Exploratory Machine Learning Predictors of Financial Performance: Evidence from Listed Egyptian Fintech Ventures

This study provides an exploratory predictive analysis to examine how different dimensions of digital infrastructure—capital market development, digital payment adoption, e-commerce penetration, and market volatility—predict the financial performance metrics of fintech ventures in Egypt. Using panel data from ten fintech ventures listed on the Egyptian Stock Exchange over the period 2017–2023, the research employs Random Forest machine learning algorithms alongside Logistic Regression as a baseline comparator. Feature importance analysis identifies the most significant predictors of profitability across four performance metrics: gross revenue, sales growth, gross margin, and net profit margin. This study employs Random Forest with five-fold cross-validation. Hyperparameters were optimized via grid search, and feature importance scores are reported with cross-validation standard deviations. To address panel structure concerns, we additionally employ leave-one-firm-out cross-validation. All findings reflect predictive associations only; no causal claims are made due to potential reverse causality. Findings show that capital market development emerges as the most important predictor across all profitability metrics, accounting for 45% of feature importance for net profit margin and 42% for gross revenue (mean importance across five folds; SD = 0.07–0.08). Digital payment adoption exhibits a paradoxical dual association—positively associated with revenue and margins through operational efficiency (38% importance for gross margin; SD = 0.08) while negatively associated with sales growth (22% importance; SD = 0.10). Gross online sales show limited predictive efficacy, affecting only gross margin. Market volatility correlates solely with sales growth. Random Forest consistently outperforms Logistic Regression across all models, with accuracy rates ranging from 68% to 76% (compared to a chance level of 50% and a majority-class baseline of 52–58%). Due to the limited sample of 70 firm-year observations, these findings must be interpreted as strictly exploratory and hypothesis-generating; they apply uniquely to publicly listed fintech firms on the Egyptian Stock Exchange and cannot be generalized to private, early-stage, or unlisted fintech startups without further empirical validation.