Machine Learning applied to Projected Financial Statements (PFS)

Bellido-Zea C.; Villalobos-Meneses B.; Alfaro-Rodriguez C.; Grados-Espinoza A.; Teran-Dianderas C.; Hoyos-Rivas F.; Ramirez-Veliz F. · Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology (2023)

Resumen

Projected financial statements represent one of the most reliable sources when it comes to making decisions involving the company's long-term performance. Therefore, finding methods to optimize their preparation and accuracy is the holy grail of financial accounting. The objective of this research is to use machine learning in projected financial statements, in order to obtain more accurate data through training in a tetradimensional space or also called Euclidean space of n dimensions.

Instituciones: Universidad Nacional del Callao