Abstracts
Keynote Presentation
A Stylized History of Quantitative Finance
9:50–10:30 a.m.
The evolution of modern portfolio theory, and in particular derivatives valuation, has proceeded through many small steps and occasional large epiphanies. In his talk, Emanuel Derman points out what he sees as some of the intellectual breakthroughs and failures, from the idea of dependency, the description of diffusion, the definition of risk, the principle of no riskless arbitrage, optimization, diversification, Sharpe ratios, replication, Black-Scholes-Merton, Feynman-Kac, calibration, the invention of implied volatility, the Smile, behavioral finance, and the infinite regress of financial modeling.
Emanuel Derman, Director of Financial Engineering, Columbia University
General Session
MathWorks Fintech Showcase and Quant Roundtable
9:10–9:50 a.m.
Big data, cloud computing, text analytics, deep learning, and artificial intelligence are creating disruptive opportunities across industries and have led to startup FinTech companies challenging established financial firm’s businesses. Learn from experts as they debate trends; introduce how MATLAB® has evolved over the past two years to help companies remain agile while innovating at a rapid pace; announce new products and capabilities released with R2017b; and answer audience questions.
Financial engineers from MathWorks and quants from industry
Track 1
Out of Memory? No Problem. Techniques for Building Machine Learning Models on Big Data
10:50–11:30 a.m.
In today’s world, there is an overabundance of data, generated from many different sources. Big data represents an opportunity for quantitative analysts and data scientists to impact the way organizations make informed business decisions.
Machine learning techniques are often used for financial analysis tasks such as time series analysis, forecasting, risk classification, estimating default probabilities, and data mining. However, implementing and comparing modeling techniques to choose the best method can be challenging, especially with big data. MATLAB® minimizes these challenges by providing several built-in tools for quick prototyping and scaling, without bringing the data into memory.
In this session, Heather Gorr introduces techniques for gaining insight into big data on a Spark™ enabled Hadoop® cluster, determining the best algorithm for your problem, evaluating model performance, and deploying machine learning models.
Heather Gorr, MATLAB Product Marketing Manager, MathWorks
FinTech Education: Introducing the ARPM Lab
11:30 a.m.–12:10 p.m.
Attilio Meucci introduces the ARPM Lab®, a multimedia platform to learn how engineering and data science apply to quantitative asset management. Attilio demonstrates the ARPM Lab, including how theory, videos, and MATLAB® exercises are used to learn historical and current state-of-the-art practices for quantitative asset management.
Attilio Meucci, Founder, ARPM
Using MATLAB for Sentiment Analysis and Text Analytics
1:30–2:10 p.m.
Sentiment scores, derived from text such as newsfeeds and social media, provide important information for determining portfolio positions. However, a document’s sentiment is often a weak signal surrounded by a large amount of noise. Extracting that signal requires a variety of techniques for working with data both in text and numeric formats, as well as machine learning techniques for automating the sentiment scoring process on large amounts of data.
Learn how to use text analytics capabilities in MATLAB® to build your own sentiment analysis tools. This presentation covers the entire sentiment scoring workflow, including getting social media feed data into MATLAB, preprocessing and cleaning up the raw text, converting text to a numeric format, and applying machine learning techniques, such as word2vec, to derive sentiment scores.
Seth DeLand, Data Analytics Product Manager, MathWorks
Quantitative Sports Analytics Using MATLAB
2:10–2:50 p.m.
Dr. Kissell provides an overview of quantitative sports analytics using MATLAB®. He presents models and methodologies from his book, Optimal Sports Math, Statistics, and Fantasy and provides techniques and MATLAB functions to solve an array of sports-related problems.
These approaches and techniques can be used by the entire sports community—students, professionals, fantasy gamers, and casual sports fans—to objectively analyze and rank teams, predict winning team and win probability, evaluate player skill and forecast future performance, compute the probability that a team will beat a sports line, as well as be applied to fantasy sports competitions. These models can also be employed by professional sports teams to assist in the player selection process, determine game-to-game match-ups, as well as for salary negotiations and salary caps problems.
Key items addressed in the presentation include:
- Ranking sports teams accurately
- Computing winning probability
- Demystifying the black-box world of computer models
- Providing insight into the BCS and RPI selection process
- Selecting optimal mix of players for a fantasy league competition
- Evaluating player skill and forecast future player performance
- Selecting team rosters
- Assisting in salary negotiation
- Determining Hall of Fame eligibility
Robert Kissell, President, Kissell Research Group
Machine Learning for Risk Modeling in MATLAB
3:10–3:50 p.m.
Artificial intelligent systems in finance have exploded over the last few years. This is especially true in risk modeling where financial institutions are being hit with massive losses and fines. Most institutions are not incurring losses because of their inability to build complex models, but due to the fact they are not able to showcase to regulators that they are versatile enough to adapt to the volatility. With this in mind, MathWorks has built a Statistics and Machine Learning Toolbox™ with specialized applications to facilitate the quick building of machine learning models, such as logistic regression, boosted and bagged trees, and regression style classifiers. In addition, MathWorks has built a Risk Management Toolbox™ with the goal of giving you complete out of the box models with transparency to areas such as credit and market risk models. This session aims to provide an intensive overview of the current state of art in financial risk modeling using high performance computing tools on big data.
Marshall Alphonso, Senior Financial Engineer, MathWorks
Exploiting Alternative Data in the Investment Process
3:50–4:30 p.m.
The emergence of big data in finance has shifted the alpha focus away from being faster to being smarter and more efficient than the competition. Access to alternative data sources is considered a key input to such a process. During his talk, Peter Hafez will provide an overview of the changing investment landscape and the “winning formula” for successful quant investing. He will also cover some of his team's latest research in the space.
Peter Hafez, Chief Data Scientist, RavenPack
Building Interactive Risk Dashboards with MATLAB
4:30–5:10 p.m.
Even today, when CROs need to see results, they ask for reports. Using high performance models combined with intuitive interactive visualization, CROs and other business users can have all the results in their fingertips with no delays.
In this presentation, Timo Salminen from Model IT demonstrates how MATLAB® can be used both for building modular high-performance risk models and modular interactive dashboards of those models. While there are multiple big data visualization tools available, interacting with complex models often requires custom built solutions.
Timo Salminen explores multiple use cases, such as stress testing, economic capital calculation, and stochastic portfolio optimization with complex liabilities, non-normal distributions, and derivative overlays.
Timo Salminen, Model IT
Track 2
Multiperiod Portfolio Selection and Bayesian Dynamic Models
10:50–11:30 a.m.
Petter Kolm and Gordon Ritter describe a novel approach to the study of multiperiod portfolio selection problems with time-varying alphas, trading costs, and constraints. They show that, to each multiperiod portfolio optimization problem, one may associate a “dual” Bayesian dynamic model. The dual model is constructed so that the most likely sequence of hidden states is the trading path which optimizes expected utility of the portfolio. The existence of such a model has numerous implications, both theoretical and computational. Sophisticated computational tools developed for Bayesian state estimation can be brought to bear on the problem, and the intuitive theoretical structure attained by recasting the problem as a hidden state estimation problem allows for easy generalization to other problems in finance. Time permitting, they will discuss several applications to this approach.
Petter Kolm, Director of the Mathematics in Finance Master’s Program and Clinical Professor, Courant Institute of Mathematical Sciences, New York University
Gordon Ritter, Senior Portfolio Manager, GSA Capital
Integrating Advanced Analytics with Big Data
11:30 a.m.–12:10 p.m.
Big data represents an opportunity to impact the bottom line of organizations. By building advanced analytics and predictive models on top of these large repositories of data business decisions can be vastly improved. Some tasks that stand to gain the most from such improvements include predicting fraud, determining trading opportunities, improving the customer experience and journey, and even forecasting which employees are at risk for switching companies.
In this session, Ian McKenna introduces ways of working with big data and rapidly deploying analytics into production.
Highlights Include:
- Managing data and integration with databases, HDFS/Hadoop®, and big data environments
- Running advanced analytics on Spark™ systems
- Scaling and increasing performance with multiple processors, clusters, and the Cloud
- Using powerful tools to take prototypes into production
Ian McKenna, Financial Engineer, MathWorks
Integrating MATLAB with Other Systems and Programming Languages for Asset Management
1:30–2:10 p.m.
By observing the gap between user tools created by researchers using MATLAB® and IT production systems created by IT professionals using general programming languages such as Java®, C++, and Python®, we propose an open paradigm of integrating MATLAB with other systems and different programming languages, with enabling features from MATLAB such as MATLAB Production Server™, web service, JSON support, and object-oriented programming in MATLAB.
To demonstrate the process, we will discuss how we built an asset management system using MATLAB integrated with our Java based production environment. The system is used for multi-factor regression on multi-asset fund-of-funds portfolios with complex factor selection rules.
To build such a mixed system, we need to carefully handle business object models, security, data caching, exception handling, logging, etc. We also propose a micro-service architecture with MATLAB as one of the core components to enable scalable MATLAB based computation from other systems.
Xiaotao Wu, Chief Quantitative Investment Modeling Engineer, JP Morgan Chase
Using MATLAB to Develop and Deploy a 7 Market Fixed-Income Model for Risk Management and Performance Analysis
2:10–2:50 p.m.
MATLAB® has evolved from an ad hoc analysis environment to a full development platform, enabling financial models and analytics developed in MATLAB to be seamlessly and efficiently deployed to financial Institutions. In this talk, Peter Orr describes how to use MATLAB to develop and deploy a multi-market fixed-income model (with a Microsoft® Excel® front-end) that simultaneously captures U.S. Treasury, corporate bond, mortgage, municipal, and other markets for risk management and performance analysis.
Peter Orr, Founder, Intuitive Analytics
Deployment of Real-Time MATLAB Models in Web Applications: On-Demand Balance Sheet Simulation
3:10–3:50 p.m.
Scotiabank has implemented a novel web-based analytics platform that is being used within a large international bank treasury department for asset liability management, earnings optimization, interest rate risk analytics, and balance sheet simulation. The in-house built analytics engine integrates MATLAB Production Server™ and MATLAB Distributed Computing Server™ together with standard SQL, Python®, and web technologies to provide on-demand analytics to multiple concurrent users.
Sean Woodworth, Director ALM Analytics and Development, Scotiabank
Kyle Pastor, Associate Director ALM Analytics and Development, Scotiabank
Pricing and Risk Analytics with the MATLAB Numerix Interface
3:50–4:30 p.m.
xVA is rapidly becoming a standard for assessing risk associated with counterparties since the financial crisis. Many regulations (FRS13, Basel III) are requiring CVA as part of the reporting frameworks. In his talk, Andy will provide an overview of xVA pricing and risk management, the main computational challenges faced when implementing them, and how the MATLAB Numerix Interface can help organizations overcome them while simplifying the management of these exotic instruments.
Andrew McClelland, Numerix
Apache Arrow: Memory Interoperability at Scale
4:30–5:10 p.m.
New hardware, distributed computing, and a plethora of machine learning frameworks have led to multiple systems with very similar, but just-different-enough in-memory formats to make sharing data between these systems difficult. At best, it is still extremely inefficient to move data between infrastructure pieces because of multiple serialization or deserialization steps. Apache Arrow™ is a solution for this problem. Arrow enables data infrastructure to use the same memory layout and share data in the most efficient way possible, including support for streaming as well as batch data. In this talk, Phillip Cloud discusses the motivation for Arrow, the in-memory format, and architecture details, as well as gives real-world example use cases.
Phillip Cloud, Software Engineer, Two Sigma
Master Classes
Risk Modeling Foundations with MATLAB
10:50 a.m.–12:10 p.m.
This master class introduces MATLAB® functionality for risk management. Through examples carried out in MATLAB, you will learn how to import data, perform data analysis and visualization, create simple risk models and perform simulations on them, and share your models and calculations with others in the form of Microsoft® Excel® add-ins. This master class provides you with the tools you need to get started with MATLAB in your own work. It is also perfect for those interested in learning about new functionality designed to make you more productive when working in MATLAB.
This session is based on a Master Class delivered at the GARP 15th Annual Risk Management Convention in 2014.
Nicole Beevers, Financial Engineer, MathWorks
Cleaning and Managing Data Made Easy in MATLAB
1:30–2:50 p.m.
To maintain a competitive advantage, it is critical to have the right tools in place that are flexible, allow you to iterate rapidly, and scale easily.
In this session, Siddharth Sundar demonstrates how MATLAB® is an ideal ETL platform for exploratory analysis, quick data transformation and cleansing, and scaling to any size data with nearly no code changes. New capabilities in MATLAB ranging from built-in functions, data containers, and external interfaces will be introduced to save time and simplify complex data management tasks.
Highlights include:
- Importing and aggregating data from different sources (datafeeds, databases, and Microsoft® Excel®)
- Working with structured and unstructured data of varying sizes (big and small)
- Handling missing data, imputation, and identifying outliers
- Managing time series data (merging and resampling)
- Feature extraction and dimensionality reduction techniques
- Performing data mining and creating interactive visualizations
Siddharth Sundar, Financial Engineer, MathWorks
Financial Software Development with MATLAB
3:10–5:10 p.m.
MATLAB® is often used for solving financial engineering and scientific problems. As the size and complexity of your application increases, it becomes more challenging to manage your development process.
MATLAB provides advanced software development capabilities, including error handling, object-oriented programming (OOP), and unit testing as well as workflows for deploying production applications.
Sean de Wolski provides best practices and tips for developing complex applications with MATLAB, including:
- Unit testing and behavior driven development
- Structuring large projects
- Object-oriented programming in MATLAB
- Performance measuring
- Productionizing MATLAB algorithms
Sean de Wolski, Applications Engineer, MathWorks
Heather Gorr
MATLAB Product Marketing Manager, MathWorks
Heather Gorr has supported customers in the areas of MATLAB programming, mathematics, and data analytics since 2013. Prior to joining MathWorks, she was a research fellow, focused on machine learning for prediction of fluid concentrations. She holds a Ph.D. in materials science engineering from the University of Pittsburgh and an M.S. and a B.S. in physics from Penn State University.
Xiaotao Wu
Chief Quantitative Investment Modeling Engineer, JP Morgan Chase
Xiaotao Wu is affiliated with JP Morgan Asset and Wealth Management Technology as the chief quantitative investment modeling engineer on the Global Research Technology (GRT) team. Prior to JP Morgan, he held R&D positions at Bloomberg and Fidelity. He earned his Ph.D. in computer science from Columbia University.
Seth DeLand
Data Analytics Product Manager, MathWorks
As product manager for Data Analytics, Seth DeLand is responsible for driving business development of this application across several industries including manufacturing, electrical power, automotive, and aerospace. Prior to this role, he worked as a product manager for Numerical Optimization and as an application support engineer. He has a B.S. and an M.S. in mechanical engineering with a minor in mathematics from Michigan Technological University.
Robert Kissell
President, Kissell Research Group
Robert Kissell is the president and founder of Kissell Research Group. He has over 20 years of professional experience specializing in economics, quantitative modeling, statistical analysis, and risk management. He advises and consults portfolio managers throughout the U.S. and Europe on appropriate risk management, trading analysis, and portfolio construction techniques. He is the author of the leading industry books Optimal Trading Strategies, The Science of Algorithmic Trading and Portfolio Management, and Multi-Asset Risk Modeling. Robert has published numerous research papers on trading strategies, algorithmic trading, risk management, and best execution. His paper “Dynamic Pre-Trade Models: Beyond the Black Box” won the Institutional Investor Prestigious Paper of the Year award.
Marshall Alphonso
Senior Financial Engineer, MathWorks
Marshall Alphonso is a senior application engineer at MathWorks, specializing in quantitative finance. He has over 7 years’ experience training clients at over 250 companies including top hedge funds, banks, and other financial institutions. Marshall was previously an advisor to the CRO of McKinsey & Co. Investment Office, he was responsible for the design and implementation of the fund liquidity framework and stress testing. He holds a B.S. in electrical engineering and mathematics from Purdue University and an M.S. in electrical engineering from George Mason University. In addition, his experience includes work in the aerospace industry and graduate work in proteomics.
Attilio Meucci
Founder, ARPM
Attilio Meucci is the founder of ARPM, under whose umbrella he created and now instructs the six-day Advanced Risk and Portfolio Management (ARPM) Bootcamp®, and manages the charity One More Reason. Prior to ARPM, Attilio was the chief risk officer and director of portfolio construction at Kepos Capital. He was also the head of research for Bloomberg’s risk and portfolio analytics platform, a researcher at Lehman POINT, a trader at the hedge fund Relative Value International, and a consultant at Bain & Co. Concurrently, he taught at Columbia-IEOR, NYU-Courant, Baruch College-CUNY, and Bocconi University. Attilio authored Risk and Asset Allocation and numerous publications in practitioner and academic journals. He earned a B.A. in physics from the University of Milan, an M.A. in economics from Bocconi University, and a Ph.D. in mathematics from the University of Milan, and is a CFA charter holder.
Ian McKenna
Financial Engineer, MathWorks
Ian McKenna joined MathWorks in 2011 as an application engineer supporting the financial services industry. He specializes in computational finance with applications focusing on asset allocation, risk management, time series forecasting, big data, and predictive modeling. Prior to joining MathWorks, he worked at the University of British Columbia developing simulation code used in industry for the heat treatment of steel alloys. Ian holds a Ph.D. from Northwestern University and a B.S. from the University of Florida in materials science and engineering with a minor in business administration.
Peter Hafez
Chief Data Scientist, RavenPack
Peter Hafez is the head of data science at RavenPack. Since joining RavenPack in 2008, he’s been a pioneer in the field of applied news analytics, bringing alternative data insights to the world’s top banks and hedge funds. Peter has more than 15 years of experience in quantitative finance with companies such as Standard & Poor's, Credit Suisse First Boston, and Saxo Bank. He holds a master's degree in quantitative finance from Sir John Cass Business School, along with an undergraduate degree in economics from Copenhagen University. Peter is a recognized speaker at quant finance conferences on alternative data and AI and has given lectures at some of the world’s top academic institutions, including London Business School, Courant Institute of Mathematics at New York University, and Imperial College London.
Nicole Beevers
Financial Engineer, MathWorks
Nicole Beevers has been supporting MATLAB users in the finance industry since 2012. Prior to joining MathWorks, she was a finance application engineer working with MATLAB users across Southern Africa. She holds a master of science in mathematics from the University of the Witwatersrand and a bachelor of science (with honors) from Rhodes University, both in South Africa.
Siddharth Sundar
Financial Engineer, MathWorks
Siddharth Sundar joined MathWorks in 2013 and moved into an application engineering role supporting the financial services industry in 2014. His focus is in computational finance with applications including risk management, portfolio optimization and asset allocation, algorithmic trading, time-series forecasting, and instrument pricing. Prior to joining MathWorks, he received his master’s in electrical engineering from the University of Michigan and bachelor’s degree in electronics and communication engineering from the Visvervaraya Technological University.
Sean de Wolski
Applications Engineer, MathWorks
Sean de Wolski joined MathWorks in November 2011 and works as an application engineer supporting MATLAB and MATLAB products. He has an M.S. and a B.S. in civil engineering with a structural engineering focus from the University of Maine.
Emanuel Derman
Director of Financial Engineering, Columbia University
Emanuel Derman is a professor at Columbia University, where he directs their program in financial engineering. He started out as a theoretical physicist, doing research on unified theories of elementary particle interactions. At AT&T Bell Laboratories in the 1980s, he developed programming languages for business modeling. From 1985 to 2002, he worked on Wall Street, where he co-developed the Black-Derman-Toy interest rate model and the local volatility model. He is the author of The Volatility Smile (Wiley, 2016) with Michael Miller; Models.Behaving.Badly (Free Press, 2011), one of Business Week’s top 10 books of 2011; and My Life As A Quant (Wiley, 2004), in which he introduced the quant world to a wide audience.
Sean Woodworth
Director ALM Analytics and Development, Scotiabank
Sean Woodworth is the director of ALM Analytics and Development within Scotiabank’s Treasury department. Located in Toronto, the team is responsible for the development and deployment of custom analytics solutions for balance sheet simulation and forecasting. Sean has spent the last 11 years working in quantitative finance in the areas of treasury ALM and market risk management. Sean has a Ph.D. in engineering physics and an M.Sc. in astrophysics.
Kyle Pastor
Associate Director ALM Analytics and Development, Scotiabank
Kyle Pastor has been with Scotiabank for four years and is currently an associate director on the Treasury ALM Analytics and Development team. As a senior developer, he is responsible for the design and integration of the in-house built analytics engine and front-end user interface. Kyle has a master’s in quantitative finance from Waterloo and an M.Sc. in physics from McMaster University.
Timo Salminen
Model IT
Timo Salminen is the head developer of MATLAB applications at Model IT, a Finnish technology company. He has a long history of working closely with customer banks, insurance companies, pension funds, and asset managers in financial modeling. Timo’s expertise is in combining latest mathematical models, high performance computing, and user interface design into fast and accurate analysis with intuitive presentation. He was previously employed by Evli Asset Management.
Timo has an M.Sc. in technology from Helsinki University of Technology. He is a CFA charter holder and a certified FRM.
Andrew McClelland
Numerix
Andrew McClelland's work at Numerix focuses on counterparty credit risk issues including valuation adjustments and counterparty exposure production for structured products. He also works on numerical methods for efficient production of risk profiles under real-world measures. Andrew received his Ph.D. in finance at the Queensland University of Technology in financial econometrics. His research involved markets exhibiting crash feedback, option pricing, and parameter estimation using particle filtering methods. His work has been published in the Journal of Banking and Finance, the Journal of Econometrics, and the Journal of Business and Economic Statistics.
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