Data Fusion for Improved Stock Closing Price Prediction: Ensemble Regression Approach.

The stock market is a complex and dynamic industry that has garnered the attention of experts who seek to understand its various trends. Accurately predicting stock prices is crucial for investors to minimize their risk of losing money. However, due to the volatile and non-linear nature of financial stock markets, this is a challenging task. This study aimed to address the challenge of accurately predicting stock prices by utilizing an ensemble regression approach, which combines multiple sources of data. The proposed approach was evaluated using Tesla company data over a four-year period and demonstrated its efficiency in predicting stock closing prices. The results showed that the ensemble regression approach was able to accurately predict stock prices under different scenarios, handle fluctuations, anticipate sudden changes, and predict both simple and radical changes in stock prices. This study highlights the potential of machine learning techniques and increased computational capabilities in improving prediction methods for the complex and dynamic stock market industry.