Uncovering key predictors of high-growth firms via explainable machine learning

TL;DR

This study combines financial, technological, and network data using explainable machine learning to improve predictions of high-growth firms, revealing key features like patent value and firm size thresholds.

Contribution

It introduces a multi-category feature analysis with explainable AI, demonstrating the enhanced predictive power of combined financial, technological, and network features.

Findings

Financial, technological, and network features improve prediction accuracy.

Maximum patent value and patent count are key non-financial predictors.

Firm size influences high-growth probability with a threshold effect.

Abstract

Predicting high-growth firms has attracted increasing interest from the technological forecasting and machine learning communities. Most existing studies primarily utilize financial data for these predictions. However, research suggests that a firm's research and development activities and its network position within technological ecosystems may also serve as valuable predictors. To unpack the relative importance of diverse features, this paper analyzes financial and patent data from 5,071 firms, extracting three categories of features: financial features, technological features of granted patents, and network-based features derived from firms' connections to their primary technologies. By utilizing ensemble learning algorithms, we demonstrate that incorporating financial features with either technological, network-based features, or both, leads to more accurate high-growth firm predictions compared to using financial features alone. To delve deeper into the matter, we evaluate the predictive power of each individual feature within their respective categories using explainable artificial intelligence methods. Among non-financial features, the maximum economic value of a firm's granted patents and the number of patents related to a firms' primary technologies stand out for their importance. Furthermore, firm size is positively associated with high-growth probability up to a certain threshold size, after which the association plateaus. Conversely, the maximum economic value of a firm's granted patents is positively linked to high-growth probability only after a threshold value is exceeded. These findings elucidate the complex predictive role of various features in forecasting high-growth firms and could inform technological resource allocation as well as investment decisions.