Dataset for Financial Performance and Machine Learning Analysis: Evidence from Mongolian Firms

This dataset provides firm-level panel data used in the study “Financial Flexibility and Firm Performance: Evidence from Machine Learning Models”. The dataset is designed to support empirical analysis of the impact of financial flexibility on firm performance using both econometric and machine learning approaches. It includes a comprehensive set of variables such as firm size, leverage ratio, liquidity measures, profitability indicators, and other financial characteristics. The data were collected from publicly available financial statements and processed into a structured panel dataset. Data preprocessing steps include cleaning missing values, variable transformation, and normalization. The dataset is suitable for regression analysis, panel data modeling, and machine learning techniques such as Random Forest, Gradient Boosting, and other predictive models. This dataset enables full replication of the study results and can be reused for further academic research in corporate finance, financial risk modeling, and data-driven decision-making.