Classification of Return on Equity (ROE) using Machine learning techniques

Music City Center 

Return on Equity (ROE) is one of the most watched financial ratios by shareholders and potential investors. Negative ROE can communicate a negative message to investors. It is important to find financial ratios that influence ROE and to find the best Machine Learning technique that can be used to predict it. We thus used four machine-learning techniques (Naive Bayes, Logistic regression, Random Forest and K Nearest Neighbour) to identify the determinants and to predict the ROE. The imbalance data was sourced from the Integrated Real-time Equity System (IRESS) which comprise of all companies in the Johannesburg Stock Exchange (JSE) that were listed in 2019. The imbalance data was balanced using original observations from previous years, using SMOTE and ROSE oversampling methods. The model evaluation metrics that were used include sensitivity, specificity, precision, F1 score and accuracy. The identified predictors were net profit margin (NPM), Interest cover (IC), earning per share (EPS), earning yield (EY) and price per earning (PPE). Random Forest dominated performance in all datasets and performed well even on imbalance dataset.

Returns on equity (ROE)

Machine Learning techniques

SMOTE and ROSE oversampling

Machine learning classifiers.

ROE predictors,

IRESS 

Section on Statistical Learning and Data Science