This thesis aims to investigate the effect of changes in the board of directors of companies on their financial state. Utilizing a specialized dataset comprising text reports, financial features and changes in a company’s board, we aim to understand these effects both in terms of the financial robustness of the underlying company but also the cascading effect of these changes on the other companies that have overlapping members on their boards of directors. We will study this effect from a data-based perspective and create predictive ML models to validate our hypotheses on downstream applications, such as bankruptcy prediction.
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