question is whether artificial intelligence (AI) techniques (particularly machine learning) might help manage financial risks by systematically reviewing the relevant literature. Conclusions: Model validation, risk modeling, stress testing, and data preparation are all areas where AI has significantly benefited market risk and credit risk management. Data quality control, fraud detection, and text mining for data augmentation are all areas in which (AI) artificial intelligence techniques have proven useful. The financial sector will continue to be influenced by financial technology as incumbents are compelled to adopt new operational methods and strategies. Consequently, it is realistic to anticipate that AI will become a mainstream component of financial risk management systems. The paper’s contribution is a survey of AI’s uses in three fields: financial (market and credit), risk management, and operational (business continuity and disaster recovery). The paper went over the most promising AI methods that should impede better managing risks in the changing financial sector. (Kumar, A., Kumar, A., Kumari, S., Kumari, N., Kumari, S., Mishra, P., & Kumar Behura, A., 2023)
Rostami and Khalili Tirtashi posed the point of view of Islam and the Islamic economy; it is accepted to use knowledge and what is known today as technology and artificial intelligence in financial systems. The use of artificial intelligence in financial systems has various functions. Reducing risk, making more realistic decisions, and creating a platform to combat money laundering and phishing are among the most important applications of artificial intelligence in financial systems, which are considered from the perspective of Islam. The Islamic economy aims for greater predictability and reducing risk in financial systems, and on the other hand, money laundering is not compatible with the foundations of the Islamic economy. Conclusion: According to the approach of the Islamic economy in using knowledge information and communication technology, it is necessary to provide platforms for the use of artificial intelligence in the Islamic financial system. (Khalili Tirtashi, N., & Rostami, A., 2023)
The data is collected from the stock organization and the stock Securities of Iran. Moreover, the data is collected from 17 companies for ten years and the data was collected through the variance formula and then the results were examined using the SSPS method. Variance formula is σ^2 = (∑(x_i − μ^2))/n. The data is completely obtained from a reliable and correct source, which is related to the Department of Statistics of Iran’s Stocks and Securities.
In order to answer the question to what are the effects of artificial intelligence (AI) on the financial performance of 17 Iranian companies and strategies for improving risk and capital management using artificial intelligence? The results of this study shows as follows:
First, the variance of each of the submitted data was calculated and then the obtained data was considered as profit risk in different years. After categorizing and classifying the collected information and data, it is processed and analyzed, and the results and findings of this study are stated. In order to examine the research questions, appropriate tests were used to analyze the data.
Hypothesis 1: profit risk in four consecutive years (2020-2023) in Shabandar shares is loss.
H0: ρ ≠ 0
Opposite hypothesis: profit risk in four consecutive years (2020-2023) in Shabandar shares is profitable.
H0: ρ = 0
According to the
| Mount of mandate | Mount of F | Sig test benefit | |
|---|---|---|---|
| Shefe Test | 3 | 3.21 | .037 |
| Interpret | Number | Average | |
|---|---|---|---|
| Variance 2020 | 6 | .043 | A |
| Variance 2021 | 9 | .011 | AB |
| Variance 2022 | 11 | .002 | B |
| Variance 2023 | 9 | .003 | AB |
Non-identical letters indicate significant differences between the tested years
Hypothesis 2: Profit risk in four consecutive years (2020-2023) is not significant in Vmelat stocks.
H0: ρ ≠ 0
Opposite hypothesis: profit risk in four consecutive years (2020-2023) in Vmelat shares is profitable.
H0: ρ = 0
According to the
The same letters indicate that there is no significant difference between the tested years.
Hypothesis 3: profit risk in four consecutive years (2020-2023) in Famli stocks is loss.
H0: ρ ≠ 0
Opposite hypothesis: profit risk in four consecutive years (2020-2023) is significant in Famli stocks.
H0: ρ = 0
According to the
Non-identical letters indicate significant differences between the tested years.
Hypothesis 4: profit risk in four consecutive years (2020-2023) in Fakhooz shares is disadvantageous.
H0: ρ ≠ 0
Opposite hypothesis: profit risk in four consecutive years (2020-2023) in Fakhooz shares is profitable.
| Mount of mandate | Mount of F | Sig test benefit | |
|---|---|---|---|
| Shefe Test | 3 | .53 | .66 |
| Interpret | Number | Average | |
|---|---|---|---|
| Variance 2020 | 6 | .003 | A |
| Variance 2021 | 9 | .001 | A |
| Variance 2022 | 11 | .002 | A |
| Variance 2023 | 9 | .002 | A |
| Mount of mandate | Mount of F | Sig test benefit | |
|---|---|---|---|
| Shefe Test | 3 | 3.15 | .04 |
| Interpret | Number | Average | |
|---|---|---|---|
| Variance 2020 | 6 | .028 | A |
| Variance 2021 | 9 | .002 | AB |
| Variance 2022 | 11 | .001 | B |
| Variance 2023 | 9 | .008 | AB |
H0: ρ = 0
According to the
Different letters indicate the difference in profit between the years under test.
Hypothesis 5: Profit risk in four consecutive years (2020-2023) in Arya shares is not profitable.
H0: ρ ≠ 0
Opposite hypothesis: profit risk in four consecutive years (2020-2023) in Arya shares is over profit.
H0: ρ = 0
According to the
| Mount of mandate | Mount of F | Sig test benefit | |
|---|---|---|---|
| Shefe Test | 3 | .66 | .58 |
| Interpret | Number | Average | |
|---|---|---|---|
| Variance 2020 | 6 | .012 | A |
| Variance 2021 | 9 | .005 | A |
| Variance 2022 | 11 | .002 | A |
| Variance 2023 | 9 | .011 | A |
| Mount of mandate | Mount of F | Sig test benefit | |
|---|---|---|---|
| Shefe Test | 3 | 1.49 | .24 |
| Interpret | Number | Average | |
|---|---|---|---|
| Variance 2020 | 6 | .013 | A |
| Variance 2021 | 9 | .009 | A |
| Variance 2022 | 11 | .001 | A |
| Variance 2023 | 9 | .002 | A |
Different letters indicate the difference in profit between the years under test.
Hypothesis 6: Profit risk in four consecutive years (2020-2023) in stocks and Vamaaden is loss.
H0: ρ ≠ 0
Opposite hypothesis: profit risk in four consecutive years (2020-2023) in stocks and Vamaaden is profitable.
H0: ρ = 0
According to the
Non-similar letters indicate profitable differences between the years tested.
Hypothesis 7: Profit risk in four consecutive years (2020-2023) in Shapdis shares is disadvantageous.
H0: ρ ≠ 0
Opposite hypothesis: profit risk in four consecutive years (2020-2023) in Shapdis shares is profitable.
H0: ρ = 0
| Mount of mandate | Mount of F | Sig test benefit | |
|---|---|---|---|
| Shefe Test | 3 | .85 | .48 |
| Interpret | Number | Average | |
|---|---|---|---|
| Variance 2020 | 6 | .018 | A |
| Variance 2021 | 9 | .025 | A |
| Variance 2022 | 11 | .001 | A |
| Variance 2023 | 9 | .004 | A |
According to the
Non-similar letters indicate profitable differences between the years tested.
Hypothesis 8: Profit risk in four consecutive years (2020-2023) in Tapiko shares is disadvantageous.
H0: ρ ≠ 0
Opposite hypothesis: profit risk is profitable in four consecutive years (2020-2023) in Tapiko shares.
H0: ρ = 0
According to the
Non-similar letters indicate the difference in profitability between the years under test.
Hypothesis 9: The profit risk in four consecutive years (2020-2023) in Tipico shares is disadvantageous.
H0: ρ ≠ 0
Opposite hypothesis: profit risk in four consecutive years (2020-2023) in typical profitable stocks.
H0: ρ = 0
According to the
| Mount of mandate | Mount of F | Sig test benefit | |
|---|---|---|---|
| Shefe Test | 3 | .97 | .42 |
| Interpret | Number | Average | |
|---|---|---|---|
| Variance 2020 | 6 | .020 | A |
| Variance 2021 | 9 | .022 | A |
| Variance 2022 | 11 | .001 | A |
| Variance 2023 | 9 | .002 | A |
| Mount of mandate | Mount of F | Sig test benefit | |
|---|---|---|---|
| Shefe Test | 3 | .75 | .53 |
| Interpret | Number | Average | |
|---|---|---|---|
| Variance 2020 | 6 | .027 | A |
| Variance 2021 | 9 | .024 | A |
| Variance 2022 | 11 | .003 | A |
| Variance 2023 | 9 | .006 | A |
| Mount of mandate | Mount of F | Sig test benefit | |
|---|---|---|---|
| Shefe Test | 3 | .84 | .49 |
| Interpret | Number | Average | |
|---|---|---|---|
| Variance 2020 | 6 | .006 | A |
| Variance 2021 | 9 | .036 | A |
| Variance 2022 | 11 | .003 | A |
| Variance 2023 | 9 | .001 | A |
Non-similar letters indicate profitable differences between the years tested.
Hypothesis 10: profit risk in four consecutive years (2020-2023) in Khodro stocks is loss.
H0: ρ ≠ 0
Opposite hypothesis: profit risk is profitable in four consecutive years (2020-2023) in Khodro stocks.
H0: ρ = 0
According to the
Non-similar letters indicate profitable differences between the years tested.
Similar letters indicate no profitable difference.
Hypothesis 11: Profit risk in four consecutive years (2020-2023) in Fars shares is unprofitable.
H0: ρ ≠ 0
Opposite hypothesis: profit risk in four consecutive years (2020-2023) in Fars shares is profitable.
H0: ρ = 0
According to the
| Mount of mandate | Mount of F | Sig test benefit | |
|---|---|---|---|
| Shefe Test | 3 | 2.86 | .053 |
| Interpret | Number | Average | |
|---|---|---|---|
| Variance 2020 | 6 | .046 | A |
| Variance 2021 | 9 | .003 | A |
| Variance 2022 | 11 | .001 | A |
| Variance 2023 | 9 | .002 | A |
| Mount of mandate | Mount of F | Sig test benefit | |
|---|---|---|---|
| Shefe Test | 3 | 1.59 | .21 |
| Interpret | Number | Average | |
|---|---|---|---|
| Variance 2020 | 6 | .021 | A |
| Variance 2021 | 9 | .012 | A |
| Variance 2022 | 11 | .002 | A |
| Variance 2023 | 9 | .002 | A |
Different letters indicate the difference in profit between the years under test.
Similar letters indicate no profitable difference.
Hypothesis 12: profit risk in four consecutive years (2020-2023) in Kachad stocks is disadvantageous.
H0: ρ ≠ 0
Opposite hypothesis: profit risk in four consecutive years (2020-2023) in Kachad stocks is profitable.
H0: ρ = 0
According to the
Non-similar letters indicate the difference in profitability between the years under test.
Similar letters indicate no profitable difference.
Hypothesis 13: Profit in four consecutive years (2020-2023) in Parsan shares is unprofitable.
H0: ρ ≠ 0
Opposite hypothesis: profit in four consecutive years (2020-2023) in Parsan shares is profitable.
H0: ρ = 0
According to the
Non-similar letters indicate profitable differences between the years tested.
Similar letters indicate no profitable difference.
| Mount of mandate | Mount of F | Sig test benefit | |
|---|---|---|---|
| Shefe Test | 3 | .72 | .55 |
| Interpret | Number | Average | |
|---|---|---|---|
| Variance 2020 | 6 | .008 | A |
| Variance 2021 | 9 | .021 | A |
| Variance 2022 | 11 | .002 | A |
| Variance 2023 | 9 | .003 | A |
| Mount of mandate | Mount of F | Sig test benefit | |
|---|---|---|---|
| Shefe Test | 3 | 2.04 | .13 |
| Interpret | Number | Average | |
|---|---|---|---|
| Variance 2020 | 6 | .030 | A |
| Variance 2021 | 9 | .012 | A |
| Variance 2022 | 11 | .001 | A |
| Variance 2023 | 9 | .001 | A |
Hypothesis 14: Profit risk in four consecutive years (2020-2023) in V0mid shares is disadvantageous.
H0: ρ ≠ 0
Opposite hypothesis: profit risk in four consecutive years (2020-2023) in v0mid shares is disadvantageous.
H0: ρ = 0
According to the
Non-similar letters indicate profitable differences between the years tested.
Similar letters indicate no profitable difference.
Hypothesis 15: profit risk in four consecutive years (2020-2023) in Shotoran stocks is disadvantageous.
H0: ρ ≠ 0
Opposite hypothesis: profit risk is profitable in four consecutive years (2020-2023) in Shotoran stocks.
H0: ρ = 0
According to the
Non-similar letters indicate profitable differences between the years tested.
Hypothesis 16: The profit risk in four consecutive years (2020-2023) in the shares of Vaparsan is disadvantageous.
H0: ρ ≠ 0
Opposite hypothesis: profit risk in four consecutive years (2020-2023) in Vaparsan stocks is profitable.
H0: ρ = 0
| Mount of mandate | Mount of F | Sig test benefit | |
|---|---|---|---|
| Shefe Test | 3 | 0.89 | .45 |
| Interpret | Number | Average | |
|---|---|---|---|
| Variance 2020 | 6 | .024 | A |
| Variance 2021 | 9 | .041 | A |
| Variance 2022 | 11 | .001 | A |
| Variance 2023 | 9 | .003 | A |
| Mount of mandate | Mount of F | Sig test benefit | |
|---|---|---|---|
| Shefe Test | 3 | 4.88 | .008 |
| Interpret | Number | Average | |
|---|---|---|---|
| Variance 2020 | 6 | .026 | A |
| Variance 2021 | 9 | .002 | A |
| Variance 2022 | 11 | .001 | A |
| Variance 2023 | 9 | .001 | A |
According to the
Non-similar letters indicate profitable differences between the years tested.
Similar letters indicate no profitable difference.
Hypothesis 17: Profit risk is not different in different stocks during 2020-2023
H0: ρ ≠ 0
Counter hypothesis: Profits in different stocks are not different during 2020-2023
H0: ρ = 0
According to the
Non-similar letters indicate profitable differences between stocks. (
| Mount of mandate | Mount of F | Sig test benefit | |
|---|---|---|---|
| Shefe Test | 3 | 1.21 | .32 |
| Interpret | Number | Average | |
|---|---|---|---|
| Variance 2020 | 6 | .035 | A |
| Variance 2021 | 9 | .015 | A |
| Variance 2022 | 11 | .001 | A |
| Variance 2023 | 9 | .003 | A |
| Mount of mandate | Mount of F | Sig test benefit | |
|---|---|---|---|
| One-way ANOVA test | 16 | 3.20 | .00 |
| interpretation | Number | average | ||
|---|---|---|---|---|
| 1 | shabandar | 36 | .012 | A |
| 2 | vmelat | 36 | .002 | ABCD |
| 3 | famli | 36 | .007 | ABC |
| 4 | fakhooz | 36 | .007 | A |
| 5 | arya | 36 | .005 | ABC |
| 6 | vamaaden | 36 | .011 | AB |
| 7 | shapdis | 36 | .010 | AB |
| 8 | tapiko | 36 | .014 | AB |
| 9 | tipiko | 36 | .011 | CD |
| 10 | khodro | 36 | .010 | A |
| 11 | fars | 36 | .008 | ABC |
| 12 | kachad | 36 | .008 | BCD |
| 13 | parsan | 36 | .009 | ABC |
| 14 | v0mid | 36 | .016 | ABC |
| 15 | shotoran | 36 | .005 | D |
| 16 | vaparsan | 36 | .011 | AB |
| 17 | Valid N (listwise) | 36 | .002 | D |
| N Statistic | Range Statistic | Minimum Statistic | Maximum Statistic | Sum Statistic | Mean | Std. Deviation Statistic | VAryance Statistic | ||
|---|---|---|---|---|---|---|---|---|---|
| Statistic | Std. Error | ||||||||
| shabandar | 35 | .1447242 | .0002373 | .1449615 | .4055647 | .011587563 | .0052178875 | .0308694390 | .001 |
| vmelat | 35 | .0171187 | .0000076 | .0171263 | .0700641 | .002001831 | .0005578601 | .0033003450 | .000 |
| famli | 35 | .1025881 | .0000390 | .1026272 | .2641484 | .007547096 | .0034857553 | .0206220063 | .000 |
| fakhooz | 35 | .0864239 | .0000446 | .0864685 | .2519932 | .007199805 | .0028205120 | .0166863738 | .000 |
| arya | 35 | .0696418 | .0000126 | .0696544 | .1849684 | .005284813 | .0022735861 | .0134507165 | .000 |
| vamaaden | 35 | .2163631 | .0000950 | .2164581 | .3805961 | .010874174 | .0064181237 | .0379701317 | .001 |
| shapdis | 35 | .1926559 | .0000929 | .1927488 | .3607872 | .010308206 | .0057271750 | .0338824243 | .001 |
| tipiko | 35 | .2106529 | .0000303 | .2106832 | .4759196 | .013597703 | .0069192062 | .0409345761 | .002 |
| tipiko | 35 | .3175779 | .0000025 | .3175805 | .3945800 | .011273714 | .0090586249 | .0535915474 | .003 |
| khodro | 35 | .2134125 | .0002085 | .2136211 | .3433977 | .009811361 | .0061373165 | .0363088539 | .001 |
| fars | 35 | .0918052 | .0000165 | .0918217 | .2710557 | .007744449 | .0035887314 | .0212312213 | .000 |
| Kachad | 35 | .1823077 | .0000087 | .1823165 | .2876152 | .008217577 | .0052579707 | .0311065741 | .001 |
| parsan | 35 | .1265742 | .0000243 | .1265986 | .3053473 | .008724208 | .0044796319 | .0265018599 | .001 |
| v0mid | 35 | .3571239 | .0000276 | .3571515 | .5501998 | .015719994 | .0103224347 | .0610683470 | .004 |
| shotoran | 35 | .0716178 | .0000016 | .0716194 | .1919304 | .005483726 | .0028242802 | .0167086669 | .000 |
| vaparsan | 35 | .1977188 | .0000088 | .1977275 | .3764265 | .010755044 | .0064546415 | .0381861738 | .001 |
| Valid N (listwise) | 35 | ||||||||
After categorizing and classifying the information and data collected from different dividends, it has been processed and analyzed and the results and findings of this study have been stated.
Appropriate tests were used to analyze the research questions.
Hypothesis 18: Dividends in four consecutive years (2020-2023) in Shabandar shares are unprofitable.
H0: ρ ≠ 0
Opposite hypothesis: dividends in four consecutive years (2020-2023) in Shabandar shares are profitable.
H0: ρ = 0
According to the
| Mount of mandate | Mount of F | Sig test benefit | |
|---|---|---|---|
| Shefe Test | 3 | .63 | .59 |
| Interpret | Number | Average | |
|---|---|---|---|
| Variance 2020 | 45 | .021 | A |
| Variance 2021 | 98 | .004 | A |
| Variance 2022 | 115 | .006 | A |
| Variance 2023 | 90 | .005 | A |
Similar letters indicate profitable differences between the years under test.
Hypothesis 19: Dividends in four consecutive years (2020-2023) are unprofitable in the shares of the Vemlet.
H0: ρ ≠ 0
Opposite hypothesis: dividends in four consecutive years (2020-2023) in Vemlat stocks are profitable.
H0: ρ = 0
According to the
Non-similar letters indicate the difference in profitability between the years under test.
Hypothesis 20: The dividend in four consecutive years (2020-2023) in the famli stocks is loss.
H0: ρ ≠ 0
Opposite hypothesis: dividends are profitable in four consecutive years (2020-2023) in Famli stocks.
H0: ρ = 0
According to the
Non-similar letters indicate profitable differences between the years tested.
Hypothesis 21: Dividends in four consecutive years (2020-2023) in Fakhooz shares are unprofitable.
H0: ρ ≠ 0
Opposite hypothesis: dividends are profitable in four consecutive years (2020-2023) in Fakhooz shares.
| Mount of mandate | Mount of F | Sig test benefit | |
|---|---|---|---|
| Shefe Test | 3 | 1.46 | .22 |
| Interpret | Number | Average | |
|---|---|---|---|
| Variance 2020 | 45 | .009 | A |
| Variance 2021 | 98 | −.005 | A |
| Variance 2022 | 115 | .003 | A |
| Variance 2023 | 90 | .002 | A |
| Mount of mandate | Mount of F | Sig test benefit | |
|---|---|---|---|
| Shefe Test | 3 | .11 | .98 |
| Interpret | Number | Average | |
|---|---|---|---|
| Variance 2020 | 45 | .024 | A |
| Variance 2021 | 98 | .000 | A |
| Variance 2022 | 115 | .002 | A |
| Variance 2023 | 90 | .006 | A |
H0: ρ = 0
According to the
Non-similar letters indicate profitable differences between the years tested.
Hypothesis 22: Dividends in four consecutive years (2020-2023) in Arya shares are unprofitable.
H0: ρ ≠ 0
Opposite hypothesis: dividends in four consecutive years (2020-2023) in Arya shares are profitable.
H0: ρ = 0
According to the
| Mount of mandate | Mount of F | Sig test benefit | |
|---|---|---|---|
| Shefe Test | 3 | 1.37 | .25 |
| Interpret | Number | Average | |
|---|---|---|---|
| Variance 2020 | 45 | .018 | A |
| Variance 2021 | 98 | .002 | A |
| Variance 2022 | 115 | −.002 | A |
| Variance 2023 | 90 | .004 | A |
| Mount of mandate | Mount of F | Sig test benefit | |
|---|---|---|---|
| Shefe Test | 3 | 4.07 | .007 |
| Interpret | Number | Average | |
|---|---|---|---|
| Variance 2020 | 45 | .028 | A |
| Variance 2021 | 98 | .004 | A |
| Variance 2022 | 115 | .001 | A |
| Variance 2023 | 90 | .002 | A |
Non-similar letters indicate profitable differences between the years tested.
Hypothesis 23: Dividends in four consecutive years (2020-2023) are unprofitable in stocks and Vamaaden.
H0: ρ ≠ 0
Opposite hypothesis: dividends are profitable in four consecutive years (2020-2023) in stocks and Vamaaden.
H0: ρ = 0
According to the
Non-similar letters indicate profitable differences between the years tested.
Hypothesis 24: Dividends in four consecutive years (2020-2023) in Shapdis shares are unprofitable.
H0: ρ ≠ 0
Opposite hypothesis: Shapdis shares are profitable in four consecutive years (2020-2023).
H0: ρ = 0
| Mount of mandate | Mount of F | Sig test benefit | |
|---|---|---|---|
| Shefe Test | 3 | .28 | .84 |
| Interpret | Number | Average | |
|---|---|---|---|
| Variance 2020 | 45 | .010 | A |
| Variance 2021 | 98 | .003 | A |
| Variance 2022 | 115 | .002 | A |
| Variance 2023 | 90 | .000 | A |
According to the
Non-similar letters indicate the difference in profitability between the years under test.
Hypothesis 25: Dividends in four consecutive years (2020-2023) in Tapiko shares are unprofitable.
H0: ρ ≠ 0
Opposite hypothesis: dividend in four consecutive years (2020-2023) in Tapiko shares is loss.
H0: ρ = 0
According to the
Non-similar letters indicate profitable differences between the years tested.
Hypothesis 26: Dividends in four consecutive years (2020-2023) in Tipiko shares are unprofitable.
H0: ρ ≠ 0
Opposite hypothesis: dividends in four consecutive years (2020-2023) in Tipiko shares are profitable.
H0: ρ = 0
According to the
Non-similar letters indicate profitable differences between the years tested.
| Mount of mandate | Mount of F | Sig test benefit | |
|---|---|---|---|
| Shefe Test | 3 | 1.18 | .31 |
| Interpret | Number | Average | |
|---|---|---|---|
| Variance 2020 | 45 | .018 | A |
| Variance 2021 | 98 | .009 | A |
| Variance 2022 | 115 | .001 | A |
| Variance 2023 | 90 | .000 | A |
| Mount of mandate | Mount of F | Sig test benefit | |
|---|---|---|---|
| Shefe Test | 3 | .67 | .57 |
| Interpret | Number | Average | |
|---|---|---|---|
| Variance 2020 | 45 | .019 | A |
| Variance 2021 | 98 | .004 | A |
| Variance 2022 | 115 | .002 | A |
| Variance 2023 | 90 | .006 | A |
| Mount of mandate | Mount of F | Sig test benefit | |
|---|---|---|---|
| Shefe Test | 3 | .98 | .96 |
| Interpret | Number | Average | |
|---|---|---|---|
| Variance 2020 | 45 | .003 | A |
| Variance 2021 | 98 | .001 | A |
| Variance 2022 | 115 | .003 | A |
| Variance 2023 | 90 | .005 | A |
Hypothesis 27: Dividends in four consecutive years (2020-2023) in Khodro stocks are unprofitable.
H0: ρ ≠ 0
Opposite hypothesis: dividends in four consecutive years (2020-2023) in Khodro stocks are profitable.
H0: ρ = 0
According to the
Non-similar letters indicate profitable differences between the years tested.
Similar letters indicate no profitable difference.
Hypothesis 28: Dividends in Pars shares are unprofitable in four consecutive years (2020-2023).
H0: ρ ≠ 0
Opposite hypothesis: dividends are profitable in Pars shares for four consecutive years (2020-2023).
H0: ρ = 0
According to the
Non-similar letters indicate profitable differences between the years tested.
Similar letters indicate no profitable difference.
| Mount of mandate | Mount of F | Sig test benefit | |
|---|---|---|---|
| Shefe Test | 3 | 2.16 | .09 |
| Interpret | Number | Average | |
|---|---|---|---|
| Variance 2020 | 45 | .030 | A |
| Variance 2021 | 98 | −.004 | A |
| Variance 2022 | 115 | .004 | A |
| Variance 2023 | 90 | .001 | A |
| Mount of mandate | Mount of F | Sig test benefit | |
|---|---|---|---|
| Shefe Test | 3 | 1.11 | .34 |
| Interpret | Number | Average | |
|---|---|---|---|
| Variance 2020 | 45 | .019 | A |
| Variance 2021 | 98 | .000 | A |
| Variance 2022 | 115 | .005 | A |
| Variance 2023 | 90 | .003 | A |
Hypothesis 29: Dividends in four consecutive years (2020-2023) are unprofitable in Kachad stocks.
H0: ρ ≠ 0
Opposite hypothesis: dividend in four consecutive years (2020-2023) in Kechad shares is profitable.
H0: ρ = 0
According to the
Non-similar letters indicate profitable differences between the years tested.
Similar letters indicate no profitable difference.
Hypothesis 30: Dividends in Parsan shares in four consecutive years (2020-2023) are unprofitable.
H0: ρ ≠ 0
Opposite hypothesis: dividends are profitable in Parsan shares for four consecutive years (2020-2023).
H0: ρ = 0
According to the
Non-similar letters indicate profitable differences between the years tested.
Similar letters indicate no profitable difference.
| Mount of mandate | Mount of F | Sig test benefit | |
|---|---|---|---|
| Shefe Test | 3 | .22 | .88 |
| Interpret | Number | Average | |
|---|---|---|---|
| Variance 2020 | 45 | .009 | A |
| Variance 2021 | 98 | .004 | A |
| Variance 2022 | 115 | .009 | A |
| Variance 2023 | 90 | .002 | A |
| Mount of mandate | Mount of F | Sig test benefit | |
|---|---|---|---|
| Shefe Test | 3 | 1.02 | .39 |
| Interpret | Number | Average | |
|---|---|---|---|
| Variance 2020 | 45 | .017 | A |
| Variance 2021 | 98 | .005 | A |
| Variance 2022 | 115 | .000 | A |
| Variance 2023 | 90 | .002 | A |
Hypothesis 31: Dividends in four consecutive years (2020-2023) in V0mid shares are unprofitable.
H0: ρ ≠ 0
Opposite hypothesis: dividend is profitable in four consecutive years (2020-2023) in V0mid shares.
H0: ρ = 0
According to the
Non-similar letters indicate profitable differences between the years tested.
Similar letters indicate no profitable difference.
Hypothesis 32: Dividends in four consecutive years (2020-2023) in Shotoran stocks are unprofitable.
H0: ρ ≠ 0
Opposite hypothesis: dividend in four consecutive years (2020-2023) in Shotoran shares is profitable.
H0: ρ = 0
According to the
Non-similar letters indicate profitable differences between the years tested.
Hypothesis 33: Dividends in four consecutive years (2020-2023) are unprofitable in Vaparsan stocks.
H0: ρ ≠ 0
Opposite hypothesis: dividends in four consecutive years (2020-2023) in Vaparsan shares are profitable.
H0: ρ = 0
According to the
| Mount of mandate | Mount of F | Sig test benefit | |
|---|---|---|---|
| Shefe Test | 3 | .39 | .76 |
| Interpret | Number | Average | |
|---|---|---|---|
| Variance 2020 | 45 | .014 | A |
| Variance 2021 | 98 | .007 | A |
| Variance 2022 | 115 | .003 | A |
| Variance 2023 | 90 | .002 | A |
| Mount of mandate | Mount of F | Sig test benefit | |
|---|---|---|---|
| Shefe Test | 3 | 1.79 | .15 |
| Interpret | Number | Average | |
|---|---|---|---|
| Variance 2020 | 45 | .017 | A |
| Variance 2021 | 98 | .001 | A |
| Variance 2022 | 115 | .000 | A |
| Variance 2023 | 90 | .002 | A |
| Mount of mandate | Mount of F | Sig test benefit | |
|---|---|---|---|
| Shefe Test | 3 | .77 | .5 |
| Interpret | Number | Average | |
|---|---|---|---|
| Variance 2020 | 45 | .020 | A |
| Variance 2021 | 98 | .000 | A |
| Variance 2022 | 115 | .004 | A |
| Variance 2023 | 90 | .004 | A |
Non-similar letters indicate profitable differences between the years tested.
Similar letters indicate no profitable difference.
Hypothesis 34: Dividends in different stocks are not different during the years 2020-2023
H0: ρ ≠ 0
Counter hypothesis: Dividends in different stocks are not different during the years 2020-2023
H0: ρ = 0
According to the
Non-similar letters indicate profitable differences between stocks (
| Mount of mandate | Mount of F | Sig test benefit | |
|---|---|---|---|
| One-way ANOVA test | 16 | .71 | .77 |
| interpretation | Number | average | ||
|---|---|---|---|---|
| 1 | shabandar | 348 | .007 | A |
| 2 | vmelat | 348 | .001 | A |
| 3 | famli | 348 | .005 | A |
| 4 | fakhooz | 348 | .003 | A |
| 5 | arya | 348 | .006 | A |
| 6 | vamaaden | 348 | .003 | A |
| 7 | shapdis | 348 | .005 | A |
| 8 | tapiko | 348 | .006 | A |
| 9 | tipiko | 348 | .003 | A |
| 10 | khodro | 348 | .004 | A |
| 11 | fars | 348 | .005 | A |
| 12 | kachad | 348 | .004 | A |
| 13 | parsan | 348 | .004 | A |
| 14 | v0mid | 348 | .005 | A |
| 15 | shotoran | 348 | .003 | A |
| 16 | vaparsan | 348 | .005 | A |
| 17 | Valid N (listwise) | 348 | .003 | A |
| N Statistic | Range Statistic | Minimum Statistic | Maximum Statistic | Sum Statistic | Mean | Std. Deviation Statistic | Variance Statistic | ||
|---|---|---|---|---|---|---|---|---|---|
| Statistic | Std. Error | ||||||||
| shabandar | 348 | 1.1522868 | −.3944511 | .7578357 | 2.4848804 | .007140461 | .0038395960 | .0716267346 | .005 |
| vmelat | 348 | .4370083 | −.1423587 | .2946496 | .3610506 | .001037502 | .0022282098 | .0415667149 | .002 |
| famli | 348 | .8736544 | −.2496860 | .6239684 | 1.9273844 | .005538461 | .0031327390 | .0584404889 | .003 |
| fakhooz | 348 | .5842428 | −.1703163 | .4139265 | 1.1524069 | .003311514 | .0030418048 | .0567441336 | .003 |
| arya | 348 | .5555236 | −.1568882 | .3986354 | 2.0787766 | .005973496 | .0025395076 | .0473738999 | .002 |
| vamaaden | 348 | .8511852 | −.2316099 | .6195753 | 1.0183253 | .002926222 | .0031534300 | .0588264739 | .003 |
| shapdis | 348 | .8045874 | −.1991444 | .6054431 | 1.8059133 | .005189406 | .0032126867 | .0599318936 | .004 |
| tapiko | 348 | 1.0262908 | −.3105664 | .7157244 | 2.0996094 | .006033360 | .0036990552 | .0690049793 | .005 |
| tipiko | 348 | .9178687 | −.1360077 | .7818610 | 1.0740788 | .003086433 | .0029112932 | .0543094709 | .003 |
| khodro | 348 | 1.0972092 | −.1953224 | .9018868 | 1.4751753 | .004239010 | .0040763285 | .0760429221 | .006 |
| fars | 348 | .8342757 | −.2330326 | .6012431 | 1.6636079 | .004780483 | .0030404467 | .0567187983 | .003 |
| kachad | 348 | .7557903 | −.1653491 | .5904412 | 1.2473142 | .003584236 | .0027019220 | .0504037005 | .003 |
| parsan | 348 | .8235838 | −.1970015 | .6265823 | 1.4777243 | .004246334 | .0030882950 | .0576113967 | .003 |
| v0mid | 348 | 1.0769609 | −.2702015 | .8067595 | 1.8900702 | .005431236 | .0035890616 | .0669530766 | .004 |
| shotoran | 348 | .6777829 | −.1602434 | .5175395 | 1.1187164 | .003214702 | .0023172467 | .0432276759 | .002 |
| vaparsan | 348 | .9577747 | −.1979094 | .7598653 | 1.8404038 | .005288517 | .0039936580 | .0745007234 | .006 |
| Valid N (listwise) | 348 | ||||||||
This study attempts to investigate of what are the effects of artificial intelligence (AI) on the financial performance of 17 Iranian companies and strategies for improving risk and capital management using artificial intelligence? The companies can be divided into Metals, Petrochemical, investment, Automotive, Oil products, and Banking. Kechad, Famili, Fakhor are metals companies. Vamaaden, Ariya, Pars, Shapdis are Petrochemical companies. Tapico, Vomid, Tipiko are investment companies. Khodro is Khodro Company. Parsan and Shatran, Shebandar Oil Products of Company. Vamelat and Vapars are banking companies. The findings of this study reveal that Shatran, Arya, Shebandar, Famili are profitable among other stocks. Other stocks are loss because of high cost of raw materials and sanction of Iran. Moreover, companies could not export their products. Plus, the purchase power of the people in the country decreases because of the inflation of the country, so the companies could not buy anything. On the one hand, liquidity power of people, on the other hand, less export lead to the decrease of cash flow, liquidity power of companies and firms and could not sell their products. It can be concluded that those stocks are loss and have high risk.
The authors declare no conflicts of interest regarding the publication of this paper.
Banizi, A. H. T., & Alikhademi, A. (2024). Using Intelligent Systems to Manage Risks and Reduce Financial Risks Using Artificial Intelligence in Large Companies. Journal of Financial Risk Management, 13, 58-87. https://doi.org/10.4236/jfrm.2024.131003