Boosting Financial Market Prediction Accuracy With Deep Learning and Big Data
Year: 2024
Type: article
Abstract: The accuracy of financial market trend prediction directly impacts investors' decisions and financial institutions' risk management, making it a focal point of research in the financial domain. While traditional statistical methods lay the foundation for market forecasting, they still face limitations in handling complex, nonlinear, and non-stationary financial time series data with accuracy and adaptability. Time series analysis plays a pivotal role in financial market prediction, revealing historical patterns and future trends within the data. Addressing the limitations of existing methods, this study proposes a deep learning-based CEEMDAN-CNN-LSTM model. Leveraging the CEEMDAN decomposition method to capture the multi-frequency components of time series, CNN extracts spatial features, and LSTM learns temporal dependencies. Experimental results demonstrate that the performance of the CEEMDAN-CNN-LSTM model surpasses traditional models on standardized evaluation metrics such as RMSE, MAE, and MAPE across four major financial datasets.
DOI: 10.4018/joeuc.358454
Authors: Zhuo Fang, Shuyong Wang
Institution: Dongbei University of Finance and Economics
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4404130526