Asset management companies have been among the largest investors in the financial market. With $89 trillion in assets under management, this industry has been experiencing rapid changes. For example, many firms started enlarging the technological capabilities to improve decision-making, data management, and client experience.

How do these structural trends change the job positions in the asset management industry? What kind of skills are essential nowadays given the technological breakthroughs and change in investors’ preferences? Can we understand better the investment style and fund culture using the human capital information? This research project aims to understand these questions. To address these research questions, I plan to use statistical analysis of novel datasets combined with interviews with investment professionals.

This is an UNPAID research project.

Faculty Advisor

  • Professor: Wei Cai
  • Center/Lab: Accounting
  • Location: Manhattanville Campus
  • Wei Cai joined Columbia University in 2020. Her research broadly investigates the role of information and incentives in shaping behavior, decision-making, and performance in complex contracting environments. For example, she examines how corporate leaders and managers can deliberately design and shape organizational culture, and improve organizational outcomes through innovative management control systems. She uses multiple research methods including statistical analyses of archival data sources, field experiments, and surveys. She closely collaborates with practitioners and collects unique data that can provide important managerial implications for the design of management control systems in shaping desirable organizational outcomes.

Professor Cai received a Doctor of Business Administration (Accounting and Management) from Harvard Business School. Prior to earning her DBA, she worked as a senior financial advisor at Ernst & Young in New York.

Project Timeline

  • Earliest starting date: 3/1/2022
  • End date: 8/31/2022
  • Number of hours per week of research expected during Spring/Summer 2022: ~10
  • Number of hours per week of research expected during Summer 2022: ~20

Candidate requirements

  • Skill sets: • Software skills: Stata/R/Python • Data scraping/Machine learning/Textual analysis • Statistics/Econometrics/Causal inference
  • Student eligibility: freshman, sophomore, junior, senior, master’s
  • International students on F1 or J1 visa: eligible
  • Academic Credit Possible: Yes