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طراحی مدلی جهت پیش‌بینی ارزش‌گذاری معاملات بلوکی با تاکید بر شبکه عصبی مصنوعی GRU درصنعت | ||
| راهبرد مدیریت مالی | ||
| مقاله 10، دوره 12، شماره 2 - شماره پیاپی 45، تیر 1403، صفحه 249-274 اصل مقاله (544.17 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22051/jfm.2024.44487.2847 | ||
| نویسندگان | ||
| عادله بحرینی1؛ مریم اکبریان فرد* 2؛ مهدی خوشنود3 | ||
| 1دانشجوی دکترای تخصصی، گروه مهندسی مالی، واحد رشت، دانشگاه آزاد اسلامی، رشت، ایران | ||
| 2استادیار گروه حسابداری، واحد صومعه سرا، دانشگاه آزاد اسلامی، صومعه سرا، ایران | ||
| 3استادیار گروه حسابداری، واحد رودسر و املش، دانشگاه آزاد اسلامی، رودسر، ایران | ||
| چکیده | ||
| پیش‌بینی ارزش‌گذاری معاملات بلوکی سبب می‌شود تا بازار بتواند به شیوه ای کارآمد کنترل بر شرکت‌ها را ارزیابی کند. هدف این پژوهش اندازه‌گیری شاخص‌های اثر‌گذار بر معاملات بلوکی در سه صنعت فعال در بورس اوراق بهادار تهران و میزان تاثیراین شاخص‌ها بر ارزش‌گداری معاملات بلوکی با بکارگیری آزمون Rmse بر روی داده‌هایTest موردمطالعه قرارگرفته است. با بهره‌گیـری از شبکه عصبی یادگیری عمیق، مدلGru روی صنایعی که تعداد جامعه‌اش در بورس زیاد است، (صنایع فلزات اساسی :فولاد، خودروو ساخت قطعات :خساپا، مواد ومحصولات دارویی دالبر) ازمجموعه شرکت‌های پذیرفتـه شـده درسـازمان بـورس اوراق بهادارتهران برای دوره زمانی 1390تا1400 استفاده شده است. مدیران صنایع شرکت‌های پذیرفته شده در بورس اوراق بهادار تهران با آگاهی از چگونگی تاثیر این مدل بر ارزش‌گذاری معاملات بلوکی می‌توانند روند تغییرات قیمت سهام بلوکی را کنترل نموده ریسک سرمایه‌گذاری در شرکت و در نهایت ریسک تأمین مالی را برای شرکت پایین آورند. درسطح تفکیکی صنایع، نتایج تاثیر شاخص‌های مالی بر ارزش‌گداری معاملات بلوکی درهرصنعت باصنایع دیگر متفاوت است کـه بیانگر استقلال صنایع از یکدیگر است.در مدل ارائه شده با اندازه گیری ارزش‌گذاری معاملات بلوکی به مدیران صنایع در بورس و استفاده‌کنندگان صاحبان سهام و سهامداران معاملات بلوکی در ارزیابی بهتر قیمت‌گذاری کمک می‌کند. | ||
| کلیدواژه‌ها | ||
| بازده سهام؛ ارزش‌گداری معاملات بلوکی؛ صنعت؛ شبکه‌های عصبی یادگیری عمیق؛ مدل Gru | ||
| عنوان مقاله [English] | ||
| Designing a Model for Predicting Valuation of Block Trade Transactions with a Focus on GRU Artificial Neural Network in the Industry | ||
| نویسندگان [English] | ||
| Adeleh Bahreini1؛ Maryam Akbaryanfard2؛ Mehdi Khoshnood3 | ||
| 1Phd Student, Department of Finance engineering, Rasht Branch, Islamic Azad University, Rasht, Iran | ||
| 2Assistant Professor, Department of Accounting, Somehsara Branch, Islamic Azad University, Somehsara, Iran | ||
| 3Assistant Professor, Department of Accounting, Rudsar and Amlesh Branch, Islamic Azad University, Rudsar, Iran | ||
| چکیده [English] | ||
| Predicting the valuation of blockTrade transaction allows the market to evaluate control over companies in an efficient manner.In this research, by measuring the indicators affecting block transactions in three active industries in Tehran Stock Exchange During the period of 1390 to the end of 1400, on a daily basis with utilization a deep learning neural network, specifically the GRU model. The study focused on industries with a significant number of market participants, namely basic metals (steel), automotive and parts manufacturing (Khodro), and pharmaceuticals (Darou). The results of the hypothesis testing indicate that, at three industry level, Nine variables significantly affect blockTrade transaction valuation: stock returns, block size, trading volume, company size, price fluctuations, industry returns, market returns, institutional ownership, and market-to-book ratio It affects the valuation of block transactions. At the separate level of industries, the results of the effect of financial indicators on the valuation of blockTrade transaction in each industry are different from other industries, which indicates the independence of industries from each other. The findings of this research will help the managers of industries in the stock market and the users of the valuation indices of blockTrade transactions in better evaluation of pricing. | ||
| کلیدواژه‌ها [English] | ||
| Stock returns, Block Trade transaction valuation, Industry, Deep learning neural networks, GRU model | ||
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