تحلیل درماندگی مالی در بخش کشاورزی و مواد غذایی با تأکید بر نقش متغیرهای کلان اقتصادی و حسابداری
محورهای موضوعی : فصلنامه علمی -پژوهشی تحقیقات اقتصاد کشاورزیسید حسام وقفی 1 , زهره حیدری 2 , سامیران خواجه زاده 3 , صدیقه کامران راد 4
1 - عضو هیات علمی دانشگاه
2 - هیات علمی گروه حسابداری، دانشگاه کوثربجنورد، بجنورد، ایران
3 - دکتری مهندسی مالی دانشگاه آزاد اسلامی شهر قدس
4 - عضو هیئت علمی دانشگاه پیام نور،تهران،ایران.
کلید واژه: الگوریتم آدابوست, طبقه‌بندی احتمالی بیز, درماندگی مالی,
چکیده مقاله :
تحلیل درماندگی مالی و تعیین احتمال درمانده شدن قبل از بروز درماندگی موضوعی بااهمیت برای سرمایه‌گذاران، اعتباردهندگان و مدیران می‌باشد. در این پژوهش با استفاده از اطلاعات شش سال مالی طی دوره 1390 الی 1395 در بخش کشاورزی و مواد غذایی به بررسی عوامل مؤثر بر درماندگی مالی و پیش‌بینی آن با استفاده از الگوریتم آدابوست و طبقه‌بندی احتمالی بیز پرداخته‌شده است. از تأثیر مستقیم تورم، ریسک مالی و تأثیر معکوس نسبت مدیران غیرموظف، بازده سالانه سهام و نسبت وجه نقد عملیاتی بر درماندگی مالی می‌باشد. همچنین نتایج نشان می‌دهد که الگوریتم تقویت انطباقی آدابوست با استفاده از داده‌های مالی و اقتصادی توانایی بالاتری نسبت به روش طبقه‌بندی احتمالی بیز در پیش‌بینی درماندگی مالی دارد. نتایج این تحقیق می‌تواند به‌صورت کاربردی موردتوجه مدیران بخش کشاورزی و مواد غذایی بورس اوراق بهادار تهران قرار گیرد که با پیش‌بینی درماندگی مالی در شرکت‌ها و کار کردن بر روی عوامل مؤثر بر آن، نسبت به مدیریت کردن جذب سرمایه سهامداران، کاهش ریسک بحران‌های مالی و کمک به سرمایه‌گذاران جهت اجتناب از زیان‌های بزرگ در بازار سهام، اقدام نمایند.
Analysis financial distress is an important phenomenon for investors, creditors and other users of financial information. Determining the probability of a company’s distress before occurrence of distress and bankruptcy is considered a very interesting and attractive subject and can be useful for both managers, and investors and creditors. In this study, using the information of 6 financial years during the period 2011 to 2016 in industry agriculture and food materials industry, the factors affecting financial distress and predicting it through methods based on machine learning (NBC and AdaBoost) have been studied. The results of the study indicate direct impack and inflationon, indirect impact of the ratio of non-executive directors, Stock returns, the ratio of operating cash flow financial distress. The results also show that AdaBoost method, using financial and economic data, has higher capability in predicting financial distress compared to NBC method.
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