ارائه مدلی برای پیش‌بینی ارزش‌گذاری معاملات بلوکی با رویکرد شبکه عصبی مصنوعی LSTM

محورهای موضوعی : بازارها و نهادهای مالی

عادله بحرینی ¹ ( گروه مهندسی مالی، واحد رشت، دانشگاه آزاد اسلامی، رشت، ایران. )
مریم اکبریان فرد ² ( گروه حسابداری، واحد صومعه سرا، دانشگاه آزاد اسلامی، صومعه سرا، ایران. )
مهدی خوشنود ³ ( گروه حسابداری، واحد رودسر و املش، دانشگاه آزاد اسلامی، رودسر، ایران. )

تاریخ دریافت : 1402/04/25 تاریخ پذیرش : 1402/08/15 تاریخ انتشار : 1402/09/07

کلید واژه: نوسانات قیمت, ارزش‌گذاری معاملات بلوکی, شبکة عصبی یادگیری عمیق, مدلLSTM,

چکیده مقاله :

هدف: در غالب پژوهش‌های پیشین در ایران و سایر کشورهای دیگر توانمندی سیستم‌های هوشمند در پیش‌بینی متغیرهای اقتصادی و مالی به‌ویژه قیمت سهام تأیید شده است، اما در ارزش‌گذاری معاملات بلوکی برای اولین‌بار محاسبه می‌گردد. هدف پژوهش حاضر بررسی نتایج رویدادها و اطلاعات از گزارش‌‌های مالی شرکت‌های پذیرفته شده در بورس اوراق بهادار تهران در قالب 15 شاخص مالی و یافتن میزان تأثیرگذاری این شاخص‌ها بر ارزش‌گذاری معاملات بلوکی با استفاده از آزمون Rmse بر روی داده‌های Test موردمطالعه قرار گرفته است.روش‌شناسی پژوهش: بدین منظور از اطلاعات مالی 64 شرکت ازمجموعه شرکت‌های پذیرفته شده درسازمان بورس اوراق بهادارتهران برای دوره زمانی 1390 تا1400 استفاده شده است. فرضیه‌ی تحقیق با بهره‌گیری از شبکه عصبی یادگیری عمیق مدل LSTM آزمون شده‌است.یافته‌ها: شبکة عصبی LSTM به جهت توانمندی بالا در آموزش داده‌ها و وزن‌های مناسب به این داده‌ها و خلق مسیری که با سرعت و دقت نتایج قابل‌قبولی جهت پیش‌بینی ارزش‌گذاری معاملات بلوکی دارد.اصالت / ارزش‌افزوده علمی: در مدل ارائه شده با اندازه‌گیری ارزش‌گذاری معاملات بلوکی، قیمت این معاملات، اثرات اطلاعات و نقدینگی معاملات با اندازه‌بزرگ را واپایش خواهیم نمود.

چکیده انگلیسی:

Objective: The capability of intelligent systems in predicting economic and financial variables, particularly stock prices, has been confirmed in previous research in Iran and other countries. However, the valuation of block transactions is calculated for the first time in this study. The aim is to investigate the outcomes and information from the financial reports of listed companies on the Tehran Stock Exchange using 15 financial indices and determine the impact of these indices on the valuation of block transactions by employing the RMSE test on the Test dataset.Research Methodology: For this purpose, financial information from 64 companies within the accepted companies of the Tehran Stock Exchange for the period from 1390 to 1400 has been utilized. The research hypothesis is tested using the Long Short-Term Memory (LSTM) deep learning neural network model.Findings: The LSTM neural network, due to its high capability in training data, appropriate weights for these data, and creating a path that efficiently and accurately produces acceptable results for predicting the valuation of block transactions.Originality/Value: In the proposed model, by measuring the valuation of block transactions, we will scrutinize the prices of these transactions and the effects of information and liquidity in large-sized transactions.

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