Search Results for Ali Ibrahim

Abstract

The Iraqi economy has known the phenomenon of foreign currency substitution since the eighties, with the increasing deterioration of the value of the Iraqi dinar and the appearance of signs of rapid inflation. Its actual expansion began with the phase of imposing the economic blockade in the nineties. Despite the decline in inflation rates following the phase of the economic blockade, the political, economic, and security turmoil After 2004, it was negatively affected by high inflation rates, especially during the period (2004-2007), which was reflected in the use of the US dollar as a tool to measure future payments, then a store of value, and then a medium of exchange in transactions that take place locally. Our study attempts to identify the phenomenon of foreign currency substitution in all its meanings and its relationship to inflation. Verifying the phenomenon of foreign currency substitution and determining its reality in the Iraqi economy during the period (2004-2022) In order to achieve the goal, the study adopted the inductive approach in analyzing the phenomenon under study, used the descriptive analysis method, and chose the correlation model to analyze the data and reach the results. After following the International Monetary Fund index in measuring foreign currency substitution represented by dividing deposits in foreign currency by the broad money supply, the study concluded There is a direct relationship between the inflation rate and the rate of foreign currency replacement in the Iraqi economy, with a strong direct correlation rate of (0.70) Therefore, the phenomenon of foreign currency substitution must be taken into consideration and the necessary measures taken to reduce it, especially through policies to stabilize inflation rates at low levels and search for another way to price crude oil instead of the US dollar, and let it be a basket of foreign currencies in order to reduce the linkage of the Iraqi economy to the dollar

Abstract

The research addresses two main topics: the International Public Sector Standard (IPSAS 24) relating to the presentation of budget information in financial statements, and the Government Financial Management Information System (GFMIS).

In relation to IPSAS 24, the research focuses on clarifying how budget information is presented in public sector financial statements. The standard aims to achieve transparency and reliability in providing financial information to governments and government institutions. The research addresses various aspects of the standard, such as defining financial terms, basic principles, and requirements that must be met in submitting the budget.

 For GFMIS, the research reviews and evaluates this system that is used in managing financial information for governments. GFMIS aims to improve the efficiency and effectiveness of government financial resources management, and facilitate financial planning, monitoring and evaluation processes. The concept of GFMIS, its components and benefits are reviewed, as well as the challenges of its implementation and future preferences for its development and improvement.

 Overall, the research aims to provide previous researchers with an overview of IPSAS 24 and its importance in presenting budget information in public sector financial statements, as well as reviewing GFMIS and its role in improving government financial information management. This research can contribute to raising public sector awareness of the importance of adhering to international accounting standards in the public sector and using advanced financial information management systems to enhance transparency and effectiveness in managing the financial resources of governments.

The main reason for linking these two variables is to enhance transparency, accountability, and financial control in the public sector and ensure that government financial information complies with international accounting standards in the public sector. Therefore, reviewing these two variables and analyzing their role will provide an important theoretical and applied framework for understanding the relationship between them to rationalize the budget. The most important conclusions reached for the review research are that the main goal of applying the (IPSAS) standards is to achieve compatibility in accounting policies at the global level by providing guidance and directives to develop a comprehensive theoretical framework for government accounting. Evaluating government performance is achieved through commitment to applying the (24 IPSAS) standard., which allows the preparation of a variety of financial statements detailing the approved budget and actual expenditures, the final budget (adjusted allocation), and achieving the qualitative characteristics of accounting information. The government unit did not disclose in the financial statements the extent of compliance with legislative and regulatory laws and other regulations imposed by external parties. (The State) As for the recommendations, the researchers suggest that government institutions should commit to implementing the IPSAS 24 standard completely and accurately to ensure compliance with international accounting standards. Government institutions should analyze their actual needs and conduct a feasibility study before making any transfers in the original budget, in order to ensure a strong scientific basis and improve the institution’s performance in adhering to budget directives. Government institutions should fully and effectively implement GFMIS in all government units to enhance transparency and financial control. The GFMIS should also be configured in a way that meets the needs of the government unit in a way that enables it to record and track financial transactions and prepare financial reports in an accurate and timely manner.

Abstract

Given the great importance of financial stocks and their significant role as one of the financial assets used in building the optimal investment portfolio, they are exposed to many risks, the most important of which is the decline in their market value. Therefore, our study addressed the reverse split method as a financial method used to raise the prices of financial stocks with low prices. A sample of Iraqi banks that suffer from A decrease in the level of share prices of (14) Iraqi banks for the period from 6/2014 to 6/2024, as the research aims to know the extent of the ability of the reverse segmentation method in building optimal investment portfolios when implementing the reverse segmentation, and two sides of the reverse segmentation were taken, which are the positive side represented by the rise in prices, as well as the negative side represented by the decrease Stock prices when implementing the reverse split, and the research aims to know the effect of this method on the returns and risks of stocks after its implementation, especially the returns and risks of portfolios that were built based on the cut rate as well as the performance of these portfolios, as it was found that the effect of the reverse split of stocks was found whether at a rise in the price level or at a fall in stock prices after its implementation, and that the returns The risk levels increased more when prices rose after the reverse split than when prices fell. The research results also showed that the optimal portfolio’s return when prices rose after the reverse split was higher than the portfolio’s return after the price decline. However, the risk of the optimal investment portfolio when prices fell after the reverse split was higher, the risk of the investment portfolio is higher when prices rise after implementation. The reverse split did not play any role in improving the performance of the investment portfolio whether prices rose or fell. Therefore, investment portfolio managers who seek to achieve high levels of returns regardless of the level of risk associated with those returns should buy shares of banks that implemented the split decision. Reverse, and this requires the management of the Iraq Stock Exchange to include the reverse split within the procedures in effect in the Iraq Stock Exchange.

Abstract

The research aimed to measure the negative impact of monetary shocks on the capital adequacy of the Iraqi banking sector for the period 2004-2022 using the Threshold Regression Model. The results of the research showed that there is an impact of the variables of monetary shocks (money supply, exchange rate, interest rate, index number). (prices) on the (banking sector capital adequacy) index. The most important findings of the research are that the banking sector capital adequacy at the threshold is less than 2.8984929 trillion. There was a positive impact of the money supply shock on the banking sector capital adequacy. However, when capital The banking sector is confined between 7.6688449 and 2.8984929, so the effect of the money supply shock on the capital adequacy of the banking sector is positive. However, in the third system, when the capital threshold is less than 11.73928 and greater than 7.6628449, there was a negative effect for both (the interest rate shock and the supply shock) at a significant level. 5%, while there was a significant positive effect of the exchange rate shock, and in the fourth system at the capital threshold greater than or equal to 11.73928, there was a negative effect of the price index shock at a significant level of 5%, while here there was a significant positive effect for both (the interest rate shock and the money supply).

Abstract

 The current research aims to clarify the role that psychological ownership, through its dimensions, plays in reducing the effects of toxic leadership, through its dimensions, in the Ministry of Industry and Minerals. The research started from a basic problem represented by the following question (using psychological ownership and its application in reducing the negative effects of toxic leadership), The researcher used the descriptive analytical method and used various statistical tools in the statistical program (SPSS V.23) to analyze the relationship between the main research variables, answer the main question, and verify the hypotheses. These methods include (regression coefficients, correlation coefficients, averages, standard deviations, and variances). In addition, various graphs and charts were used to illustrate the relationship between the research variables and their sub-dimensions. The sample was randomly selected from workers in some selected companies affiliated with the Ministry of Industry and Minerals in Baghdad, and the sample size reached 124 individuals. One of the most important results is that there is an effect of the psychological ownership variable, based on the four dimensions, in reducing toxic leadership behaviors in its five dimensions. Psychological ownership, stemming from individuals' feelings of ownership and connection, affects attitudes and behaviors in various contexts, including toxic leadership. The consequences of these behaviors can be harmful to individuals and organizations if left unaddressed.

Abstract

Given the swift digital changes occurring in the Banking industry, the purpose of this paper is to examine how well artificial intelligence systems can forecast and protect against future disasters.  By utilizing its skills in big data analytics, forecasting financial behavior, and more accurately and effectively managing risks, artificial intelligence (AI) is increasingly regarded as a crucial component in the development of banking systems and improving their operational efficiency.

 By enhancing client satisfaction, tailoring banking services to meet the demands of each individual, and cutting down on operational errors and administrative expenses, banks hope to gain a competitive edge by utilizing these technologies.  AI also helps to speed up credit decisions, make it possible to identify financial crime early, and create clever marketing plans based on forecasts of future market trends.

In order to ensure financial sustainability and achieve integration between digital transformation and the demands of banking innovation, studies show that the future of AI encompasses strategic, cultural, human, technological, and organizational dimensions in addition to technical ones.

 The paper also examined a number of anticipated long-term effects of AI applications, such as increased forecasting precision, lower operating expenses, better customer satisfaction, increased worker productivity, and assistance with investment choices.  The findings show that implementing AI applications in the banking sector is a strategic requirement to guarantee long-term growth and competitiveness in the digital era, not a technical luxury.

In order to enhance lending decisions and lower default risks, the paper also assesses how well a number of categorization algorithms work in assessing loan applicants' creditworthiness.  Using a dataset that represented the traits and financial activities of clients, seven machine learning techniques were used: Gradient Boosting, Random Forest, Extra Trees, Gaussian Naive Bayes, Logistic Regression, SVC-RBF, and KNN.

The paper used a database of 21 variables for loan applicants. Numerical variables included (age, income, credit score, debt-to-income ratio, and loan amount). Descriptive variables included (loan purpose, region, marital status, employer, educational level, and application channel). Binary variables included (whether or not the applicant had a history of default). These variables were used to predict the approval or rejection decision, with the dependent variable being represented by two values: 0 for rejection and 1 for approval.

The models were evaluated using the following six key performance indicators: Accuracy, Precision, Recall, F1 Score, Receiver Operating Characteristic Area Under the Curve (ROC AUC), and Brier Score.   The findings demonstrated that the Gradient Boosting algorithm performed best overall in both probability prediction quality and customer differentiation across different risk levels.  The Random Forest algorithm, which showed stability and balanced metrics, came next.  On the other hand, despite its moderate performance, Logistic Regression provided great interpretability, while the Gaussian Naive Bayes algorithm demonstrated high sensitivity in identifying high-risk customers.  In terms of overall accuracy and probability quality, some models—like SVC-RBF and KNN—performed worse.

Abstract

In light of the importance of stocks, whose investment and trading play a fundamental role in stock market activity, it is therefore necessary to show importance in evaluating and predicting the prices of these stocks in the future. In light of the changes in economic conditions and the difficulty of forecasting, this research dealt with one of the financial methods represented by (valuation multiples) with its six models for forecasting and evaluating stock prices and applying them to real data in the Iraqi Stock Exchange by taking a sample of the banks listed on the Iraqi Stock Exchange, which are banks ( Assyria, Baghdad, Iraqi Commercial, Business Gulf, Iraqi Investment, Al-Mansour, Sumer) which are continuing within the market activities by publishing their annual share prices, as the research aimed to determine the accuracy and closeness of the banks’ evaluation of their share prices to the market prices through the use of (valuation multipliers) For the period from (2016-2020) up to the predicted year, which is 2021, and then comparing it with the market price for the year (2021), which can greatly affect investment strategies and market activity. In addition, the relationship between the two values was tested through the nonparametric test, Mann-Whitney, in proportion to the selected sample. In light of this, the research reached a set of conclusions, the most important of which is that some of these banks are valued higher than their market value, and some are equal to or lower than the market value. Which resulted in the fact that there are no significant differences between the real value calculated by valuation multiples and the market value of the stock according to statistical tests.

Abstract

This research aims to analyze the impact of bank Credit risk as an independent variable on the returns of investors represented by the free cash flow available to the owner of Common stocker as a dependent variable. To achieve this goal, we used a sample of (20) Iraqi Commercial bank for the period from 2014 - 2020. using quantitative risk metrics, the study found correlations and adverse influence between two of dependent variables and positive correlations and effect with the other variables.

Abstract

This study aims to compare and improve the methods of building investment portfolios for a sample of Iraqi banks listed on the Iraq Stock Exchange, by comparing traditional methods such as the Markowitz model with modern techniques based on machine learning. The Markowitz model is key to balancing return and risk across the medium-variance optimization framework, a traditional model that many financial institutions rely on. The study focused on exploring the extent to which machine learning techniques such as key component analysis (PCA), supporting vector machine (SVM), logistic regression, and random forest can improve the performance of the investment portfolios of these banks in a volatile environment such as the Iraq Stock Exchange. These techniques rely on processing and analyzing huge financial data to discover hidden patterns and relationships that help increase returns and reduce risk more effectively compared to traditional methods. The historical financial data related to the shares and assets of the banks of the research sample in the Iraq Stock Exchange was used to evaluate the performance of portfolios according to indicators such as expected return, variance, and Sharpe ratio. The study aims to provide innovative solutions that help banks make smarter and more effective investment decisions, commensurate with the local market conditions and the economic and political challenges they face.