Abstract
Free cash flows is one of firm valuation methods and useful information contents to all parties. However, There is not enough to investigate for free cash flow forecasting. In this study, the performance of machine learning algorithms for predicting corporate free cash flow using financial big data of public companies in Korea was compared. The support vector machine algorithm, which is known for its excellent predictive power, was mainly discussed. It is a generalization of the idea of maximal margin classifier that separates the entities in the p－dimensional space using a p－1 dimensional hyperplane. In addition, the logistic regression model that is connected with the support vector machine was also considered. As a result of the analysis, the overall predictive power of the logistic regression model was the best. Nevertheless, there was no significant difference between the models considered in this study, which calls for future studies incorporating various machine learning algorithms well－suited for financial attributes.
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