Application Of Machine Learning Algorithms to Free Cash Flows Growth Rate Estimation

Machine learning (ML) demonstrates superior accuracy in financial time-series forecasting compared to traditional statistical models. While most studies focus on applying ML algorithms to high-frequency pricing data, the availability of fundamental financial data is limited as it is generated quarterly. This paper investigates the performance of nine ML algorithms in small sample data sets, against an ARIMA model — frequently used for financial time-series forecasting — serving as a benchmark. Results obtained from 100 US companies indicate that the majority of ML algorithms exhibit low error rates on the test set, outperforming benchmark results. Notably, the k-nearest neighbor algorithm achieves the highest prediction accuracy among the algorithms considered, even with only 33 data observations, while avoiding overfitting.