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Niazi, Naimeh, Razavi, Hamideh. (1403). Prediction the Choice of Financing for Start-ups using Machine Learning Algorithms and Behavioral Biases. پژوهش‌های فلسفی -کلامی, 10(1), 238-261. doi: 10.22091/jemsc.2024.11203.1200
Naimeh Niazi; Hamideh Razavi. "Prediction the Choice of Financing for Start-ups using Machine Learning Algorithms and Behavioral Biases". پژوهش‌های فلسفی -کلامی, 10, 1, 1403, 238-261. doi: 10.22091/jemsc.2024.11203.1200
Niazi, Naimeh, Razavi, Hamideh. (1403). 'Prediction the Choice of Financing for Start-ups using Machine Learning Algorithms and Behavioral Biases', پژوهش‌های فلسفی -کلامی, 10(1), pp. 238-261. doi: 10.22091/jemsc.2024.11203.1200
Niazi, Naimeh, Razavi, Hamideh. Prediction the Choice of Financing for Start-ups using Machine Learning Algorithms and Behavioral Biases. پژوهش‌های فلسفی -کلامی, 1403; 10(1): 238-261. doi: 10.22091/jemsc.2024.11203.1200

Prediction the Choice of Financing for Start-ups using Machine Learning Algorithms and Behavioral Biases

Engineering Management and Soft Computing
دوره 10، شماره 1 - شماره پیاپی 18، آبان 2024، صفحه 238-261    اصل مقاله (1.03 M)
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.22091/jemsc.2024.11203.1200
نویسندگان
Naimeh Niazi؛ Hamideh Razavi*
Department of Industrial Engineering, Faculty of Engineering, University of Ferdowsi of Mashhad, Mashhad, Iran
چکیده
The aim of this paper is to predict financing methods to support decision-making for startup founders and their investors. Initially, factors influencing the choice of financing methods, including structural, demographic, and behavioral factors, were identified. These factors were then assessed using a questionnaire consisting of 32 items, which was sent online to startup founders. Based on 70 responses received and using algorithms including binary matching, classification chains, label power set, K-nearest neighbors, extreme gradient boosting, cluster boosting algorithm and random forest, the financing methods chosen by startups were predicted. Comparison of the results from the algorithms shows that the boosting ensemble algorithm, with an F1 score of 89 and precison of 85%, predicts the selected financing methods on the test dataset better than other algorithms. Additionally, data analysis indicates that startups are more inclined towards personal funding methods, which aligns with the prevalence of loss aversion bias among entrepreneurs. Following loss aversion, overconfidence, anchoring, and illusion of control biases were the most frequent among entrepreneurs.
کلیدواژه‌ها
Startup؛ Financing؛ Ensemble learning؛ Cluster boosting algorithm (Catboost)؛ Cognitive Biases
عنوان مقاله [English]
پیش‌بینی شیوه تأمین مالی استارتاپ‌ها با استفاده از الگوریتم‌های یادگیری ماشین و درنظرگرفتن سوگیری‌های رفتاری
نویسندگان [English]
نعیمه نیازی؛ حمیده رضوی
کارشناسی‌ارشد، مهندسی صنایع، دانشکده مهندسی دانشگاه فردوسی مشهد، مشهد، ایران. رایانامه: naimeniazi@gmail.com
چکیده [English]
هدف از این مقاله پیش‌بینی شیوه‌های تأمین مالی برای پشتیبانی تصمیم‌گیری‌ بنیان‌گذاران استارتاپ‌ها و سرمایه‌گذاران آن‌هاست. در ابتدا عوامل موثر در انتخاب روش تامین مالی شامل عوامل ساختاری، جمعیت‌شناختی و رفتاری شناسایی شدند. سپس این عوامل با استفاده از پرسشنامه‌ای مشتمل بر 32 گویه، به‌صورت آنلاین برای بنیان‌گذاران استارتاپ‌ها ارسال گردید. بر اساس 70 پاسخ دریافت‌شده و استفاده از الگوریتم‌های تطابق دودویی، زنجیره‌های طبقه‌بندی، مجموعه توان برچسب‌ها، K-نزدیک‌ترین همسایه، تقویت گرادیان شدید، تقویت دسته و جنگل تصادفی به پیش‌بینی شیوه‌های تامین مالی که استارتاپ‌ها انتخاب می‌کنند، پرداخته شد مقایسه نتایج حاصل از الگوریتم‌ها نشان می‌دهد که الگوریتم تقویت دسته با شاخص ارزیابیF1 معادل 89 و دقت معادل 85 درصد بهتر از سایر الگوریتم‌ها، روش‌های تامین مالی منتخب را بر روی مجموعه داده آزمون پیش‌بینی می‌نماید. همچنین تحلیل داده‌ها نشان می‌دهد که استارتاپ‌ها بیشتر به روش پرداخت شخصی برای تامین مالی تمایل دارند که با فراوانی سوگیری زیان‌گریزی در میان کارآفرینان همسو است. همچنین پس از سوگیری زیان‌گریزی، سوگیری‌های اعتمادبه‌نفس بیش‌از حد، لنگرانداختن و توهم کنترل، بیشترین فراوانی را در میان کارآفرینان داشتند.
کلیدواژه‌ها [English]
استارتاپ, تأمین مالی, یادگیری گروهی, الگوریتم تقویت دسته, سوگیری‌های رفتاری
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