Comparative Analysis of S&P 500 and NASDAQ: A Machine Learning Approach to Understanding Differential Market Sensitivity and Growth Stocks

Abstract

The purpose of this study is to gain a deeper understanding of the dynamics and market sensitivity of technology stocks by constructing and predicting random stock portfolios in the S&P500 and NASDAQ indices using machine learning models. The article employs a rolling time window cross-validation approach to train the models, ensuring optimization based on continuously updated data. By comparing the predicted results with actual outcomes, the article analyze whether technology stocks possess characteristics of growth stocks and how key events impact the price fluctuations of technology stocks. The results also indicate the need to dynamically adjust the learning rate based on market characteristics, volatility, trends, and other factors to enhance the adaptability of the machine learning models in different conditions and better uncover the latent information within diverse data samples. Overall, the findings of this study underscore the importance of dynamically adjusting the learning rate of machine learning models under various market conditions, providing a fresh perspective for financial application research. Future research can further explore and develop cutting-edge methods related to optimizing the learning rate of models, such as adaptive learning rates, multi-task learning, and incremental learning, to more effectively cope with different market situations and similar issues in other fields.