A Hybrid Approach to Machine Learning and Data Mining for Predictive Modeling in Finance

The aim of this paper is to apply hybrid machine learning (ML) and data mining (DM) techniques for financial predictive modeling to improve the predictive performance and adaptability of financial predictions. Proposed Model However, time-series analysis/regression models used in traditional financial predictions can not effectively capture these non-linear and dynamic financial dataset. In order to ameliorate these constraints, we combine different ML & DM algorithms such as Random Forests, K-means clustering and Artificial Neural Networks (ANN) into a strong hybrid model in a way that contributes to increase the overall predictive performance. In regards to obtaining the performance metrics like accuracy, precision, recall, and AUC-ROC, hybrid method is better than any method from ML and DM domain separately. This segmentation and eventually applying the supervised learning algorithms like Random Forest and ANN by these models makes the algorithm able to predict such data like stock prices, market trends, and credit risk more reliable. Still, concerns about computation complexity as well as interpretability in hybrid models persist. These models do need further research to make them more perfect for real time financial applications especially for emerging markets where data quality is questionable. The conclusion of this paper indicates the promising capability of hybrid approaches in enhancing the quality of financial forecasting through scalable, adaptive, and accurate models to address the dynamics of the contemporary financial markets.