An Enhanced Ensemble Machine Learning Methods in Financial Marketing

In recent years, financial institutions (FIs) have been hesitant when it comes to using supply chain finance (SCF), which is short for supply chain financing. This is because SCF stands for supply chain financing, which is used to address the financing needs of small and medium-sized businesses. One of the most difficult and time-consuming tasks in the industry of financial planning is currently the assessment of the credit risk that is posed by small and medium-sized enterprises (SME). On the other hand, the requirements of such forecasting are not something that can be provided by employing conventional models of credit risk. This article uses a stacking model, which takes into account both technical aspects and macroeconomic data, in order to make predictions regarding the movement of the stock price index in reference to the price that was in effect not too long ago. A recursive application of the cross-validation procedure is carried out in order to produce the input for the second-level classifier. This is done to mitigate the risk of the model being overly constrained by the data. Logistic regression and its regularized version are used as meta-classifiers in the second layer to the fundamental classifier to class learning. The outcome of our research is an exhaustive stacking architecture that has the potential to be applied in the banking sector.