Sanjiv Das

Sanjiv Das, Santa Clara University

Goals-Based Wealth Management

Given any set of exogenously provided efficient portfolios, we develop a dynamic programming algorithm that constructs an optimal portfolio trading strategy to maximize the probability of attaining an investor’s specified goal wealth at the end of a designated timeframe. Our algorithm can also accommodate periodic infusions or withdrawals of any size with no degradation in runtime performance. We explore how the terminal wealth distribution is sensitive to restrictions on the segment of the portfolio’s efficient frontier made available to the investor. Because our algorithm’s optimal strategy is on the efficient frontier, allowed to depend on the investor’s wealth, and allowed to depend on the investor’s individual goals and specifications, we show that it soundly beats the performance of target date funds for attaining investors’ goals. These optimal goals-based wealth management strategies are useful for modern day behavioral-based FinTech offerings, both advisor-driven or robo-driven, and are also applicable for pension funds.

Bio:

Sanjiv Das is the William and Janice Terry Professor of Finance and Data Science at Santa Clara University's Leavey School of Business. He previously held faculty appointments as Professor at Harvard Business School and UC Berkeley. He holds post-graduate degrees in Finance (M.Phil and Ph.D. from New York University), Computer Science (M.S. from UC Berkeley), an MBA from the Indian Institute of Management, Ahmedabad, B.Com in Accounting and Economics (University of Bombay, Sydenham College), and is also a qualified Cost and Works Accountant (AICWA). He is a senior editor of The Journal of Investment Management, co-editor of The Journal of Derivatives and The Journal of Financial Services Research, and Associate Editor of other academic journals. Prior to being an academic, he worked in the derivatives business in the Asia-Pacific region as a Vice-President at Citibank. His current research interests include: machine learning, social networks, derivatives pricing models, portfolio theory, the modeling of default risk, systemic risk, and venture capital. He has published over ninety articles in academic journals, and has won numerous awards for research and teaching. His recent book "Derivatives: Principles and Practice" was published in May 2010 (second edition 2016). He currently also serves as a Senior Fellow at the FDIC Center for Financial Research.