Forecasting Financial Time Series Using Deep Learning Networks: Evidence from Long-Short Term Memory and Gated Recurrent Unit

  • سال انتشار: 1401
  • محل انتشار: مجله مالی ایران، دوره: 6، شماره: 4
  • کد COI اختصاصی: JR_IJFIFSA-6-4_004
  • زبان مقاله: انگلیسی
  • تعداد مشاهده: 411
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نویسندگان

Mohammadreza Ghadimpour

MSc., Department of Financial Engineering, Faculty of Industrial Engineering, Khajeh Nasir Toosi University of Technology, Tehran, Iran.

Seyed babak Ebrahimi

Assistant Professor, Department of Financial Engineering, Faculty of Industrial Engineering, Khajeh Nasir Toosi University of Technology, Tehran, Iran. Pardis St. Molasadra Ave., Vanak Sq, Tehran ۱۹۳۹۵-۱۹۹۹, Iran

چکیده

The ability to predict the stock market and analyze market trends is invaluable to researchers and anyone interested in investing. However, this task is a challenging problem due to a large number of parameters and unpredictable noise that may affect the stock price. To overcome this issue, researchers have employed numerous approaches such as Moving Average (MA), Support Vector Machine (SVM), and Neural Networks. With technological advances, deep learning methods have become popular in processing time-series data. In this paper, we compare two recently introduced deep learning models, namely a Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), in forecasting daily movements of the Standard & Poor (S&P ۵۰۰) index using the daily closing price of this index from ۱۴/۵/۱۹۹۱ to ۱۴/۵/۲۰۲۱. Results show that both models are effective and accurate in stock market prediction. In this case study, the mean squared error (MSE) and mean absolute error (MAE) for the GRU model are slightly lower than the LSTM model; hence, GRU outperformed the LSTM model despite its simpler structure. The results of this study are applicable in various instances where it is challenging to identify patterns among large volumes of unstructured data, such as medical data analysis, text mining, and financial time series modeling.

کلیدواژه ها

Machine Learning, Recurrent Neural Network, Long Short-Term Memory, Gated Recurrent Unit, Financial time series

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