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Cash Holdings Prediction Using Decision Tree Algorithms and Comparison with Logistic Regression Model

Abstract Cash is a very important property of an enterprise, but offers the lowest return of any asset. Nevertheless, firms do not invest all assets into higher-paying assets and hold part of their cash, especially firms in the high-tech electronics industry. Firms in this industry hold considerable amounts of cash because they have high expenses and recovery is uncertain, potentially leaving the firm without sufficient funds. It is therefore necessary to prepare a certain amount of cash. This study uses a decision tree algorithm including J48, LMT, Random Forest, REP Tree, Simple CART, Extra Tree, and BF Tree to measure the performance of predictions. This study has three experiments: (1) testing the predictive ability of the decision tree algorithm, (2) testing the decision tree algorithm with performance improvements, and (3) determining the best decision tree forecast rate comparison using the logistic regression model. The experiments indicate that the random forest has the highest and best prediction rate comparison with the logistic regression model.