[Legacy Report] SMCS Chapter Seminar on FinTech Trends: The Rise of Robo-Advisors
SMCS Chapter Seminar on
FinTech Trends: The Rise of Robo-Advisors
Speaker: Dr. Zhen Guo
Place: ECE 202, NJIT
Time: 7:30pm, June 30, 2016 (Thursday)
Abstract:
Over the last six years, a segment of FinTech that has received a lot of attention, is automated investment services, also known as Robo-Advisors. Robo-advisors are a class of financial adviser that provides automated portfolio management with various data driven algorithms based on modern portfolio theory.
In principle Modern Portfolio Theory (the mean-variance approach of Markowitz) offers a solution to this problem once the expected returns and covariances of the assets are known. The major limitation of this theory is: difficult to come up with reasonable estimates of expected returns.
Black–Litterman overcame this problem by not requiring the user to input estimates of expected return; instead it assumes that the initial expected returns are whatever is required so that the equilibrium asset allocation is equal to what we observe in the markets. The user is only required to state how his assumptions about expected returns differ from the market's and to state his degree of confidence in the alternative assumptions. From this, the Black–Litterman method computes the desired (mean-variance efficient) asset allocation.
Asset allocation is by far the biggest driver of client returns, Robo advisors are typically designed to seek the optimal balance between return and risk with constraints of certain limitation, such as transaction cost, the exposure cap/floor, and etc. By utilizing the historical market weighted cap and correlation between asset classes, certain optimization algorithms come into play, such as quadratic optimizer, mean variance model, and etc. There are many open research topics and/or issues on portfolio optimization to be addressed or enhanced, and we believe there are enormous room for machine learning techniques to contribute.
Bio-Sketch
Dr. Zhen Guo is a senior investment professional in a top 3 global alternative asset management firm with $94 billion of asset under management, a market leader in both size and investment return performance. Dr. Guo serves as a principal/director in a dynamic credit multi-strategy, multi-currency, cross assets portfolio management division with reliable and attractive annualized return in average 18% ~ 20% in the past 6 years. His strategies include relative value dynamics; structured credit arbitrage; Fundamental credit picking; and rates hedging strategies. With proven track record of index beating performance of the portfolios, Dr. Guo is overseeing, constantly generating alpha respected by peers. He previously worked for Royal Bank of Canada and Freddie Mac, covering the asset classes such as structure products and Bonds/Rates between 2008 and 2012. He holds a PhD degree from New Jersey of Technology (2006) and Bachelor degree from University of Science and Technology of China, Hefei, China (1999).
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