| Abstrakt: |
In the financial market, the use of machine learning algorithms for predicting stock prices is particularly advantageous. Stock prices, a critical factor influencing decisions made by traders, investors, and large firms, are subject to volatility influenced by various external factors, rendering prediction a complex endeavor. Accurate predictions, however, play a pivotal role in optimizing profitability within the realm of stock trading. Machine learning algorithms, endowed with the capacity to autonomously learn and improve, are well-suited to this task when integrated with sentiment and technical indicators. They possess the capability to process vast volumes of historical data, unveiling patterns that may elude immediate human perception. This paper presents an analysis that incorporates sentimental and technical analyses and employs machine learning algorithms viz. Logistic Regression, Random Forest, and Decision Tree for the prediction of stock prices. These algorithms, when combined with technical and emotional analysis, serve to efficiently forecast market behavior. [ABSTRACT FROM AUTHOR] |
