Predicting Company Growth by Econophysics informed Machine Learning

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Tác giả: Xu Jing, Kaiwei Liu, Ruyi Tao, Jiang Zhang

Ngôn ngữ: eng

Ký hiệu phân loại: 006.31 Machine learning

Thông tin xuất bản: 2024

Mô tả vật lý:

Bộ sưu tập: Metadata

ID: 204568

Comment: 18 pages, 12 figuresPredicting 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.
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