Predicting Company Growth using Scaling Theory informed Machine Learning

TL;DR

This paper introduces a novel machine learning framework that combines scaling theory with data-driven models to improve the prediction of company growth, capturing both structural trends and volatile fluctuations.

Contribution

The paper presents the STIML framework that integrates a scaling-based growth model with machine learning, extending growth modeling to multiple financial indicators and analyzing their predictability.

Findings

Scaling-based models capture trend-driven predictability.

Fluctuation predictability depends on company size and volatility.

Macro variables contribute less to growth prediction.

Abstract

Predicting company growth is a critical yet challenging task because observed dynamics blend an underlying structural growth trend with volatile fluctuations. Here, we propose a Scaling-Theory-Informed Machine Learning (STIML) framework that integrates a scaling-based growth model to capture the mechanism-driven average trend, together with a data-driven forecasting model to learn the residual fluctuations. Using Compustat annual financial statement data (1950--2019) for 31,553 North American companies, we extend the growth model beyond assets to multiple financial indicators, and evaluate STIML against growth model-only and purely data-driven baselines. Across 16 target variables, we show that company growth exhibits a clear separation between trend-driven predictability and fluctuation-driven predictability, with their relative importance depending strongly on company size and volatility. Interpretability analyses further show that STIML captures multivariate dependencies beyond simple autocorrelation, and that macroeconomic variables contribute significantly less to predictive performance on average. Moreover, we find the scaling-based growth model overlooks asymmetric deviations, which instead contain the structured and learnable signals, suggesting a path to refine mechanistic models.