Comparative Analysis of Bayesian Approaches and Variant Methods in the Financial Field

It is important for researchers to select the most appropriate model for specific financial tasks. This comparative analysis describes the strengths, limitations, and trade-offs of Bayesian approaches and variant methods. Moreover, this study aims to compare Bayesian approaches and their variant methods in the financial field and assess their effectiveness and relevancy for stock price prediction. This study starts with a brief overview of Bayes’ Theorem, Naïve Bayes (NB), multinomial Naïve Bayes (NBM), and Gaussian Naïve Bayes (GNB). In the prediction of the Brazilian stock market, NBM demonstrates superior performance compared to other models in terms of accuracy, recall, and F1-score. In the meantime, the GNB and linear discriminant analysis-based combined model (GNB_LDA) exhibits superior performance in accuracy and F1-score, while the model incorporates GNB, standardization, and factor analysis (GNB_Z-Score_FA) excels in mean specificity in the prediction of seven different stocks. This study also suggests further exploration of hybrid models that combine the strengths of Bayesian models with other techniques to enhance financial analysis and decision-making.