Enhancing Financial Forecasting with Random Forest: A Performance Evaluation
Keywords:
Stock Price Prediction, Random Forest (RF), Machine Learning, Financial Forecasting, Time Series Analysis, Investment StrategyAbstract
Stock price prediction is an important and challenging task because it can help investors to make investment strategy decisions and risk management. The popular ensemble learning method Random Forest (RF) is utilized in this study to predict the stock price. We choose the RF model as it is less prone to overfit compared to XGBoost and can work with many attributes in our dataset. Introduction In this study, using historical stock data to predict future prices with four principal parameters: open price, close price, trading volume and moving average. There are selected few selection parameters which makes an impact how the price moves up or down, what market is making trends. Understanding that the RF model is based on a dataset of recorded stock prices and everyone contributes to its set of features. The RF learns complex, non-linear relationships in the data with a decision-tree-based ensemble approach. We evaluate model performance based on MAE, RMSE etc to keep validity of the models and make predictions which can generalize well. Analysing this through comprehensive analysis we show how well (and not so well) Random Forests can predict stock prices showcasing possible strengths and constraints to the model. Any technology that makes a difference in such an ecosystem is indeed worth mentioning because whoever contributes to it can instantly be plunged into the ocean of success, as well said: “Imminent minds think alike” this article will uncover how tech innovations have opened new doors and enhanced stock market analysis offering strong predictive methodology for people who are looking at investment through data-driven lenses
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