Abstract:
When it comes to stock price prediction, machine learning has grown in popularity. Accurate
stock prediction is a very difficult activity as financial stock markets are unpredictable
and non-linear in nature. With the advent of machine learning and improved computational
capabilities, programmed prediction methods have proven to be more effective in stock price
prediction. Extreme gradient boosting(XGBoost) is the variant of the gradient boosting machine.
XGBoost, an ensemble method of classification trees, is investigated for the prediction
of stock prices based on the fundamental analysis. XGBoost outperformed the competition
and had higher accuracy. The developed XGBoost model proved to be an effective model
that accurately predicts the stock market trend, which is considered to be much better than
conventional non-ensemble learning techniques.
