Identification of Investment-Ready SMEs: A Machine Learning Framework to Enhance Equity Access and Economic Growth

Periklis Gogas (), Theophilos Papadimitriou, Panagiotis Goumenidis, Andreas Kontos and Nikolaos Giannakis
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Periklis Gogas: Department of Economics, Democritus University of Thrace, 69100 Komotini, Greece
Theophilos Papadimitriou: Department of Economics, Democritus University of Thrace, 69100 Komotini, Greece
Panagiotis Goumenidis: Department of Economics, Democritus University of Thrace, 69100 Komotini, Greece
Andreas Kontos: Department of Economics, Democritus University of Thrace, 69100 Komotini, Greece
Nikolaos Giannakis: Department of Economics, Democritus University of Thrace, 69100 Komotini, Greece

Forecasting, 2025, vol. 7, issue 3, 1-36

Abstract: Small and medium-sized enterprises (SMEs) are critical contributors to economic growth, innovation, and employment. However, they often struggle in securing external financing. This financial gap mainly arises from perceived risks and information asymmetries creating barriers between SMEs and potential investors. To address this issue, our study proposes a machine learning (ML) framework for predicting the investment readiness (IR) of SMEs. All the models involved in this study are trained using data provided by the European Central Bank’s Survey on Access to Finance of Enterprises (SAFE). We train, evaluate, and compare the predictive performance of nine (9) machine learning algorithms and various ensemble methods. The results provide evidence on the ability of ML algorithms in identifying investment-ready SMEs in a heavily imbalanced and noisy dataset. In particular, the Gradient Boosting algorithm achieves a balanced accuracy of 75.4% and the highest ROC AUC score at 0.815. Employing a relevant cost function economically enhances these results. The approach can offer specific inference to policymakers seeking to design targeted interventions and can provide investors with data-driven methods for identifying promising SMEs.

Keywords: SMEs; investment readiness; machine learning; SAFE dataset; equity financing; economic resilience; gradient boosting; random forest; logistic regression; SVC; VIM (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2025
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