Predictive Research of the US Stock Market: Comparative Analysis Based on Multiple Data Science Methods

Abstract

This study thoroughly investigates the effectiveness of numerous data science techniques in forecasting trends inside the US stock market using a wide range of machine learning (ML) and deep learning (DL) approaches. Among the investigated techniques are decision trees, random forests, long short-term memory networks (LSTM), and support vector machines (SVM). The study underlines both the benefits and drawbacks of every model in real-world settings by means of numerous comparisons, therefore evaluating its predictive capability. The results show that although simpler models such random forests and decision trees have a degree of interpretability and are easier for investors to understand, they frequently fail to portray the complexity of market behavior. On the other hand, creative models like LSTMs and fusion techniques show shockingly great capacity to analyze and predict complex market processes, so far much above conventional approaches. For legislators seeking improved knowledge of market behavior and trends as well as for investors seeking portfolio optimization, these findings have major ramifications. By means of improved prediction models, stakeholders can make better judgments, so enhancing possibly financial returns. At last, this study provides perceptive analysis that enhances our ability to project on the always shifting terrain of the financial markets.