دانلود مقاله ISI انگلیسی شماره 22266
عنوان فارسی مقاله

هشدار مالی اولیه مدل سیستم و نرم افزار داده کاوی برای تشخیص خطر

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
22266 2012 16 صفحه PDF سفارش دهید محاسبه نشده
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عنوان انگلیسی
Financial early warning system model and data mining application for risk detection
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Expert Systems with Applications, Volume 39, Issue 6, May 2012, Pages 6238–6253

کلمات کلیدی
داده کاوی - سیستم های هشدار دهنده - خطرات مالی - بحران مالی
پیش نمایش مقاله
پیش نمایش مقاله هشدار مالی اولیه مدل سیستم و نرم افزار داده کاوی برای تشخیص خطر

چکیده انگلیسی

One of the biggest problems of SMEs is their tendencies to financial distress because of insufficient finance background. In this study, an early warning system (EWS) model based on data mining for financial risk detection is presented. CHAID algorithm has been used for development of the EWS. Developed EWS can be served like a tailor made financial advisor in decision making process of the firms with its automated nature to the ones who have inadequate financial background. Besides, an application of the model implemented which covered 7853 SMEs based on Turkish Central Bank (TCB) 2007 data. By using EWS model, 31 risk profiles, 15 risk indicators, 2 early warning signals, and 4 financial road maps has been determined for financial risk mitigation.

مقدمه انگلیسی

All enterprises especially SMEs need to think about global dimensions of their business earlier than ever. Especially in developing countries, in addition to the administrative insufficiencies, competition, economical conditions, the permanent threat towards SMEs from globalization, and financial crisis have caused distress and affect firms’ performance. SMEs are defined as enterprises in the non-financial business economy (NACE, Nomenclature statistique des activités économiques dans la Communauté européenne (Statistical classification of economic activities in the European Community)) that employ less than 250 persons. The complements of SMEs – enterprises that employ 250 or more persons – are large scale enterprises (LSEs). Within the SME sector, the following size-classes are distinguished: • Micro enterprises, employing less than 10 persons. • Small enterprises, employing at least 10 but less than 50 persons. • Medium-sized enterprises that employ between 50 and 250 persons. This definition is used for statistical reasons. In the European definition of SMEs two additional criteria are added: annual turnover should be less than 50 million €, and balance sheet total should be less than 43 million € (Commission Recommendation, 2003/361/EC). SMEs play a significant role in all economies and are the key generators of employment and income, and drivers of innovation and growth. Access to financing is the most significant challenges for the creation, survival and growth of SMEs, especially innovative ones. The problem is strongly exacerbated by the financial and economic crisis as SMEs have suffered a double shock: a drastic drop in demand for goods and services and a tightening in credit terms, which are severely affecting their cash flows (OECD, 2009). As a result, all these factors throw SMEs in financial distress. The failure of a business is an event which can produce substantial losses to all parties like creditors, investors, auditors, financial institutions, stockholders, employees, and customers, and it undoubtedly reflects the economics of the countries concerned. When a business with financial problems is not able to pay its financial obligations, the business may be driven into the situation of becoming a non-performing loan business and, finally, if the problems cannot be solved, the business may become bankrupt and forced to close down. Those business failures inevitably influence all businesses as a whole. Direct and indirect bankruptcy costs are incurred which include the expenses of either liquidating or an attempting to reorganize businesses, accounting fees, legal fees and other professional service costs and the disaster broadens to other businesses and the economics of the countries involved (Ross et al., 2008, Terdpaopong, 2008 and Warner, 1977). The awareness of factors that contribute to making a business successful is important; it is also applicable for all the related parties to have an understanding of financial performance and bankruptcy. It is also important for a financial manager of successful firms to know their firm’s possible actions that should be taken when their customers, or suppliers, go into bankruptcy. Similarly, firms should be aware of their own status, of when and where they should take necessary actions in response to their financial problems, as soon as possible rather than when the problems are beyond their control and reach a crisis. Therefore, to bring out the financial distress risk factors into open as early warning signals have a vital importance for SMEs as all enterprises. There is no specific method for total prevention for a financial crisis of enterprises. The important point is to set the factors that cause the condition with calmness, to take corrective precautions for a long term, to make a flexible emergency plan towards the potential future crisis. The aim of this paper is to present an EWS model based on data mining. EWS model was developed for SMEs to detect risk profiles, risk indicators and early warning signs. Chi-Square Automatic Interaction Detector (CHAID) Decision Tree Algorithm was in the study as a data mining method. Remaining of this paper is organized as follows: Section 2 presents definition of EWS. Section 3 contains data mining model for risk detection and early warning system. Implementation of data mining for risk detection and early warning signals is presented in Section 4. Concluding remarks and strategies were suggested in Section 5.

نتیجه گیری انگلیسی

Financial early warning system is a technique of analysis that is used to predict the achievement condition of enterprises and to decrease the risk of financial distress. By the application of this technique of analysis, the condition and possible risks of an enterprise can be identified with quantity. Risk management has become a vital topic for all institutions, especially for SMEs, banks, credit rating firms, and insurance companies. The financial crisis has pushed all firms to active risk management and control financial risks. All enterprises need EWS to warn against risks and prevent from financial distress. But, when we consider the issues of poor business performance, insufficient information and insufficiencies of managers in finance education, it is clear that EWS is vital for SMEs. Benefits of an EWS can summarize as early warning before financial distress, road maps for good credit rating, better business decision making, and greater likelihood of achieving business plan and objectives. Developing practical solutions will not only help to SMEs but also to the economies of countries. Having information about their financial risk, monitoring this financial risk and knowing the required roadmap for the improvement of financial risk are very important for SMEs to take the required precautions. Data mining, that is the reflection of information technologies in the area of strategically decision support, develops a system for finding solutions to the financial administration as one of the most suitable application area for SMEs as the vital point of economy. In this study, we developed a financial EWS based on financial risk by using data mining. As results of the study we classified 7853 SMEs in 31 different risk profiles via CHAID. Results showed that 31.4% of the covered SMEs financially distress. All of SMEs in profiles 1st, 2nd, and 6th have poor financial performance and these SMEs are exactly distressed firms. These profiles contain SMEs with highest financial risk. All of SMEs in profiles 19th, 22nd, 24th, and 26th have good financial performance and these SMEs are exactly non distressed firms. According to these profiles, we identified that profit before tax to own funds ratio, return on equity ratio, cumulative profitability ratio, short-term liabilities to total loans, total loans to total assets, interest expenses to net sales, fixed assets to long term loans + own funds, long term liabilities to total liabilities, gross profit to net sales, bank loans to total assets, inventory dependency ratio, own funds turnover, short-term receivables to total assets total assets, operating expenses to net sales assets, receivables turnover affect financial performance or in other words distress of the covered SMEs. When we consider risk profiles and these 15 risk indicators together, only 2 indicators can be identified as early warning signals. Financial early warning signs for covered SMEs are profit before tax to own funds and return on equity (ROE). If profits before tax to own funds and ROE ratios are lower than and equal to 0, financial distress is indispensable for SMEs. Beside these findings we determine financial road maps for risk mitigation and improve financial performance. Financial road maps can use for decision making process as inputs. According to our study findings, we developed 4 financial road maps. All of 4 road maps provide risk indicators and their values for successful management and risk hedging. EWSs should develop and implement in every business, to provide information relating to the actions of individual officers, supervisors, and specific units or divisions. In deciding what information to include in their early warning system, business should balance the need for sufficient information for the system to be comprehensive with the need for a system that is not too cumbersome to be utilized effectively. The system should provide supervisors and managers with both statistical information and descriptive information about the function of business. In case of using our EWS model by SMEs, some of expected contributions can be summarized as: • Determine financial performance and position of firms. • Determine financial strategies by minimum level of finance education and information. • Financial and operational risk detection. • Roadmaps for risk mitigation. • Prevent for financial distress. • Decrease the possibility of bankruptcy. • Decrease risk rate. • Efficient usage of financial resources. • By efficiency in resources; • Increase the competition capacity. • New potential for export. • Decrease the unemployment rate. • More taxes for government. • Adaptation to BASEL II capital accord Developing a financial EWS based on financial risk is not enough for to understand and manage the financial risks that can cause insolvency and distress. Managers need also to manage operational risks that can arise from execution of a company’s business functions, and strategically risks that can undermine the viability of their business models and strategies or reduce their growth prospects and damage their market value. For this reason we suggest to develop EWS that contain all kind of risk factors.

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