MACHINE LEARNING MODELS FOR UNDERSTANDING CORPORATE RISKS AND PERFORMANCE PATTERNS

This research addresses the question of how the size of a firm influences its financial performance and risk-takingbehavior by analyzing the first 100 companies of the FTSE index over the period 2018-2023. This period ischaracterized by economic uncertainty due to Brexit and the Covid-19 pandemic. The main question is whetherlarge firms perform better and manage their risks more efficiently than smaller ones.Previous research has been criticized for using only traditional methodologies like OLS regression, whereas thisstudy employs machine learning techniques, thus, moving one step further. The researchers employed sevendifferent models: Logistic Regression, Support Vector Machine (SVM), Random Forest, Decision Tree, GradientBoosting, Extra Trees, and K-Nearest Neighbors (KNN). Stock price changes were used as a proxy for firmperformance, stock return volatility as a measure for risk-taking, and log of market capitalization as a proxy forfirm size.The results firm size is highly positively correlated with financial performance showing that large firms generallymore profitable and stable. Moreover, they engage in fewer unstructured risk activities, which implies that theirfinancial operations are more intentional and under control. Logistic Regression was the best-performing modelin terms of predictive accuracy with a 97% success rate, among all machine learning models tested, showing theeffectiveness of machine learning in financial research.This paper is a great contribution in terms of new insights from the UK market and it addresses the gap in theexisting literature which is mostly based on the US and Asia. Besides, it proves that machine learning can be agreat tool in corporate finance analysis by discovering the complex patterns that traditional methods may fail touncover.