Comparative analysis of VARMA method and LSTM in predicting stock price
Publish place: 3rd International Conference on Challenges and New Solutions in Industrial Engineering, Management and Accounting
Publish Year: 1401
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
CSIEM03_516
تاریخ نمایه سازی: 14 آذر 1401
Abstract:
Nowadays, financial markets are affected by various social and political events. This makes forecasting even more important. Forecasting financial markets help stockholders to sell or buy the stock, when necessary, which leads to increased profits. Since there are many forecasting methods, this paper applies two methods, Vector Autoregressive Moving Average (VARMA) and Long Short-Term Memory (LSTM) for forecasting. Forecasting was done on the stock price data of two Apple and Microsoft companies, which include four features: Open, High, Close, and Low. By comparing these two prediction models using Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) criteria, it is concluded that LSTM method is more suitable for stock price prediction than VARMA.
Keywords:
Stock price , Vector autoregressive moving average , Recurrent neural network , Long short-term memory.
Authors
Mohadeseh Fatehi
Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran
S.M.T Fatemi Ghomi
Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran
S.M.R Kazemi
Department of Industrial Engineering, College of Engineering, Birjand University of Technology, Birjand, Iran