پیش‌بینی درماندگی مالی شرکت‌های پذیرفته شده در فرابورس و بورس اوراق بهادار تهران با استفاده از رگرسیون لجستیک لاسو

پیش‌بینی درماندگی مالی شرکت‌های پذیرفته شده در فرابورس و بورس اوراق بهادار تهران با استفاده از رگرسیون لجستیک لاسو

نوع مقاله : مقاله پژوهشی

نویسندگان

دانشگاه شیراز

چکیده

هدف این مقاله، کشف درماندگی مالی بالقوه و هشدار زودهنگام درماندگی مالی قریب‌الوقوع شرکت‌های پذیرفته شده در فرابورس و بورس اوراق بهادار است. بدین منظور، دامنه گسترده‌ای از ویژگی‌ها از جمله متغیرهای حسابداری تعهدی، حسابداری نقدی، بازار سهام، مکانیسم‌های حاکمیت شرکتی و شاخص‌های اقتصاد کلان برای پیش‌بینی درماندگی مالی شرکت‌های نمونه شناسایی شده‌اند. نمونه نهایی شامل 421 شرکت و در نتیجه، 3670 شرکت-سال مشاهده است. سپس، داده آماده شده با استفاده از نسبت 70 به 30 به مجموعه داده آموزشی و آزمایشی تفکیک شد.
در این پژوهش، تکینک‌های پیش‌ پردازش داده یادگیری ماشین نظیر استانداردسازی نمره Z، وان-هات انکدینگ، اعتبارسنجی متقابل K لایه طبقه‌ای، همراه با مهندسی ویژگی برای بهبود عملکرد طبقه‌بندی کننده بکار گرفته شدند. روش اعتبارسنجی متقابل K لایه طبقه‌ای با (5=K) برای برآورد عملکرد پیش‌بینی مدل طی مرحله آموزش استفاده شد. طی مرحله آموزش، میزان‌سازی اَبرپارامتر مدل با استفاده از جستجوی حریص انجام شد. افزون بر این، رویکرد فرایادگیری حساس به هزینه همراه با معیار مختص مسائل نامتوازن یعنی نمره F1 برای غلبه بر مسأله نامتوازنی افراطی کلاس‎ها استفاده شده است.
بر اساس نتایج تجربی، مدل لجستیک لاسو به نمره F1، ضریب همبستگی متیوز، فراخوانی و دقتی به ترتیب برابر با 50%، 50%، 73% و 38% بر روی مجموعه آموزشی دست یافت. سرانجام، مدل پیشنهادی بر روی مجموعه آزمایشی کنار گذاشته شده آزمون شد که به نمره F1، ضریب همبستگی متیوز، فراخوانی و دقتی به ترتیب برابر با 51%، 51%، 73% و 38% بر روی مجموعه آزمایشی منجر شد.

کلیدواژه‌ها


عنوان مقاله [English]

Financial Distress Prediction of the Listed Companies on Tehran Stock Exchange (TSE) and Iran Fara Bourse (IFB) Using Lasso Logistic Regression

نویسندگان [English]

  • Mohammad Namazi
  • Shahla Ebrahimi
Shiraz University
چکیده [English]

The purpose of this article is the detection of potential financial distress and early warnings of impending financial distress among the listed companies on Tehran Stock Exchange (TSE) and Iran Fara Bourse (IFB). To do so, a wide range of features including accrual accounting variables, cash-based accounting variables, market-based variables, corporate governance mechanisms, and macroeconomic indicators have been identified to prospectively predict the financial distress in the companies.
The final sample includes 421 firms leading to 3,670 firm-year observations. The prepared data, was then split into a train and test data set using a 70/30 ratio.
In this research, various data pre-processing machine learning techniques i.e., Z-score standardization, one-hot encoding, stratified K-fold validation combined with feature engineering are applied to improve classifier performance. Stratified K-fold cross validation method, (with k = 5) was used for estimation of model prediction performance during training phase. During the training phase, hyper-parameter tuning of a model was carried out using a grid-search. Furthermore, a cost-sensitive meta-learning approach in conjunction with the proposed imbalance-oriented metric i.e., F1 score were used to overcome the extreme class imbalance issue.
Based on the experimental results, the tuned LASSO logistic model achieved a f1-score, MCC, recall and precision of respectively, 50%, 50%, 73% and 38% on the training set. Finally, the proposed model was tested on the hold-out test set which resulted in a f1-score, MCC, recall and precision of 51%, 51%, 73% and 39%, respectively.

کلیدواژه‌ها [English]

  • Financial Distress Prediction
  • Lasso Logistic Regression
  • Machine Learning
  • Data Mining
  • Tehran Stock Exchange
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