Stock Price Forecasting in Iran Stock Market: A Comparative Analysis of Deep-learning Approaches
- سال انتشار: 1402
- محل انتشار: فصلنامه بین المللی وب پژوهی، دوره: 6، شماره: 2
- کد COI اختصاصی: JR_IJWR-6-2_003
- زبان مقاله: انگلیسی
- تعداد مشاهده: 327
نویسندگان
Graduate School of Management and Economics, Sharif University of Technology, Tehran, Iran
School of Railway Engineering, Iran University of Science and Technology, Tehran, Iran
Graduate School of Management and Economics, Sharif University of Technology, Tehran, Iran
School of Railway Engineering, Iran University of Science and Technology, Tehran, Iran
چکیده
The capital market plays a crucial role within a country's financial structure and is instrumental in funding significant, long-term projects. Investments in the railway transport industry are vital for boosting other economic areas and have a profound impact on macroeconomic dynamics. Nonetheless, the potential for delayed or uncertain returns may deter investors. Accurate predictions of rail company stock prices on exchanges are therefore vital for making informed investment choices and securing sustained investment. This study employs deep learning techniques to forecast the closing prices of MAPNA and Toucaril shares on the Tehran Stock Exchange. It utilizes deep neural networks, specifically One-dimensional Convolutional Neural Networks (۱D-CNN), Long Short-Term Memory (LSTM) networks, and a combined CNN-LSTM model, for stock price prediction. The effectiveness of these models is measured using various metrics, including MAE, MSE, RMSE, MAPE, and R۲. Findings indicate that deep learning methods can predict stock prices effectively, with the CNN-LSTM model outperforming others in this research. According to the results, The CNN-LSTM model reached the highest R۲ of ۰.۹۹۲. Also, based on criteria such as MAE, MSE, RMSE, and MAPE the best results belong to LSTM (Kaggle-modified) with ۵۲۱.۷۱۵, ۶۵۱۱۱۹.۱۹۴, ۸۰۶.۹۲۰, and ۰.۰۲۸, respectively.کلیدواژه ها
time series prediction, Iran Stock Market, Railway Stock, Deep Learning, wavelet transformationاطلاعات بیشتر در مورد COI
COI مخفف عبارت CIVILICA Object Identifier به معنی شناسه سیویلیکا برای اسناد است. COI کدی است که مطابق محل انتشار، به مقالات کنفرانسها و ژورنالهای داخل کشور به هنگام نمایه سازی بر روی پایگاه استنادی سیویلیکا اختصاص می یابد.
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