Abstract: Goals-Based Wealth Management (GBWM) is a new retirement planning paradigm that is gaining traction. We develop a dynamic programming methodology that seeks to maximize investor outcomes over multiple, potentially competing goals (such as upgrading a home, paying college tuition, or maintaining an income stream in retirement), even when financial resources are limited. Unlike Monte Carlo approaches currently in wide use in the wealth management industry, our approach uses investor preferences to dynamically make the optimal determination for fulfilling or not fulfilling each goal and for selecting the investor’s investment portfolio. This can be computed quickly, even for numerous investor goals spread over different or concurrent time periods, where each goal may be all-or-nothing or may allow for partial fulfillment. The probabilities of attaining each (full or partial) goal under the optimal scenario are also computed, so the investor can ensure the algorithm accurately reflects their preference for the relative importance of each of their goals. This approach vastly outperforms buy and hold strategies and target-date funds, widely used in the wealth management industry. The presentation will cover a sequence of 3 papers that develop a mathematical and practical approach to GBWM.
About the Speaker: Sanjiv Das is the William and Janice Terry Professor of Finance and Data Science at Santa Clara University's Leavey School of Business, and an Amazon Scholar at AWS. 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), PGDM 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, Associate Editor of Management Science and other academic journals, and is on the Advisory Board of the Journal of Financial Data Science. 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: portfolio theory and wealth management, machine learning, financial networks, derivatives pricing models, the modeling of default risk, systemic risk, and venture capital. He has published over a hundred and ten 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).
