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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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| مراجع | ||
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Aggarwal, A., Gupta, I., Garg, N., & Goel, A. (2019). Deep Learning Approach to Determine the Impact of Socio-Economic Factors on Bitcoin Price Prediction. Twelfth International Conference on Contemporary Computing (PP. 1-5). India: IEEE, doi: 10.1109/IC3.2019.8844928.
Ameri, M. H., & Belgurian. M. (2016). Thesis on Damrani's investigation of large and block transactions in the Tehran Stock Exchange market. (PP. 1-5). doi:10.30495/jdaa.2023.1962972.1043.
Albuquerque, R., & Schroth, E. (2010). Quantifying private benefits of control from a structural model of block trades. Journal of Financial Economics, 96, (PP. 33-55). doi:10.1016/j.jfineco.2009.12.003.
Alikhani, A., & Soroushyar, A. (2022). Comparison of different types of profits and their effect on excess stock return, quarterly journal of judgment and decision making in accounting and auditing. Types of Profit and Their Effect on Stock Returns, 2, (PP. 39-58). doi:10.30495/jdaa.2023.1962972.1043.
Basu, N., Paeglis, I., & Toffanin, M. (2017). Reading between the blocks. Journal of Corporate Finance, 45, (PP. 294-317). doi:10.1016/j.jcorpfin.2017.04.017.
Nahhal, B. (2023). Effect of Block Trading on the Moroccan Stock Exchange. African Development Finance Journal, 5, (PP. 33-52). http://journals.uonbi.ac.ke/index.php/ad.
Cho, K., Van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning Phrase Representations Using Rnn Encoder-Decoder For Statistical Machine Translations. Arxiv. 1406. 1078. doi:10.48550/arXiv.1406.1078.
Azarkh, D., & Pacheca, J. (2019). Market Trends Block Trades. Lexis Practice Advisor, (PP.2-5). https://www.stblaw.com/docs/default-source/Publications/lexis-practice-advisor-market-trends-2018_19_block-trades.
Dong, L., Uchida, K., & Hou, X. (2014). Block trade targets in China. Journal of Corporate Finance, 25, (PP. 188-201). doi: 10.1016/j.jcorpfin.2013.12.001.
De, S., & Jindra, J. (2012). Why newly listed firms become acquisition targets. Journal of Banking & Finance, 36, (PP. 2616-2631., doi: 10.1016/j.jbankfin.2012.06.006.
Dehghan, N. M., Izdi, H., & Alidousti, F. (2016). Investigating the impact of corporate governance indicators on the rate of return on assets of Tehran Stock Exchange banks. Forth International Conference on New Researches in Management, Economics and Accounting, (PP. 1-5). Germany. https://scholar.conference.ac/index.php/download/file/8533-The-effect-of-corporate-governance-index-rate-of-return-on-assets-of-banks-Tehran-Stock-Exchange.
Pérez-Soba, l., Martínez-Cañete, A. R., & Márquez–De-La-Cruz, E. (2021). Private Benefits From Control Block Trades In The Spanish Stock Exchange. The North American Journal of Economics and Finance, 56, (PP.1-35). doi: 10.1016/j.najef.2020.101338.
Etamadi, H., Dehghani, T., Azar, A., Anwari, R., & Asghar, A. (2013). Designing a model for pricing control blocks of stocks. Scientific Research Quarterly. Journal of Financial Knowledge of Securities Analysis, 6, (PP. 71-84). https://jfksa.srbiau.ac.ir/article_2625_67b3f887a30f67a91e72058711d10fc3.
Fama, E., & French, K. (1993). Common Risk Factors In The Returns On Stocks and Bonds. Journal of Financial Economics, 33, (PP. 3-56). doi:10.1016/0304-405X(93)90023-5.
Gul-Arzi, G. H., & Badi-Dast, I. (2021). Investors' perception of market returns and its effect on the formation of collective behavior with an approach based on collective adherence to beta. Research Paper on Development and Capital, 7, (PP. 87-100). https://www.sid.ir/fa/VEWSSID/J_pdf/4043314011205.
Ghasemi-Dodran, S., Asgari, H., & Pakmaram, A. (2018). Experimental test of the information content of Ayers and Olson's adjusted cash flow model in companies listed on the Tehran Stock Exchange. First National Conference on Management, Accounting and Economics with an Eemphasis on Marketing Regional and Global, (PP. 1-15). Iran:.https://sid.ir/paper/899376/fa.
Ghaemi, M. H., & Masoumi, J. (2013). Determining the length of the event time range for event research in Tehran Stock Exchange. Accounting Knowledge Magazine, 2, (PP. 25-7). https://ensani.ir/fa/article/download/231734.
Hatami, N. (2018). Combining neural networks for stock forecasting. Journal of Economic Sciences, 7, (PP. 61-80). https://www.sid.ir/frontend/image/citecounter.svg
Harris, M. & Raviv, A. (1988). Corporate control contests and capital structure. Journal of Financial Economics, 20, (PP. 55-86). doi:10.1016/0304-405X(88)90040-2.
Jensen, M.C. (1986). Agency costs of free cash flow, corporate finance, and takeovers. The American Eeconomic Review, 76, (PP.323-329). https://www.jstor.org/stable/1818789.
Premaratne, G., & Balasubramanyan, L. (2003). Stock Market Volatility. A Survey of North America, Europe and Asia. National University of Singapore, Department of Economics, (PP. 1-21). doi:10.2139/ssrn.375380.
Mehraban, M. R., Tehrani, R., & Jamshidi, H. (2017). Analysis of the role of block transactions in creating abnormal returns and the effect on unsystematic fluctuations in the Tehran Stock Exchange. Scientific Quarterly of Asset Management and Financing, 8, (PP. 1-5). https://amf.ui.ac.ir/article_23827_13e60b541f3b0c451d3eedbe967e5997.pdf
Mehrani, S., Moradi, M., Iskandar, H., & Hashemi, M. M. J. (2014). Institutional Ownership and Financial Flexibility. Scientific Research, 7, (pp. 43-56). https://faar.ctb.iau.ir/article_520149_db5ff4d90858d38955209218d1651388.
Osmani, F., Cheshmi, A., Salehnia, N., & Ahmadi, Sh. M. T. (2023). The response of stock returns of various Iranian industries to inflation and interest rates with the Panel-ARDL approach. Planning and Budget Research Quarterly, 28, (PP. 75 -53). http://jpbud.ir/article-1-2155-fa.html.
Meng, Q., Song, X., Liu, C., Wu, Q., & Zeng, H. (2020). The Impact Of Block Trades On Stock Price Synchronicity From China. Sciencedirect, 68, (PP. 239-253). doi:10.1016/j.iref.2020.04.009.
Selmi, R., Mensi, W., Hammoudeh, S., & Bouoiyour, J. (2018). Is Bitcoin a hedge, a safe haven or a diversifier for oil price movements? A comparison with gold. Energy Economics, 74, (PP. 787-801). doi:10.1016/j.eneco.2018.07.007.
Refenes, A. N., Zapranis, A., & Francis, G. (1994). Stock performance modeling using neural networks: a comparative study with regression models. Neural networks, 7, (PP. 375-388). doi: 10.1016/0893-6080(94)90030-2.
Salehnejad, S. H., & Ghayor, V. (2019). The effect of the rate of return on assets and the rate of return on equity and the financial leverage of the shares of companies admitted to the Tehran Stock Exchange. Researcher (Management), 7, (PP. 17- 27). https://www.sid.ir/paper/151497/fa#downloadbottom.
Syedkhani, R., Mohammadi, M. A., & Amini, P. (2021). Investigating the ability of operational cash flows in assessing the performance of companies with an emphasis on the quality of disclosure during periods of financial crisis. Researches in Financial Accounting and Auditing, 13, (PP. 147-176). https://www.sid.ir/paper/393082/fa#downloadbottom.
Sediqi, A. H., & Sajdinejad, A. (2018). Presenting an approach based on deep learning to detect fraud in financial payment systems. Information Management Scientific Quarterly, 5, (PP. 458-458). https://www.aimj.ir/article_101840_c517de6fd03df4131f4585d463d0713c.
Tehrani, R., & Flowerjani, R. R. (2007). Examining the ratio of book value to market value as a risk substitute variable using the leverage approach. Journal of Accounting and Auditing, 15, (PP. 37-54). https://ensani.ir/fa/article/14254.
Tu, T. T., & LIAO, C. W. (2020). Block trading based volatility forecasting: An application of VACD-FIGARCH model. Journal of Asian Finance, Economics and Business, 7, PP(59-70). doi: 10.13106/jafeb.2020.vol7.no4.59.
Sun, Y., & Ibikunle, G. (2017). Informed trading and the price impact of block trades: A high frequency trading analysis. International Review of Financial Analysis, 54, (PP. 114-129). doi:10.1016/j.irfa.2016.07.005.
Yim, J. (2002, June). A comparison of neural networks with time series models for forecasting returns on a stock market index. In International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (PP. 25-35). Berlin: Springer Berlin Heidelberg. doi:10.1007/3-540-48035-8_4.
Zhang, G. P. (2003). Time Series Foricasting Using A Hybrid Arima And Neural Network Model. Neurocompuling, 50, (PP. 159-175). doi:10.1016/S0925-2312(01)00702-0.
Zare, M. H. & Nilchi, M. (2018). Comparative evaluation of Markowitz approach with a hybrid method to form an optimal portfolio using DNN deep learning and gravity search algorithm. Journal of Financial Management Perspective, 9, (PP. 165-188). https://ensani.ir/fa/article/download/437244. | ||
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