Presenting the Forecasting Model of Analysis of Capital market Signals Using (CEEMD-DL(LSTM)) approach

Abstract

Non-linearity feature and high fluctuations in financial time series have made the forecasting of stock prices and financial indicators face many challenges. However, recent developments in deep learning (DL) models with structures such as long-short-term memory (LSTM) and convolutional neural network (CNN) have made improvements in the analysis of this type of data. Another approach that can be effective in the analysis of financial time series is the decomposition of capital market signals through algorithms such as complete integrated empirical mode decomposition (CEEMD). Considering the importance of forecasting in the financial markets, in this research, by combining deep learning models and complete integrated empirical mode decomposition (CEEMD), The hybrid CEEMD-DL(LSTM) model has been used to forecast the Tehran Stock Exchange index. In this regard, the daily data of the total index of the Tehran Stock Exchange in the period of 2012/12/01 – 2022/02/20 be used and the results were compared with the results of competing models based on efficiency measurement criteria. Based on the obtained results, the introduced model (CEEMD-DL(LSTM)) has higher efficiency and accuracy in stock exchange index forecasting. Accordingly, the use of this model in financial forecasts is suggested.

Keywords

• deep learning models (dl)
• complete integrated empirical mode decomposition (ceemd)
• tehran stock exchange index
• long-short-term memory (lstm)
• convolutional neural network (cnn)