Seyed Mahdi Sadatrasoul; Omid Mahdi Ebadati; Amir Amirzadeh Irani
Abstract
Companies have different considerations for using smoothing in their financial statements, including annual general meeting, auditing, Regulatory and Supervisory institutions and shareholders requirements. Smoothing is done based on the various possible and feasible choices in identifying company’s ...
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Companies have different considerations for using smoothing in their financial statements, including annual general meeting, auditing, Regulatory and Supervisory institutions and shareholders requirements. Smoothing is done based on the various possible and feasible choices in identifying company’s incomes, costs, expenses, assets and liabilities. Smoothing can affect credit scoring models reliability, it can cause to providing/not providing facilities to a non-worthy/worthy organization orderly, which are both known as decision errors and are reported as “type I” and “type II” errors, which are very important for Banks Loan portfolio. This paper investigates this issue for the first time in credit scoring studies on the authors knowledge and searches. The data of companies associated with a major Asian Bank are first applied using logistic regression. Different smoothing scenarios are tested, using wilcoxon statistic indicated that traditional credit scoring models have significant errors when smoothing procedures have more than 20% change in adjusting company’s financial statements and balance sheets parameters.
Seyed M. Sadatrasoul; O. Ebadati; R. Saedi
Abstract
The purpose of this study is to reduce the uncertainty of early stage startups success prediction and filling the gap of previous studies in the field, by identifying and evaluating the success variables and developing a novel business success failure (S/F) data mining classification prediction model ...
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The purpose of this study is to reduce the uncertainty of early stage startups success prediction and filling the gap of previous studies in the field, by identifying and evaluating the success variables and developing a novel business success failure (S/F) data mining classification prediction model for Iranian start-ups. For this purpose, the paper is seeking to extend Bill Gross and Robert Lussier S/F prediction model variables and algorithms in a new context of Iranian start-ups which starts from accelerators in order to build a new S/F prediction model. A sample of 161 Iranian start-ups which are based in accelerators from 2013 to 2018 is applied and 39 variables are extracted from the literature and organized in five groups. Then the sample is fed into six well-known classification algorithms. Two staged stacking as a classification model is the best performer among all other six classification based S/F prediction models and it can predict binary dependent variable of success or failure with accuracy of 89% on average. Also finding shows that “starting from Accelerators”, “creativity and problem solving ability of founders”, “fist mover advantage” and “amount of seed investment” are the four most important variables which affects the start-ups success and the other 15 variables are less important.
