Financial Time Series Analysis and IPO Pricing Model Based on Machine Learning

This article studies machine learning based financial time series analysis and Initial Public Offering (IPO) pricing models, aiming to improve the accuracy of financial forecasting and the efficiency of IPO pricing through advanced machine learning techniques. Firstly, wavelet transform is used for data denoising, then a stacked autoencoder (SAE) is used to extract deep features, and finally a long short-term memory network (LSTM) is used to construct a prediction model. The experimental results show that compared with the traditional time series analysis method, this method shows better performance in dealing with nonlinear and high-noise financial data, and can predict financial indicators such as stock prices more accurately. In addition, the study also shows how to apply the machine learning model to the IPO pricing process, so as to comprehensively capture complex factors such as market dynamics and investor sentiment, thus improving the accuracy and efficiency of IPO pricing. These findings provide more effective decision-making tools for financial market participants, and provide new directions and ideas for future financial time series analysis and IPO pricing model research.