SQA Fuzzy Day Conference 2018
Abstracts and Biographies:
Robert Stamicar, Head of Multi-Asset Class Research at Axioma Inc.
“A CVaR Scenario-Based Framework for Minimizing Downside Risk in Multi-Asset Class Portfolios”
Multi-asset class (MAC) portfolios can be composed of investments in equities, fixed income, commodities, foreign exchange, credit, derivatives, and alternatives such as real estate and private equity. The return for such nonlinear portfolios is asymmetric with significant tail risk. The traditional Markowitz mean–variance optimization (MVO) framework, which linearizes all the assets in the portfolio and uses the standard deviation of return as a measure of risk, does not always accurately measure the risk for such portfolios. We consider a scenario-based conditional value at risk (CVaR) approach for minimizing the downside risk of an existing portfolio with MAC overlays. The approach consists of two phases: Phase 1 uses Monte-Carlo simulations to generate the asset return scenarios, and Phase 2 incorporates the return scenarios in a scenario-based convex optimization model to generate the overlay holdings. We illustrate the methodology in two examples involving the hedging of an equity portfolio with index puts and the hedging of a callable bond portfolio with interest rate caps. We compare the CVaR approach with parametric MVO approaches that linearize all the instruments in the MAC portfolio and show that the CVaR approach generates portfolios with better downside risk statistics; and further, it selects hedges that produce more attractive risk decompositions and stress test numbers—tools commonly used by risk managers to evaluate the quality of hedges.
Sarah Jiang, Managing Director at AQR Capital Management
“Craftsmanship Alpha: An Application to Style Investing”
Successful investing requires translating sound investment concepts into actual trading strategies. We study many implementation details that portfolio managers should pay attention to when constructing multistyle portfolios across asset classes. This presentation focuses on portfolio implementation choices, including how to transform signals into portfolio weights and how to combine multiple styles, optimization, risk control, and trading. While these kinds of decisions apply to any type of investment strategy, they are particularly important in the context of style investing, where the craftsmanship choices that can impact investment success are ubiquitous. In fact, the skillful targeting and capturing of style premia may constitute a form of alpha on its own—one that we refer to as “craftsmanship alpha.”
Jason MacQueen, Northfield Information Services
“Smart Beta Bond Portfolios”
In October 2015 we held a webinar on Smart Equity Portfolios. Although Smart Beta ETFs have become very popular, our contention was that the way in which the portfolios were constructed was not very efficient, and that the actual performance of such funds were therefore driven as much by their exposures to other factors as they were by their exposure to the target Style factor.
We created a number of optimized Smart Portfolios, in which we deliberately maximized the exposure of each portfolio to the target Style factor, while minimizing its exposure to all other factors as far as possible, consistent with the long-only constraint. These Smart Portfolios’ performance compared very favorably with many of the Smart Beta ETFs available in the market at the time.
In this research exercise, we are looking at using the composition of the Smart Equity Portfolios to build a set of corresponding Smart Corporate Bond portfolios. The holding of each equity is replaced with a corporate bond issued by the same company. To do this, we use the Merton formulation of a corporate bond as effectively consisting of a combination of the underlying equity and some (risk-free) Treasury bonds. The results proved to be surprisingly good!
In 1980 Jason MacQueen founded QUANTEC, which was the first firm to develop risk models for equity markets outside the USA, and which ultimately built risk models for all of the developed and most of the emerging markets. In 1984 QUANTEC launched the first global asset allocation model, including currency hedging overlays and the first use of reverse optimization for efficient portfolio rebalancing.
Jason also pioneered the development and use of multi-factor stock selection models in the U.S.A. and Japan, and the investment track records of his long term collaborators are exceptional. In the early 1990s QUANTEC developed the first truly global risk model and a global stock selection model, both incorporating global common factors.
In the late 1990s Jason and his colleagues developed a statistical risk model-based technique for the American Stock Exchange to enable them to offer Exchange Traded Funds (ETFs) on Actively-managed Mutual Funds without knowing the underlying holdings. This technology can also be used by enable pension funds and others to manage their overall portfolio risks without having full transparency from their external managers.
QUANTEC was sold to Thomson Financial in February 2001, and after consulting to them for two years, he co-founded R-Squared Risk Management in 2003 to develop Customized Hybrid Risk Models for institutional investors to manage their portfolio risks more efficiently.
R-Squared has also developed a unique set of XRD equity risk models covering different geographies. In addition, the RSQRM XRD models can be used to estimate the sensitivity of portfolios to a wide range of macro-economic variables and commodity prices. In December 2014, R-Squared Risk Management was acquired by Northfield Information Services, where he is now Director of Research.
Since founding QUANTEC in 1980 Jason has developed the theoretical framework of Markowitz and his successors into a practical set of tools for institutional fund managers. By his passionate pleas for a disciplined and logically coherent approach to portfolio management, he has acquired an international reputation as speaker, consultant and iconoclast. He was educated at Oxford and London Universities, where he read Mathematics and Theoretical Physics.
He has been an Honorary Lecturer at Lancaster University Management School, and Visiting Professor at Tokyo University’s Center for Advanced Research in Finance.
He was the founder and first Chairman of the London Quant Group, a not-for-profit organization established in 2007 to arrange Seminars on the practical application of quantitative investment technology, and is also a Director of the Society of Quantitative Analysts in New York.
Marcos Lopez de Prado, Berkeley Lab
"The 7 Reasons Most Machine Learning Funds Fail”
The rate of failure in quantitative finance is high, and particularly so in financial machine learning. The few managers who succeed amass a large amount of assets, and deliver consistently exceptional performance to their investors. However, that is a rare outcome, for reasons that will become apparent in this presentation. Over the past two decades, I have seen many faces come and go, firms started and shut down. In my experience, there are 7 critical mistakes underlying most of those failures.
Marcos Lopez de Prado manages several multibillion-dollar funds for institutional investors using ML algorithms. He is also a research fellow at Lawrence Berkeley National Laboratory (U.S. Department of Energy, Office of Science). One of the top-10 most read authors in finance (SSRN's rankings), Marcos has published dozens of scientific articles on ML in the leading academic journals, and he holds multiple international patent applications on algorithmic trading. Marcos earned a PhD in Financial Economics (2003), a second PhD in Mathematical Finance (2011) from Universidad Complutense de Madrid, and is a recipient of Spain's National Award for Academic Excellence (1999). He completed his post-doctoral research at Harvard University and Cornell University, where he teaches a Financial ML course at the School of Engineering. Marcos has an Erdös #2 and an Einstein #4 according to the American Mathematical Society.
Charles Tapiero, NYU
"Financial Data Science:An intelligence challenge"
Information and Computing Technology (ICT), the broad availability of financial and other data, have upended the challenges to integrate finance theories and their intelligence, statistics, new brands of data mining, learning, data management optimization and their use. A push-pull challenge emerges between the traditional ex-ante rationale of complete markets and their models and real finance up ended by ex-post Big data. A merger of Quant Finance and Inverse Data Finance, define now future finance. One without the other will be incomplete in a broad sense.
The purpose of this lecture is to highlight the intelligence that challenges and reconciles both financial Quant-Intelligence, its theoretical and statistical origins and the push-pull forces that define an emerging future financial intelligence.
Charles S. Tapiero, is the Topfer Chair Distinguished Professor of Financial Engineering and Technology Management at the Tandon School of Engineering of New York University. Professor Tapiero has founded and Chaired the Department of Finance and Risk Engineering from 2006 to 2016. He has an extensive educational, academic, public and personal history. He obtained his first degree in Electrical Engineering at the Ecole Polytechnique (Montreal) in 1966 and in 1969 and 1970 the MBA and PH.D in Operations research and Management at the NYU Graduate School of Business. Following his graduation he entered Columbia University as an Assistant Professor and subsequently as an Associate Professor. At which time he moved to Israel where he assumed academic positions at the Hebrew University and simultaneously assumed numerous public positions and Board Memberships in Israel’s leading industrial and public institutions (such Koor Industries as a Board member, The Jewish Agency as Department Head and founder of numerous social programs, Senior Advisor to the Minister of Foreign Affairs in Israel, Advisor to the Chair of the Federation of Labor, and other positions). Additional academic positions have included the Kermit O.Hanson Chair at the University of Washington, Seattle, WA (1987-1988). The Lewis Progressive (Insurance) Chair Professor , Case Western Reserve University (1989-1991), a Professorship at ESSEC (France), Bar Ilan University, The Institute of mathematical Finance and a Quebec Prized position at Concordia University. Since 2006, he has returned to the US and joined the Polytechnic University (merged with NYU in 2015). In 2007 he founded the department of Finance and Risk Engineering, that became one of the largest in the world with 300-350 Graduate students.
Professor Tapiero has participated in numerous academic meetings and assumed positions in academic Journals as Editorial Board Member and Co-Chief Editor and co-founder of the the Journal RDA (Riek and decision Analysis). He published over 17 Books spanning Optimal and Stochastic Control, Risk Finance and Assets Pricing, Supply Chains and Games, Engineering Risk Finance, Globalization, Gating and Risk Finance (Wiley, 2018)and others. His research outputs includes over 400 academic papers. Current research papers with Pierre Vallois include Fractional Finance and the Brownian Bridge (Physica A), Statistical Randomness and Fractional Stable Distributions as well as research on Data Science and Intelligence as well as Financialization and Society.