Predicting Legal Systems: an Artificial Neural Network Approach With Statistical Analysis for Comparative Study of Civil Law and Common Law Countries

Abstract

This study compares countries with common law with countries with civil law systems and investigates the possibility of predicting legal systems using artificial neural networks (ANNs). The OLS model, ANOVA, ANN, and Tensor Flow are used in the research to analyze the data. The goal is to find out how board characteristics and country legislative frameworks affect how European corporations disclose their social performance. The performance of a hidden layer with five nodes is best, according to the ANN model. The model's accuracy throughout testing and validation is 0.750. The confusion matrix shows that, of the four observations in the test set, three were correctly categorized as "Civil law" and one was incorrectly categorized as "Common law."

To evaluate the model's efficacy, evaluation metrics are computed. The model's accuracy is 0.750, which represents a prediction success rate of 75%. For the "Civil law" class, the recall (true positive rate) is 1.0, indicating that all "Civil law" cases are correctly identified. Metrics for the "Common law" class, however, are not available due to the scant amount of data that is available. The prevalence of countries with common law and civil law systems is compared in the ANOVA analysis. As shown by the computed F-value of 0.482, there is less variance inside each legal system than there is between the two. There is no statistically significant difference in frequency between the two legal systems, according to the p-value of 0.495. Overall, the research's conclusions imply that social performance disclosure between countries with common law and civil law systems differs only slightly. The neural network model's network weights provide insight into the importance of different features in prediction.