Search Results for forecasting

Abstract

This paper aim aims to predict the quantities of water needed in the city of Baghdad for the next 10 months. This paper focuses on potable water, based on the time series data of the water consumption phenomenon in the city, which was obtained from the Ministry of Water Resources, specifically the Baghdad Water Department. Statistical forecasting techniques were used on the monthly water consumption data for the city of January 2014 until May (2024, a total of 125 months, and that is Baghdad in the period from to reach an estimate of the quantities needed by the city of Baghdad in the future. Curve Estimation and Linear Regression forecasting techniques were used, such as linear regression analysis and the Box - Jenkins (ARIMA) methodology, to obtain the best water consumption model in the city of Baghdad and the most accurate. In This paper we concluded that it is the best model suitable for predicting monthly water consumption in Baghdad city is (3,1,1) ARIMA among the models proposed in the Box-Jenkins methodology in terms of accuracy measures and (Mean Absolute Percentage Error) which reached (2.44-MAPE).While the (Mean Absolute Percentage Error) for the Simple Linear (MAPE=8) Quadratic Regression model and the Quadratic Regression model were also found, the research concluded that monthly consumption will increase in the city of Baghdad, when compared to Between the actual values ​​and the predictive values ​​of the methods used in the paper to predict the future. Finally, it is recommended to take the necessary measures to limit water consumption in the city, through pricing, awareness, education, intermittent supplies and other measures that preserve water resources and achieve sustainability.

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

The current study aims to predict the failure of companies through the use of financial ratios derived from cash flow disclosure and then categorize them into two categories, the safe category means that the company is in a secure financial position capable of providing cash and fulfilling financial obligations, and the second category is the unsafe category where the company is In a troubled financial situation unable to meet the financial obligations, as (11) financial ratios derived from the cash flow statement were used, and the study was applied in the Iraq Stock Exchange, as the sample consisted of (42) companies listed in it and for the period 2016-2020. Through the use of logistic regression analysis to the prediction model that works to classify companies, with an accuracy rate of 52.4%, the model consists of (4) financial ratios, which are (the ratio of operational activity, the ratio of operating cash to sales, the ratio of operating cash return to total assets, and finally the percentage of cash return operating to total liabilities)

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

The research focuses on calculating the expected credit risks according to the IFRS9 9 standard and how to apply this standard in the National Bank of Iraq. IFRS9 9 is an accounting standard that deals with the classification, financial value of financial assets and the management of risks related to them. Modern accounting standards require considering the financial risks of loans and other financial products owned by the bank. The IFRS9 9 standard aims to supply a comprehensive credit risk management system and supply a probable estimate of expected losses on loans and other financial products. The process of calculating the expected credit risk by the IFRS9 9 standard includes several main steps. First, financial products should be classified according to the degree of expected risk. This classification is based on the quantitative and qualitative information relevant to the bank and the credit risk assessment for each category. After that, the expected credit size for each category is decided based on forecasting models and risk estimates. These models are based on a set of accounting, economic and business standards. Historical data and current information are used to decide the expected credit volume and the possible risks entailed by financial portfolios. According to accounting standards, banks must include the expected credit volume in periodic financial reports and constantly update it. This helps third parties, such as investors and regulators, to understand the bank's exposure to credit risks and the efficiency of the bank's risk management. This process is reflected in the volume of credit applied at the National Bank of Iraq by improving the bank's understanding of credit risks and thus the ability to make better decisions in granting loans and managing risks. The aim of this research is to study the calculation of expected credit risks following the IFRS9 9 standard and analyze their impact on the credit volume in its application at the National Bank of Iraq. The focus is on understanding the details of the standard and how to apply it to improve risk management and make better decisions in granting loans. Through this research, we have concluded that calculating the expected credit risks by IFRS9 contributes to enhancing the bank's understanding of credit risks and improving its efficiency in risk management, and the correct application of the standard helps in supplying more transparent and predictable financial reporting of potential losses. Based on the findings, there are some recommendations for improving risk management at the National Bank of Iraq and applying the IFRS9 standard. The bank should strengthen its technical capabilities to collect and analyze financial data and credit ratings in a more correct and effective manner, and the bank should supply continuous training to employees on the standard and methods of its implementation and the use of proper predictive models to calculate the expected credit risks. Finally, the bank should give financial reports in an organized and transparent manner, explaining the expected credit volume and the potential risks entailed by this volume. This will help investors and regulators understand the extent of the bank's exposure to credit risks and the efficiency of the bank's risk management.

Abstract

This research aims to provide a theoretical and applied framework for employing machine learning algorithms in management accounting and costing systems.

The research focuses on the importance of improving resource consumption monitoring, accurately tracking cost behavior, identifying unutilized energy, and supporting decision-making through historical data analysis to enhance the accuracy of production reports.

To achieve the research objective, a descriptive approach was adopted, drawing on available studies. A field study was also used, using a questionnaire to collect data from the research sample (the Electrical Cables and Wires Factory - Ur General Company).

The research also reached a number of conclusions, most notably that employing machine learning algorithms contributes to improving the prediction of quantitative resource consumption, which helps detect deviations and identify their potential causes, and enhances the accuracy and comprehensiveness of production reports.

The research concluded with a set of recommendations, most notably the need to establish an integrated data management system that includes operational data processing to provide real-time solutions and alternatives that contribute to supporting decision-making related to rationalizing resource consumption.

Abstract

This study aims to demonstrate the impact of announcing the distribution of dividends in light of information asymmetry and in light of the phenomenon of terrorism for the purpose of predicting stock prices for companies listed on the Iraqi Stock Exchange. The study was applied to a sample of various market sectors, taking into account the diversity in the sectors, which included  Mosul Dam Company that met the conditions of the study, which identified the companies that distributed dividends for two consecutive years (2014-2015), and the event study method was used with a (40) day event window with a period of (20) days before and after the event to measure the information asymmetry, as the forecasting method was adopted to identify the effects. The future of the dividend decision depends on investors' decisions in light of conditions of instability. In addition, two statistical methods were used to test the study's hypotheses, namely the regression analysis method and the scenario method. The study reached a set of conclusions, the most important of which is the possibility of achieving extraordinary returns by relying on the informational content of the dividend dividend. There is also a significant impact Statistical significance for the dividend decision due to information asymmetry. The scenario method contributes to predicting stock prices better than the traditional method. One of the most important recommendations reached by the study is the necessity of adopting scientific methods to measure the impact of market-related terrorist events on the accuracy of financial results, especially the use of mathematical models to measure the impact. Market events on stock prices. It is also preferable to adopt the scenario method in predicting stock prices and financial performance and adopt it as a model that provides multiple options for financial decisions, in addition to not being satisfied with the extraordinary return as only one tool for making investment decisions, but rather other factors such as risk must be taken into account.

Abstract

This study explores the impact of entrepreneurial resilience and entrepreneurial success. A researcher conducted a survey of entrepreneurs in small and medium-sized companies in Zakho city in the Kurdistan Region of Iraq to understand the extent to which flexibility affects company growth and success. They used the descriptive analytical approach to analyse the survey data. The questionnaire was the primary approach to data gathering. Data analysis was performed using both SPSS v26 and Smart PLS v 4.0.9. (82) questionnaires were collected from a total of 95 survey cases. The findings of this study show a substantial and statistically significant effect of entrepreneurial resilience on entrepreneurial success. Entrepreneurial resilience is critical to increasing a company's effectiveness and profitability by addressing immediate and continuous difficulties that force it to seek out possibilities, innovate, and face risks. While these results are convincing in the context of the study, it is necessary to be careful when generalizing them to other corporate settings. Further research is recommended to delve deeper into the nuances of entrepreneurial resilience across diverse contexts. This study contributes valuable insights for entrepreneurs seeking to improve performance through entrepreneurial resilience. Through understanding and leveraging forecasting and forcing conditions, organizations can promote better outcomes and achieve success.

Abstract

This research aims to shed light on the concept of real-time financial reporting, the benefits of preparing such reports, and to demonstrate the concept and importance of financial reporting transparency. It also aims to identify the role of real-time financial reporting in improving the transparency of financial reporting. To achieve the research objectives, a questionnaire was distributed to a number of employees at the Baghdad Soft Drinks Company. The research reached several conclusions, including that real-time financial reporting represents a qualitative leap for entities seeking to maintain their competitiveness and flexibility in today's rapidly evolving environment. By providing immediate access to accurate financial data, these reports enable better decision-making, strengthen financial control, improve forecasting, increase transparency, and comply with regulations.

Abstract

This research aims to know the role that strategic intelligence performed in its dimensions (forecasting, organized thinking, partnership, invention, and benchmarking) in influencing customer relationship management in its dimensions (customer acquisition, strengthening the relationship with the customer, customer retention, customer satisfaction) for a sample of private banks In the province of Baghdad, the two researchers relied on the questionnaire as a measure of the research variables, and the relationship between them was tested by selecting intentional sample that included (106) of (principals of senior departments - departments - and administrative units) working in the banking sector using statistical methods(SPSS, v.25) The results showed a direct impact relationship of strategic intelligence in managing customer relations, and the increasing interest of the surveyed banks in strengthening the relationship with the customer, which was one of its most prominent goals, and the research reached to a number of recommendations, most notably working on paying attention to strategic intelligence and activating its role among banking leaders to support invention capabilities and improve the reality of banking services.