Wang, HongmingMisicko, GregorySiminoff, Ellen F2022-11-2320222022-11-222022Siminoff, Ellen F. 2022. Developing A Board Power Algorithm and Stock Performance Correlation. Master's thesis, Harvard University Division of Continuing Education.29996338https://nrs.harvard.edu/URN-3:HUL.INSTREPOS:37373598This Master’s Thesis analyzes the relationships among public company board members, inspecting how these relationships correlate with longer-term stock price performance. The Thesis inspects the depth and breadth of these connections using graph theory and network analysis, relying on the Neo4j database and its algorithms, and, subsequently, the indexed power or relevance in predicting stock market returns. The data comprise the last decade's stock prices and the composition of the boards behind the companies who produced them. As part of this analysis, a robust scoring algorithm was created, which quantifies director power or interconnectedness. The system then evaluated whether (or not) high levels of indexed PowerScores among boards drove better long-term stock performance. There did not appear to exist a high mapping correlation such that any given high- power director related directly with the constituencies of other high-power directors. This Thesis further discusses factors that contributed to or detracted from performance and positions a company and its directors to be in the top decile of performers.application/pdfenboard of directorsgraph theoryneo4jnetwork analysispower score algorithmstock performanceComputer scienceComputer engineeringEconomicsThesis or Dissertation2022-11-23