Predicting Company Growth by Econophysics informed Machine Learning
This addresses the problem of inaccurate and non-interpretable company growth predictions for stakeholders like strategists and risk assessors, though it appears incremental by combining existing approaches.
The paper tackled predicting company growth by integrating an econophysics model with machine learning to capture intrinsic mechanisms and fluctuations, resulting in superior predictive performance, especially in long-range tasks, compared to time-series-only methods.
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.