Abstract:
Corporate Governance is a very well-known and accepted technique for the assessment
of company performance in the stock market and to predict and ensure that the company’s
stock value will not fall. This practice throughout the globe helps ensure the safety of
investor’s money and also keeps company’s stakeholders and shareholders on board with
the truth about the real strength of the company and its worth. The board of directors in the
corporate governance keeps transparency between the managers and the owners of the
company. However, it has been seen that the composition and characteristics of this board
affect the overall performance of the company. Therefore, in order to assess the
performance of this board of governors/directors in light of the characteristics and
composition of board, I have implemented a new technique of machine learning that can
assess if the company’s stock value will crash in the stock market or not, depending upon
the characteristics and composition of the board. This thesis uses data from Bloomberg
Platform, Osiris and Corporate Library covering 500 banks and financial institutions to
validate our algorithm. Moreover, in this thesis, I have compared the empirical results of
this algorithm with the baseline known algorithms of SVM and logistic regression. Results
show that the proposed algorithm is more accurate than the baseline methods. The thesis
concludes with the effects and role of corporate governance features in stock price crash
prediction.
