Application of wavelet transform image processing technology in financial stock analysis

Abstract

Traditional mathematical models have problems in the analysis of financial stocks that are not intuitive enough. In order to improve the intuitiveness of the stock forecasting model, based on the image recognition technology, this study normalizes the image and performs feature recognition with grayscale images. At the same time, this paper ignores the small fluctuations and combines the characteristics of stock images to remove the drying process and proposes an algorithm model based on feature recognition. In addition, in order to improve the image accuracy, the model combines the edge extraction technology to extract features, which reflects the actual rise and fall of the stock. Finally, this paper designs experiments to conduct research and analysis. The research results show that the proposed method has certain effects and can provide theoretical reference for subsequent related research.