Quantifying Market Signals: An Ensemble IoT-Driven Approach to Stock Market Prediction

In this groundbreaking research, we utilized seven different machine learning models to predict fluctuations in the stock market. To conduct this research, we leveraged a comprehensive dataset of the S&P 500 from 1950 to the present. Our ensemble consists of a Decision Tree Classifier, Random Forest Classifier, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Long Short-Term Memory (LSTM), Logistic Regression, and Linear Regression. We conducted a comprehensive analysis, examining various performance metrics such as precision, recall, F1 score, accuracy, and AUC-ROC. Additionally, we presented a detailed side-by-side comparison using visually captivating elements like confusion matrices, precision–recall curves, ROC curves, and informative bar charts. Our investigation delved into the distinct advantages and disadvantages of each model, uncovering their refined capabilities for stock market predictions. The dataset, carefully curated by Yahoo Finance, contains closing prices and binary indicators denoting price movements. We seamlessly integrated essential preprocessing measures such as feature scaling and standardization into the research narrative. Our findings highlight subtle variations in model performance, offering profound insights into their efficacy in navigating the complexities of stock market dynamics. Beyond showcasing the predictive advantages of the models, this study significantly enriches the literature with a comprehensive, visual, and quantitative comparative analysis.