Machine Learning Prediction Approach For Financial Forecast System In Stock Exchange Marketing Management

Financial forecasting is a crucial task in stock exchange marketing management. In recent years, machine learning algorithms have gained popularity as a powerful tool for financial forecasting due to their ability to handle complex patterns in the data. In this context, this paper proposes a novel machine learning prediction approach for financial forecasting using ARIMA and Gradient Boosting algorithms. ARIMA is a widely used time series model that can capture the temporal dependencies in the data. Modelling using ARIMA uses Those three factors, namely trends in the data may be captured by adjusting the following orders: differencing (d), autoregression (p), and moving average (q). Modelling using ARIMA can handle time series, both stationary and non-stationary data, making it a suitable choice for financial forecasting. Gradient Boosting, to contrast, is a ML algorithm that can pick up complex patterns in the numbers by iteratively Modelling using decision trees. It works by modifying the importance of individual data points to improve the accuracy of the predictions. Gradient Boosting can handle both regression and classification tasks and is particularly useful for predicting non-linear relationships in the data. The proposed approach combines the strengths of ARIMA and Gradient Boosting to improve the accuracy of financial forecasting. The ARIMA model is first used to pre-process the time series data and to extract the temporal dependencies. The residuals obtained from the ARIMA model are then used as input to the Gradient Boosting algorithm, which learns the non-linear relationships in the data. The proposed approach was evaluated using real-world financial data from the S&P 500 index. The The findings supported the strategy that was presented outperformed both the ARIMA and Gradient Boosting algorithms alone regarding precision and forecasting performance. All things considered, the planned approach achieved an accuracy of 96.4%, which is significantly higher than the 91.3% and 92.7% achieved by the ARIMA and Gradient Boosting algorithms, respectively. In conclusion, the proposed approach provides a novel solution for financial forecasting in stock exchange marketing management. By combining the strengths of ARIMA and Gradient Boosting, the proposed approach can improve the accuracy of financial forecasting and provide valuable insights for decision-making in the financial industry.