Stock Market Prediction with Gaussian Naïve Bayes Machine Learning Algorithm

The stock market is one of the key sectors a country’s economy. It provides investors with an opportunity to invest and gain returns on their investment. Predicting very challenging task has attracted serious interest from researchers many fields such as statistics, artificial intelligence, economics, finance. An accurate prediction reduces investment risk in market. Different approaches have been used predict performances Machine learning (ML) models are typically superior those statistical econometric models. ability Gaussian Naïve Bayes ML algorithm price movement not addressed properly existing literature, hence this work attempt fill that gap by evaluating performance GNB when combined different feature scaling extraction techniques prediction. set up were ranked using Kendall’s test concordance for various evaluation metrics used. results indicated that, predictive model based integration Linear Discriminant Analysis (GNB_LDA) outperformed all other considered three four (i.e., accuracy, F1-score, AUC). Similarly, algorithm, Min-Max scaling, PCA produced best rank specificity results. In addition, better technique than it does standardization