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SQA Fuzzy Day Conference
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3/7/2018
When: Wednesday, March 7, 2018
8:30 AM


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SQA Fuzzy Day Conference

 

March 7, 2017

 

8:30 a.m. - 3:30 p.m.

 

AB
1345 Avenue of the Americas New York, NY 10105

 

Speakers Include:

 

Sarah Jiang, AQR, "Craftmanship Alpha: An Application to Style Investing"

Jason MacQueen, Northfield, "Converting Smart Equity Portfolios into Smart Corporate Bond Portfolios"

Marcos Lopez de Prado, Berkeley Lab, "The 7 Reasons Most Machine Learning Funds Fail"

 Robert Stamicar, Axioma, "A CVaR Scenario-Based Framework for Minimizing Downside Risk in Multi-Asset Class Portfolios"

 

Charles Tapiero, NYU, "Financial Data Science: An intelligence challenge"

 

Abstracts:

 

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!

 

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.

 

 

 

 

 

 

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.  

 

 

 

 

Conference Agenda

 

8:30 a.m. Registration & Breakfast

 

8:45 a.m. Conference Welcoming

 

9:00 – 10:00 a.m. Robert Stamicar, Axioma, "A CVaR Scenario-Based Framework for Minimizing Downside Risk in Multi-Asset Class Portfolios"

 

10:00 – 11:00 a.m. Marcos Lopez de Prado, Berkeley Lab, "The 7 Reasons Most Machine Learning Funds Fail"

11:00 – 11:15 a.m. Break

11:15 – 12:15 p.m. Jason MacQueen, Northfield, "Converting Smart Equity Portfolios into Smart Corporate Bond Portfolios"

12:15 – 1:15 p.m. Lunch

1:15 – 2:15 p.m. Sarah Jiang, AQR, "Craftmanship Alpha: An Application to Style Investing"

2:15 – 3:15 p.m. Charles Tapiero, NYU, "Financial Data Science: An intelligence challenge"

 

3:15 p.m. – Conference Closing

 

 

Member - $495
Student/Transitional Member - $250

 Non-Member - $695
Non-Member Afilliate - $595

 

Early Bird  - Register By March 1st  and receive $100 discount!

 

 

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