Predicting Company Growth by Econophysics informed Machine Learning

Autor:	Tao, Ruyi, Liu, Kaiwei, Jing, Xu, Zhang, Jiang
Rok vydání:	2024
Předmět:	Computer Science - Computational Engineering Finance and Science Computer Science - Machine Learning Economics - General Economics Physics - Physics and Society
Druh dokumentu:	Working Paper
Popis:	Predicting company growth is crucial for strategic adjustment, operational decision-making, risk assessment, and loan eligibility reviews. Traditional models for company growth often focus too much on theory, overlooking practical forecasting, or they rely solely on time series forecasting techniques, ignoring interpretability and the inherent mechanisms of company growth. In this paper, we propose a machine learning-based prediction framework that incorporates an econophysics model for company growth. Our model captures both the intrinsic growth mechanisms of companies led by scaling laws and the fluctuations influenced by random factors and individual decisions, demonstrating superior predictive performance compared with methods that use time series techniques alone. Its advantages are more pronounced in long-range prediction tasks. By explicitly modeling the baseline growth and volatility components, our model is more interpretable. Comment: 18 pages, 12 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2410.17587 Zobrazit plný text záznamu View this record from Arxiv