جستجوی مقالات مرتبط با کلیدواژه « decision trees » در نشریات گروه « مالی »

This article aims to forecast Iran Cooperative Development Bank's profit/loss using a two-stage collective learning method. Employing machine learning for profit and loss prediction is a novel approach to numerical computations, aligning with the article's goal of leveraging big data. Hence, we employ a two-stage collective learning method utilizing Support Vector Machines, Decision Trees, and Weighted Averaging models for learning, testing, and predicting the profit/loss of the Cooperative Development Bank.

In the initial stage of machine learning, Support Vector Machines, and Decision Trees serve as the base models, while the second stage employs a weighted averaging approach. The combination of base learning models was utilized to initially test the prediction process on the performance of 35 branches of the Cooperative Development Bank nationwide from 2012 to 2021. The two-stage machine learning method relies on the utilization of 12 variables grouped into 5 factors for the task. These variables encompass interest-free loans and short-term deposits, deposit interest expenses, total customer deposits, income from facilities and investments, common income, cash balances, administrative expenses, long-term deposits, non-common income, asset depreciation expenses, and the bank's size. Relevant data for the study was obtained from the financial statements of 35 branches of the Cooperative Development Bank spanning the years 2012 to 2021. To minimize prediction errors and facilitate ratio comparisons, all indicators mentioned were adjusted relative to the total value of the bank's assets. Furthermore, to minimize prediction errors and enhance the comparability of ratios, all the mentioned indicators were adjusted in proportion to the total value of the bank's assets. In the initial stage of applying this method, two machine learning models - Support Vector Machines and Decision Trees - were employed, followed by a weighted averaging approach in the second stage.

This article contrasts linear regression with machine learning approaches for predicting the Cooperative Development Bank's actual profit/loss. The results reveal notably high-performance accuracy, evidenced by an MAE metric of 5.66 and an MSE metric of 620.34. Additionally, the correlation between training data and predictions from the two-stage collective machine learning stands at 0.9977. Following this method's performance assessment, it is subsequently employed to predict the Cooperative Development Bank's profit/loss for the years 2022 to 2027.

The results, showcasing the high efficiency of the two-stage collective machine learning method, suggest that managers can employ these approaches for profit/loss prediction in banks. Based on this method, and under normal conditions without abnormal or non-normal circumstances, the obtained results indicate a prospective decrease in the accumulated losses of the Cooperative Development Bank in future years, leading to an ultimate increase in profits by the year 1405. The results of data analysis reveal that the average ratio of the net profit or loss of the bank to its total assets has been computed in a manner that reflects the average profitability of the bank branches over the research period.

The objective of this study is to identify and ranking of factors affecting detecting the financial statement frauds using the judgment technique based on the Analytic Hierarchy Process and data mining techniques techniques. The population of the study comprised of senior auditors, supervisors, senior supervisors, audit manager and partner of the audit institute employed in audit institutes member and also companies listed in the Tehran Stock Exchange. In order to the research goal, 56 questionnaires and 109 Listed for the year 2012-2017 and analyzed. Based on the technique of judgment, the pressure dimension of the first priority, opportunity, second factor and rationalization are ranked as the third effective factor on the detection of fraud. These results are different with other techniques. Empirically, the ANNs and CART approaches work with the training and testing samples in a correct classification rate of 98/65% (ANNs) & 91.5% (CART) and 69/79% (ANNs) & 69.10% (CART), respectively, which is more accurate than the logistic model that only reaches 72.32% and 88.10% of the correct classification in assessing the fraud presence. In addition, type II error of CART drops significantly to 58.18% from 72.7% and 55.6% compared to the ones using ANNs and logistic models. According to the accuracy index, the decision tree model is more efficient than other models; therefore, among the data mining techniques, the weight of each of the input variables of the decision tree is the basis for the final ranking of the research variables