Utilizing Comprehensive RNN Neural Network Model for Valuation of BlockTrade Transactions

Subject Areas : Information Technology in Engineering Design (ITED) Journal

Adeleh Bahreini ¹ , Maraym Akbaryan fard ² , Mehdi KHoshnood ³

1 - Phd Student Department of Finance engineering, Rasht Branch, Islamic Azad University, Rasht, Iran
2 - Assistant Professor, Department of Accounting, Somehsara Branch, Islamic Azad University, Somehsara, Iran (corresponding author)
3 - Assistant Professor, Department of Accounting, Rudsar and Amlash Branch, Islamic Azad University, Rudsar, Iran

Received: 2023-11-11 Accepted : 2024-01-17 Published : 2024-02-20

Keywords: BlockTrade transaction valuation, deep learning neural networks, RNN model, Stock Returns,

Abstract :

In the past, studies have focused on the influencing factors of companies' block trades, but the valuation of block transactions for the first time using Rnn deep learning model, It challenges the trader's trading behavior and strategy based on a measured value. This evaluation helps to choose the right combination of block Trade shares to maximize the return on the investment time horizon. The purpose of this research is to review the news and The information obtained from the financial reports of companies admitted to the Tehran Stock Exchange in the form of 15 financial indicators and to find the effect of these indicators on the valuation of block Trade transactions has been studied using the rmse test on the test data. For this purpose, the financial information of 64 companies from the group of companies accepted in the Tehran Stock Exchange for the period of 1390-1400 have been used. As a result, this criterion can help us on the one hand. It allows us to identify the hidden motivations in the purchase of blocks from the perspective of the buyer, and the purpose of block Trade transactions is to achieve control and achieve financial or strategic benefits.