UCLA

According to M Firth, PMY Fung, OM Rui (2006), the stock market is pretty sensitive to the top management turnover. Therefore, it is important to monitor the top management turnover in a very short time. This thesis attempts to use statistical and machine learning techniques to identify the Chief Executive Officer in a specific company. The natural language processing techniques we used may be able to show who is the Chief Executive Officer from millions of words in financial reports within few minutes. The whole process comes with four sectors: data collection, manipulation, named entity recognition and relationship analysis. We demonstrate this method with Tesla’s proxy statements (code: DEF 14A) from the U.S. Securities and Exchange Commission. The output shows that we can use both the named entity recognition algorithm with word2vec algorithm to detect the relationship between a job title and a human name.