Analyzing Financial Risk Prediction Using Deep Learning Algorithms: A Comparative Study of LSTM and CNN Models

Abstract

Financial risk prediction is a critical challenge in the financial industry, which has been improved with the development of deep learning models, particularly LSTM and CNN. These models enable more accurate and efficient predictions of market fluctuations by analyzing complex and large financial data. To compare the performance of LSTM and CNN models in financial risk prediction based on accuracy, processing speed, and computational complexity. This study employs a descriptive analysis method to examine and compare two deep learning models, LSTM and CNN. Historical financial data and previous related studies were reviewed to assess their performance. The comparison criteria included prediction accuracy, processing speed, and applicability in different financial scenarios. The results indicated that the LSTM model, due to its ability to retain and recall long-term temporal data, performs better in long-term predictions and trend analysis in financial markets. In contrast, the CNN model, with faster processing speed and a simpler structure, is more effective in short-term predictions and real-time data analysis. Both models have unique strengths and weaknesses, making each suitable for specific data types and analytical needs. LSTM is more appropriate for long-term risk predictions, while CNN excels in short-term forecasts. Combining these two models can enhance prediction accuracy and mitigate financial risks. Future research could focus on developing hybrid methods and optimizing the performance of these models.