Abstracts
Challenge-Response: Data Science and AI in Production
9:10–9:50 a.m.
Time to market, regulatory compliance, talent shortages, and new opportunities are fueling innovation in financial services. From text analytics to automated development workflows for compliance, customers are using MATLAB® to respond with production applications. Deep learning and NLP in investment, risk model governance, and ESG examples will be discussed.
David Rich, MathWorks
Applied Uses of AI for Investment Insights and Operational Efficiency
9:50-10:30 a.m.
With the dawn and rapid evolution of artificial intelligence and machine learning, financial institutions waste no time innovating with the technologies and techniques. This is particularly true in buyside investing with significant elements of quantitative discipline, including JPMorgan Asset Management (JPMAM).
The breadth of the innovations touches a wide spectrum of investment processes, including but not limited to robust technology operation with pre-emptive issue identification, innovative and efficient product construction for passive strategies, and forward-looking detection of market rotations, as well as systematic prediction of alpha opportunities for active businesses.
In this talk, David will discuss thematically the applied uses of AI and ML technology that help generating operation efficiencies and investible insights for JPMAM clients. This discussion will feature a brief demo live in visualizing the techniques deployed.
David Lin, JP Morgan Asset Management
The R-Factor: Converting ESG and Corporate Governance Data into Investable Insights
10:30-11:10 a.m.
Availability of financially material, consistently reported, and comparable ESG data is one of the biggest challenges facing investors across the capital markets. That’s why State Street Global Advisors built the R-Factor, a scoring model that leverages multiple data sources and aligns them to widely accepted, transparent materiality frameworks to generate a unique ESG score for 5,500 global companies. They draw on the materiality framework of the Sustainability Accounting Standards Board (SASB) and national/investor-created corporate governance frameworks. R-Factor was built to solve for the data quality challenges in the market, and to remove opaqueness around ESG materiality in the scoring process. It is the only score that is backed by a strong stewardship commitment from an asset manager and is designed to put companies in the driver seat to help create sustainable markets.
Todd Bridges, State Street Global Advisors
Dynamic Replication and Hedging: A Reinforcement Learning Approach
11:20-12:00 p.m.
In this talk, we address the problem of how to optimally hedge an options book in a practical setting, where trading decisions are discrete and trading costs can be nonlinear and difficult to model.
Based on reinforcement learning (RL), a well-established machine learning technique, we propose a model that is flexible, accurate, and very promising for real-world applications. A key strength of the RL approach is that it does not make any assumptions about the form of trading cost. RL learns the minimum variance hedge subject to whatever transaction cost function one provides. All it needs is a good simulator in which transaction costs and options prices are simulated accurately.
This is joint work with Gordon Ritter.
Published paper: Deep Reinforcement Learning for Option Replication and Hedging
Petter Kolm, New York University
CCAR Neural Networks Model
1:10-1:50 p.m.
Neural network (NN) models represent an opportunity to improve the credit loss forecasting and stress testing in Comprehensive Capital Analysis and Review (CCAR), which can be estimated as a function of macroeconomic variables using ARIMA-type models. For reference, please refer to: Capital Planning at Large Bank Holding Companies: Supervisory Expectations and Ranges of Current Best Practices, 2013. However, there remain challenges for the application of NN models such as model interpretability per regulatory requirements and a tendency to overfitting. This session will discuss this research and highlight the following:
- By leveraging a credit card firm’s monthly write-off data for over 15 years, a parsimonious NN model can be developed, which outperforms the traditional regression model with ARIMA errors in Mean Squares Error (MSE).
- Two macroeconomic variables with lags are selected from a pool of 500 by the combination of LASSO and Stepwise regression algorithms, which enables the NN model to be interpretable for the CCAR scenario narratives. The sign or direction of estimated input weights should be consistent with or constrained by business intuition.
- This study also found that the NN model could be vulnerable to overfitting. The stress testing is sensitive to the design of network architect.
Heng Chen, HSBC and Northwestern University
How MATLAB Reshapes the Landscape to Serve Wealth Management Clients
1:50-2:30 p.m.
Wealth management is an integral function of financial institutions that provides financial and investment advisory services to high-net worth clients. Thousands of clients with a unique financial need require a powerful analytic platform to perform tasks such as asset allocation, manager selection, and custom report generation. MATLAB has been used to accommodate multi-tread calculations throughout the entire process, from data preprocessing to financial modeling to report generating. As a result, a stable recommendation to clients with sophisticated nonlinear constraints can be generated in timely manner.
Stephanie Wang, Morgan Stanley Wealth Management
Applications of Academic Theory and Quant Techniques in Securities Lending
2:50-3:30 p.m.
Like many other markets, Securities Lending has seen a significant increase in digitization and electronification. This has increased market complexity and the need for more systematic decision making. As a result, Securities Finance and State Street Associates have leveraged quantitative techniques to build intelligent pricing algorithms and quantitative models to capture market price pressures. In this presentation we will discuss our approach to quantitative modeling, examples of solutions we built and their applications.
Penn Wharton Budget Model: Macroeconomics in MATLAB
3:30-4:10 p.m.
The Penn Wharton Budget Model (PWBM) is an integrated model of the U.S. economy. It consists of multiple components: a demographics microsimulation, tax modules, Social Security and other government programs, and a dynamic macro-economy model. The dynamic model computes optimal decisions by heterogeneous, rational, forward-looking agents and finds the aggregate effect on prices such that agents’ actions and prices are in equilibrium. These types of models are computationally intensive and are the workhorse models used by modern computational macro-economists. PWBM uses the dynamic model, built in MATLAB, to project the effect of policy changes on U.S. household behavior and the consequences for macro-economic variables such as GDP, interest rates, debt, and capital formation.
Efraim Berkovich, University of Pennsylvania
Model Risk Management for Alpha Strategies Created with Deep Learning
4:10-4:50 p.m.
In this session, you’ll learn about:
- Understanding the challenges of using deep learning to build alpha generation strategies
- Model risk management to detect when machine learning strategies are not performing as intended
- Can you model a constantly moving market? When DL should (and should not) be used
Ben Steiner, BNP Paribas Asset Management
A Master Class in Building Production-Grade NLP Pipelines
11:20-12:00 p.m.
Building and deploying NLP applications involves multiple steps, including data ingesting, pre-processing, labeling, model building, model selection, and deployment.
While data scientists are typically involved in prototyping end-to-end applications, deploying robust NLP applications in production requires building enterprise-grade pipelines and designing each stage in the pipeline to accomplish a particular task. This workshop presents QUSandbox, an enterprise platform for prototyping, designing, and scaling production-worthy machine learning pipelines. The platform and language agnostic platform enables integrating multiple tools to design coherent and production-grade pipelines that are auditable, replicable, and scalable. This master class will demonstrate the use of natural language processing techniques to analyze EDGAR call earnings transcripts that could be used to generate sentiment analysis scores using the Amazon Comprehend, IBM Watson, Google, and Azure APIs (application programming interfaces) to train your own model built in MATLAB. We will then illustrate how the various steps can be streamlined through a QuSandbox pipeline to enable building scalable machine learning applications in production.
Sri Krishnamurthy, QuantUniversity
A Platform for Risk Models
1:10–2:30 p.m.
See how MATLAB® is used to drive the development, review, deployment, and monitoring stages of credit and market risk models, using the latest advances in artificial intelligence and Live Editor technology to meet regulatory standards of traceability and reproducibility.
- Technology advances to guarantee model results can be reproduced by validation teams and in production
- Explain and interpret the output of artificial intelligence models to stakeholders and regulators
- Improve consistency of model development, validation, and documentation activities through automation
Paul Peeling, MathWorks
AI, Machine, Deep Learning, and NLP in Enterprise Investment and Risk Management
2:50-4:10 p.m.
With the dreams of artificial intelligence, we have come a long way since the 1950s. Predictive modeling and machine and deep learning have started to permeate every aspect of finance. Two hot areas for adoption of these techniques is model validation and stress testing. With the onslaught of Twitter increasing the volatility in our markets, newer companies are exploding on the scene with novel risk management systems that utilize Twitter in predicting volatility. Through demos, we will be exploring the state-of-the-art in artificial intelligence to generate more insights into how to best to manage portfolios in this time of fake news and volatility.
Key areas of focus:
- Introduction to the state of Predictive Modeling, AI, Machine Learning, and Deep Learning in Risk and Finance
- Demos to illustrate natural language processing
- Discussions around scaling to production
- Discussions around model governance
Marshall Alphonso, MathWorks
Natural Language Processing and Deep Learning in Finance
4:10-4:50 p.m.
In 2010, the worldwide IP traffic exceeded 20 exabytes (20 billion gigabytes) per month. With the explosion of unstructured information came a massive demand for computational power, efficiency, and explainable AI. Even with the explosion of quants (approx. 10,000 worldwide) we are still massively short in our ability to translate the data into meaningful decision-making information that moves businesses. So, rather than just allowing for humans to interact with the data through the lens of a quant, natural language processing allows us to interact with some of humans’ most practiced skills: spoken and written word.
Natural language processing (NLP) refers to the broad class of computational techniques for incorporating speech and text data, along with other types of financial data, into the development of smart systems. MathWorks NLP systems are currently being implemented at various financial institutions worldwide. The work is being done using algorithms and visualizing capabilities built into our Text Analytics Toolbox™. Many of these algorithms are complemented by using the Deep Learning Toolbox™ and Statistics and Machine Learning Toolbox™.
Financial applications that are using NLP:
- Text analytics for preparing data for an NLP system (entity modeling, cleaning data)
- Volatility modeling (GARCH, VARMA) and risk analytics (market and credit) using text
- Sentiment modeling to inform discover new alpha opportunities and beat the market
- Sentiment indicators for attribution of portfolio movements
- Economics research indicator development using state-space models such as a Kalman filter
- Litigation modeling: Work with complex legal languages to audit firms
- Fund research: Modeling audits of firms to prioritize investment opportunities and flag risks
Text Analytics and Sentiment Analysis
11:20–12:00 p.m.
Natural language processing (NLP) is a rapidly growing area of interest in the financial services industry as quants, risk managers, and financial analysts are all interested in deriving new alpha and insights from speech and text data. In this session, you will learn how MATLAB® as a platform assists in common techniques used in text analytics, ranging from preprocessing your text data to using machine learning to model that data. Specific techniques covered in this session include:
- Text analytics for preparing data for an NLP system (entity modeling, cleaning data)
- Natural language processing: LSA, LDA, and word embeddings
- Sentiment modeling to discover new alpha opportunities and beat the market
Alex Link, MathWorks
Software Development with MATLAB
1:10-1:50 p.m.
MATLAB® is often used in financial services as a modeling tool. And with any tool, as the size and complexity of your application increases, it becomes more challenging to manage your development process.
If you want to develop reusable and reliable MATLAB code, collaborate with a large team, and/or build user interfaces around your models to present them to business users, you should be at this session.
Highlights:
- Structuring large projects with Source Control and MATLAB Projects
- Error handling and readying code for production
- Unit testing and behavior-driven development
- Dashboard development to share models
Siddharth Sundar, MathWorks
Integrating Python and MATLAB
2:50-4:10 p.m.
Financial engineers who rely only on Python® may find themselves encountering challenging tasks when it comes to C/CUDA code generation, building interactive dashboards, parallelizing applications, signal and image processing, computer vision, portfolio/risk management, and deep learning. Contrarily, MATLAB® is a full-stack advanced analytics platform that empowers domain experts to rapidly prototype ideas, validate models, and push the applications into production with ease.
However, sometimes it is advantageous to integrate MATLAB and Python to build on top of open-source libraries and pipe data between different IT systems or the web.
In this session, we demonstrate the many ways in which MATLAB and Python can integrate to give business users and decision makers immediate access to many of MATLAB’s built-in analytics capabilities.
Highlights include:
- Calling Python libraries directly from MATLAB
- Using MATLAB to rapidly build machine learning models for trading strategies
- Calling a live MATLAB session from Python
- Packaging MATLAB analytics as royalty free .py libraries
- Scaling hybrid MATLAB/Python applications via the MATLAB Production Server™
Ian McKenna, MathWorks
David Rich
MathWorks
David Rich is the marketing director for the MATLAB family of products at MathWorks, spanning MATLAB and related toolboxes as well as cloud, server, and web capabilities. He also directs the company’s support of the startup community. Prior to this, he focused on strategic business development at Microsoft®, joining the company as part of its acquisition of Interactive Supercomputing, where he served as the vice president of marketing. David has experience working at large enterprises and startups ranging from Advanced Micro Devices (AMD) and Fujitsu® to API Networks and Dolphin Interconnect. He has a computer science degree from Brown University.
Paul Peeling
MathWorks
Paul Peeling is a consultant engineer who focuses on the financial services industry in modeling credit and market risk, and managing the risk associated with those models, especially when machine learning is applied. He also has expertise in signal processing, software design and application development in MATLAB and big data for enterprise-scale data analytics. Prior to joining MathWorks, Paul worked applying pattern recognition techniques to detect and combat online fraud. Paul has a Ph.D. in statistical signal processing from the University of Cambridge.
Marshall Alphonso
MathWorks
Marshall Alphonso, senior engineer at MathWorks, specializes in quantitative finance and is currently the global lead engineer for five top banks. He has more than 10 years’ experience training clients at more than 250 companies, including top hedge funds, banks, and other financial institutions around the world.
As advisor to the CRO of McKinsey & Co. Investment Office, Marshall was responsible for the design and implementation of the fund liquidity framework, stress testing framework, and a multitude of quantitative risk and investment tools, enabling evaluation of exposures for risk and attribution. His prior experience includes design of artificial intelligent systems and advanced statistical signal processing algorithms in real-time communication and geostationary satellite systems.
He holds a B.S. in electrical engineering and mathematics from Purdue University and an M.S. in electrical engineering from George Mason University. Additional significant graduate work includes NIH-sponsored research in proteomics at Harvard University and KISR-sponsored data science research at MIT.
Alex Link
MathWorks
Alex Link is an application engineer at MathWorks supporting the financial services industry. She works with various financial customers to help advance their modeling capabilities and overall performance using technology ranging from machine learning to cloud computing. She holds a B.S. in computer science from Georgia Institute of Technology.
Siddharth Sundar
MathWorks
Siddharth Sundar joined MathWorks in 2013 and moved into an application engineering role supporting the financial services industry in 2014. His focus is on computational finance with applications including risk management, portfolio optimization and asset allocation, algorithmic trading, time-series forecasting, and machine learning. He has extensive experience supporting customers with software development best practices in this space. Prior to joining MathWorks, he earned his master’s in electrical engineering from the University of Michigan and bachelor’s in electronics and communication engineering from the Visvesvaraya Technological University.
Ian McKenna
MathWorks
Ian McKenna joined MathWorks in 2011 as an application engineer supporting the financial services industry. During this time, his focus has been in computational finance with applications ranging from risk management, portfolio optimization and asset allocation, time series forecasting, and instrument pricing. Prior to joining MathWorks, he worked at the University of British Columbia, developing simulation code used in industry for 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.
David Lin
JP Morgan Asset Management
David Lin is a managing director and the chief technology officer of Beta Strategies and Solution Business at JPMorgan Asset Management (AM). He has been with the firm since 2001. He is currently responsible for end-to-end technology solutions for the two Lines of business. In addition, he is responsible for AM-wide performance attribution, portfolio analytics, and optimization. Before this, David was the head of Global Research Technology, Global Quantitative Technology, and prior to that, the head of Quantitative Equity Technology. Before joining JPMorgan, David was the chief architect and head of development at CNBC.com.
David holds a bachelor’s degree in computer science with a minor in commerce from University of Toronto, and a master’s degree in business administration from Columbia University. He is a member of Global Quant Council (GQC) of JPMorgan Asset and Wealth Management, and Society of Quantitative Analysts (SQA).
Todd Bridges
State Street Global Advisors
Todd Arthur Bridges, Ph.D., heads ESG Research and Strategy Development at State Street Global Advisors and is on the Global Equity Beta Solutions Team. The Global Equity Beta Solutions research team is responsible for research across index equities, smart beta, ESG, and climate. The team leads the thought-leadership efforts in these areas through white papers, academic articles, and new strategy development. Prior to joining State Street Global Advisors in 2017, Todd was head of research at Ethic Investing. In that role, he helped build the financial technology startup, and was responsible for developing the sustainable investing framework, integrating ESG data into portfolio management, and developing shareholder engagement strategies for the platform. Prior to joining this entrepreneurial venture, he was managing director of a cross-disciplinary research institute at Cornell University, focused on the role of finance, technology, and governance in sustainable economic development.
Todd holds MA and Ph.D. degrees from Brown University and has completed research fellowships at the National Science Foundation, Max Planck Society for the Advancement of Science, Oxford University, and Harvard University. He has published research and presented findings to central banks, sovereign wealth funds, state pension funds, corporate pension funds, and endowments throughout the North Americas, United Kingdom, EMEA, Russia, and APAC regions.
Ben Steiner
BNP Paribas Asset Management
Ben handles chief-of-staff and business management responsibilities for the CIO of Global Fixed Income at BNP Paribas Asset Management.
He has over 18 years of industry experience with hedge funds and investment managers in London and New York.
His previous roles include head of model development, portfolio manager, research manager, and senior quantitative researcher. His experience covers multiple asset classes: from default and loss models in the less liquid markets (private debt and real estate) to alpha models in the more liquid (managed futures, global macro, equity long/short and absolute return fixed income) to portfolio construction and the evaluation of systematic strategies.
He holds a BA (Hons) in economics from the University of Manchester and an MSc in mathematical finance from Imperial College London. Since 2013, Ben has served on the board of directors of the Society of Quantitative Analysts.
Despite now being a reformed quant, he still presents on deep learning and model risk management topics at Columbia and NYU, as well as industry events. He also still tries to find time to play with MATLAB for fun.
Heng Chen
HSBC and Northwestern University
At HSBC, Heng Z. Chen is responsible for supporting CCAR/DFAST loss forecast modeling and the group economic capital modeling in operational risks management. He is also an adjunct professor at Northwestern University.
As a team lead and senior manager in Discover Financial Services and GE Capital for eight years, Heng led the team members to address business challenges with innovative solutions such as Reject Inference methodology for retail new accounts modeling and LGD modeling methodology for commercial lending risk management.
Heng’s risk management experience includes new accounts underwriting, credit line assignment, fraud risks, customer risk management, economic capital, and loss forecast for retail credit cards and loans, and wholesale commercial loans business.
Heng received his two M.S. degrees from University of California at Davis in 1988 and a Ph.D. degree from Ohio State University.
Stephanie Wang
Morgan Stanley Wealth Management
Stephanie Wang is an assistant vice president at Morgan Stanley Wealth Management. She provides quantitative research on goal-based financial planning.
Stephanie began her professional career at Standard and Poor’s Rating Services Quantitative Analytics and Research Group in 2013 and joined Morgan Stanley Wealth Management in 2016.
Stephanie received a B.S. in environmental sciences from Peking University, an M.S. in Earth system science from University of California, Irvine, and an M.S. in operations research from Columbia University.
Stephanie Lo
State Street Securities Finance and State Street Associates
Stephanie Lo is the head of Quantitative Driven Research (QDRSF) for State Street Securities Finance. She is part of State Street Associates, State Street’s academic arm. In this capacity, Stephanie oversees multiple research projects related to securities finance. These projects include the use of alternative data, integration of academic insights and methods, and quantitative techniques such as machine learning and artificial intelligence.
Prior to joining State Street, Stephanie worked as a natural gas trader at a quantitative trading firm, a graduate researcher at the Federal Reserve Bank of Boston, and a management consultant at the Boston Consulting Group. Her research includes work on equities, mortgages and monetary policy, financial intermediation, secular stagnation, and Bitcoin.
She holds a PhD in economics, a master’s in economics, and a bachelor of arts in economics from Harvard University.
Yasser El Hamoumi
State Street Global Markets
Yasser El Hamoumi is an algorithmic trader with State Street Global Market’s Agency Lending program. He is responsible for the analytical framework, quantitative development, and implementation of algorithmic pricing. Additionally, Yasser oversees the technical development of the lending program’s market microstructures, which includes but is not limited to the lending program’s electronic trading platform, analysis and research on broker trading behavior, intraday price discovery, and implementation of new trading technologies.
Prior to his current role, Yasser worked as a quantitative developer with State Street’s Liquidity and Liability Management team. In this role, he was primarily focused with the development of data intensive models used for the management of State Street’s liabilities. These projects include the design of the firm’s operational deposit model, the quantitative estimation of credit lines with central banks and financial market utilities, and the execution of Federal Reserve Bank mandated stress testing.
Yasser attended Union College on the Posse Foundation Full Tuition Leadership Merit Scholarship, and holds a bachelor of science in mechanical engineering.
Efraim Berkovich
University of Pennsylvania
Efraim Berkovich leads development of the Penn Wharton Budget Model (PWBM) dynamic equilibrium model and technical infrastructure. Prior to PWBM, he was a college professor of economics and finance. Efraim has also worked in information technology as a technical architect at AXA Financial, CTO at an internet startup, and software engineer at Sanford C. Bernstein & Co.’s global equities group, as well as NASD (now FINRA), and the U.S. Treasury.
Efraim’s published work in economics includes papers in the Journal of Derivatives and the Review of Network Economics. He also has a number of publications and patents in computer engineering. Efraim earned his Ph.D. in economics from the University of Pennsylvania, his M.S. in electrical engineering from the University of Maryland, College Park, and his B.S. in mathematics from Georgetown University.
Sri Krishnamurthy
QuantUniversity
Sri Krishnamurthy, CFA, CAP, is the founder of QuantUniversity.com, a data and quantitative analysis company, and the creator of the Analytics Certificate program and Fintech Certificate program. Sri has more than 15 years of experience in analytics, quantitative analysis, statistical modeling, and designing large-scale applications. Prior to starting QuantUniversity, Sri worked at Citigroup, Endeca, MathWorks, and with more than 25 customers in the financial services and energy industries. He has trained more than 1,000 students in quantitative methods, analytics, and big data in the industry and at Babson College, Northeastern University, and Hult International Business School. Sri is leading development efforts in creating a platform called QuSandbox for adopting open source and analytics solutions within regulated industries.
Gary Kazantsev
Bloomberg Head of Quant Technology
Gary is the head of Quant Technology Strategy in the Office of the CTO at Bloomberg. Prior to taking on this role, he created and headed the company’s Machine Learning Engineering group, leading projects at the intersection of computational linguistics, machine learning, and finance, such as sentiment analysis of financial news, market impact indicators, statistical text classification, social media analytics, question answering, and predictive modeling of financial markets. Prior to joining Bloomberg in 2007, Gary had earned degrees in physics, mathematics, and computer science from Boston University. He is engaged in advisory roles with fintech and machine learning startups and has worked at a variety of technology and academic organizations over the last 20 years. In addition to speaking regularly at industry and academic events around the globe, he is a member of the KDD Data Science + Journalism workshop program committee and the advisory board for the AI & Data Science in Trading conference. He is also a co-organizer of the annual Machine Learning in Finance conference at Columbia University.
Travis Whitmore
State Street Securities Finance and State Street Associates
Travis Whitmore is a quantitative researcher in the Securities Finance Research team at State Street Associates (SSA). Since joining SSA in early 2018, Travis has helped develop and apply numerous quantitative models and contributed to several thought leadership pieces within the securities lending market.
Prior to joining SSA, he worked in State Street Global Markets as part of their rotational leadership program, where he developed collateral optimization models for the trading desks and built out an award-winning application to help mitigate fraudulent behavior.
Travis interned with Morgan Stanley and several technology startups before he graduated from the University of Vermont with a Bachelor’s of Science in Computer Science and Finance.
