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Big Data is the New Currency - New York City

Join top industry experts and practitioners as they debate the future of big data monetization in capital markets. Watch our previous event highlights video for what to expect in NYC! Register today.

Industry Leaders

For almost a decade, RavenPack Symposiums have consistently provided data-driven finance professionals with riveting forward-looking content, new research and insights, and practical use cases from industry leaders and top scholars.

This RavenPack Symposium event is free to attend (by invitation only) and will take place in NYC on September 10, 2019.

Agenda

Last Updated: August 21, 2019

8:30 am - Registration Starts

Light breakfast served

9:00 am - Welcoming Remarks

Introduction: Big Data is the New Currency

While data monetization becomes a crucial part of their strategy, financial institutions must address the risks associated with data privacy, the sourcing of alternative data, data leaks and security breaches, and learn how to mitigate those risks.

9:10 am - Presentation - Data Protection, Sourcing & Privacy

Keynote Presentation: The Coming Political Fight over Personal Information and Who Owns those Data

Technological advances always produce unanticipated economic value, and the internet is no exception. Many examples represent more efficient ways of doing what people and companies were already doing; others represent new activities with new value or such profound transformations of existing activities that they’re effectively new. Beyond these developments, the internet also provides the basis for a radical new business model, one embodied in Facebook and Google but also integral part of most companies with an internet presence. This new business model is the collection, organization, analysis and monetization of the personal data from everyone who visits or uses most websites. What are these data worth? We developed a model to estimate their value to platforms such as Facebook and Google, data brokers and credit card companies, internet service providers and mobile search engines, as well as the economic value of personal information to the people and families the data describe. Given the substantial and fast-growing value of these data, we will also explore the emerging question of whether the data are people’s personal property or belong to the companies that use their resources to collect, organize, analyze and rent or sell them. Finally, in this context, we will examine the emerging political and policy responses to the new business model based on monetizing people’s personal information.

9:50 am - Presentation - Data Protection, Sourcing & Privacy

How Much is that Byte Worth? Quantifying the Value of Information

In some ways, data are like gold ore – of little worth in bulk, but of enormous value when refined into useful information. Making some information public would lower or eliminate its value, like trade secrets. In other cases, the value of the information depends upon how well-known it is, like a photo on Instagram or a video on YouTube. What about personally identifiable information that companies are required to keep private? Such information represents a liability for financial services firms – in other words, it has a negative value. Further muddying these waters is the question of veracity. Fake news is worse than no news at all. In this thought-provoking keynote, industry analyst Jason Bloomberg will explore the subtleties of assigning value to data and information. Attendees of this session will: Learn the difference between data and information, and why this distinction is important. Gain a greater understanding of the complexities of valuing information. Take away some practical advice for considering the value of information in financial services organizations.

10:20 am - Break

Tea & coffee will be served

10:50 - Panel - Data Protection, Sourcing & Privacy

How Much is Your Personal Data Worth?

Practically every person on the planet generates some form of data (also known as “data exhaust”). With five billion people globally having a mobile phone connection, every call, message, purchase or internet search we make, shapes our lives while leaving a digital footprint. Even when we walk on busy city streets, CCTV cameras capture our physical movements while our smartphones track our every location. Our expert panelists will discuss how governments and corporations go about harvesting this information, what they do with it, who they provide it to, whether it is right or wrong and even how much money is being made at the expense of your data privacy. The panel will also address concerns over data protection in a world where the rise of “Business A.I.” seems unstoppable.

11:30 am - Presentation - Monetizing Big Data

Enhancing Quantamental Signals with Sentiment

Milind introduces a Quantamental investing model and synopsizes a sentiment signal. He shows how this sentiment signal performs as an overlay. He shows how his approach is effective in anticipating buyouts, which should be of much interest to activists, risk arbitrageurs and speculators on the long side as well as market participants (typically quants), looking to eliminate event risk on the short side.

11:50 am - Presentation - Monetizing Big Data

Finding Relevant Insights in a Data-Overloaded World

Many fundamental investors are struggling to find useful insights in a world that is becoming "over-abundant" with data. How do investors decide what is relevant? When are the best times to generate insights with statistical modeling and when do you use the mosaic theory? What are the best ways to use datasets that don't lend themselves to modeling? Barry Hurewitz, founder of UBS Evidence Lab, will share findings from extensive research on the investment processes of top investors and the implications on the use of alt data.

12:10 pm - Presentation - Monetizing Big Data

Asset Managers: It's Time to Monetize Your Own Data

We generate too many emails, store too many files and have more information than we know about or are able to handle. While conversations across email and messengers are growing senseless, our data is mostly unused, fragmented and decentralized making it impossible to keep track, let alone know exactly what we have. While organizations believe there is value in their own data, the challenge is finding ways to monetize it. In this presentation, Peter Hafez will present a real case study showcasing how a fundamental asset management firm is leveraging RavenPack technology to transform their own emails, messages, and files into trading signals to create alpha generating strategies.

12:30 pm - Lunch

While lunch is being served, you can attend workshops from RavenPack and DTCC.

13:30 pm - Presentation - Monetizing Big Data

Novel Mathematical Techniques for Extracting Structure From Text

Abstract to be confirmed soon

13:50 pm - Presentation - Monetizing Big Data

The Uses and Misuses of Machine Learning in Finance: What Asset Owners Should Know

Dr. Simonian will discuss the differences, advantages and disadvantages of traditional econometrics vs. financial data science. He will discuss the types of questions that asset owners should be asking when speaking to managers who claim to use machine learning in their investment process, drawing on his own experience with and solutions to common pitfalls in financial data science research.

14:10 pm - Presentation - Monetizing Big Data

A New Dimension in ESG Investing and Accounting Quality – Quadrophobia

In finance and accounting, it is more about direction than absolute accuracy in most occasions, i.e., accounting materiality. For example, in the US, reported EPS is almost always rounded to the nearest cent rather than showing six decimal places. This seemingly innocent numerical rounding rule, however, may have serious unintended consequences. Company management is far more likely to be incentivized to round EPS upwards, a phenomenon known as Quadrophobia. We find there is a significant underrepresentation of digit four in the third decimal place in calculated EPS. Our findings show that Quadrophobia is pervasive in the US market and likely a result of accounting distortion by company management. We design a stock-selection signal called QUAD (Quadrophobia Underrepresentation of Accounting Digit) to measure this numerical rounding behavior. Our findings reveal that Quadrophobia is more prevalent in companies exhibiting large accruals, smaller EPS values, consistent EPS profile, and at risk of missing analysts’ expectations. Furthermore, we find that companies exhibiting aggressive Quadrophobia behavior tend to be penalized by the market with lower subsequent stock returns.

14:30 pm - Panel - Monetizing Big Data

Why Natural Language Processing is the Crown Jewel of A.I.

As humanity enters the intelligent era, smart devices and data volume will rise at unprecedented speeds, as will the demand for human-computer interaction through speech and language. Natural language processing (NLP) is a key area of research in Artificial Intelligence (AI) that helps computers understand, interpret and manipulate human language. Will machines ever really understand language? Can they learn knowledge or common sense? What will it take to create programs that develop reasoning? These are only some of the topics that our distinguished speakers will address during this panel discussion.

15:15 am - Break

Tea & coffee will be served

15:40 pm - Presentation - Monetizing Big Data

Machine learning based Transaction Cost Analysis in Algorithmic Trading

Performance of equity agency trading algorithms is driven by hundreds of factors with varying degrees of interaction. These factors range from client instructions and market conditions to various algorithm settings. Often recommendations to improve performance are based on past experience and intuition and employ a lot of discretion. This approach is expensive and does not scale beyond a set of focus clients. In this paper, we formulate a methodology using machine learning to sift through troves of order execution data to identify key drivers of algorithm performance and provide actionable recommendations to clients in delivering execution alpha. When a client executes an order, the entire state of the order and the market is stored in a high performance data repository. We apply machine learning algorithms on this extensive data store to search the parameter space and identify performance drivers ranked by their order of importance. Using machine learning we are able to analyze and attribute performance of algorithmic trading orders and provide clients with never before seen insights on the key drivers of execution performance beyond traditional metrics such as average daily volume, spread and volatility. This approach provides us the ability to focus on the important performance drivers and optimize those for further enhancing algorithm performance. We find this to be a highly scalable and efficient process versus current Transaction Cost Analysis (TCA) methods that focus on a standard set of metrics with few actionable insights for improving the client execution experience.

16:00 pm - Panel - Monetizing Big Data

Getting Dirty: What it Really Takes to Tease Out Alpha from Big Data

Some of today’s most successful companies have demonstrated that data appreciates in value when translated into meaningful information. Retailers are paying billions of dollars to banks so they can send targeted discount offers directly to customers by analyzing their shopping habits from their credit card transactions. Quantitative hedge funds are successfully analyzing large volumes of news and social media to generate excess returns. Banks and investment research firms are paying premiums to service providers that mine unique insights from alternative data sources. In this panel, our distinguished speakers will discuss how financial institutions are going about monetizing non-traditional big data sources to generate both investment and operational alpha.

16:50 pm - Closing Remarks

17:00 pm - Cocktail Reception at the Venue

We invite you for some well deserved entertainment, drinks and canapes after the conference
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Speakers

Last Updated: August 21, 2019

Robert J. Shapiro

Robert J. Shapiro

Economist and Political Adviser

Robert J. Shapiro is former U.S. Under Secretary of Commerce for Economic Affairs and current Chairman of Sonecon, LLC. Dr. Shapiro brings broad knowledge and experience in economics and politics based on his decades of conducting analysis and providing advice to presidents including U.S. president Bill Clinton and British Prime Minister Tony Blair, senators, representatives and governors, as well as foreign leaders and senior executives at numerous Fortune 50 and Fortune 100 companies.

Gordon Ritter

Gordon Ritter

Quantitative Trading Entrepreneur

New York University

Gordon Ritter is 2019 Buy-side Quant of the Year, adjunct professor at New York University and a former senior portfolio manager at systematic hedge fund GSA Capital in New York. Gordon founded his own quantitative trading firm; before that he was a senior portfolio manager at GSA Capital where he designed, built, and managed statistical arbitrage strategies in multiple geographies and asset classes, and directed research.

Javed Jussa

Javed Jussa

Quantitative Strategist

Wolfe Research

Javed Jussa is responsible for alpha signal, Big Data, ESG, and small-cap research and managing the day-to-day operations of the QES team. Prior to Wolfe Research, Javed was the US Head of Quantitative Strategy at Deutsche Bank. Javed also has several years of experience in the investment business with Macquarie Capital, CIBC World Markets, and IBM Consulting. Javed holds a joint Bachelors of Electrical Engineering and Computer Science and an MBA in finance and statistics.

Jason Bloomberg

Jason Bloomberg

Best Selling Author

Jason Bloomberg is a leading IT industry analyst, author, keynote speaker, and globally recognized expert on multiple disruptive trends in enterprise technology and digital transformation. He is ranked #5 on Onalytica’s list of top Digital Transformation influencers for 2018 and #15 on Jax’s list of top DevOps influencers for 2017, the only person to appear on both lists.

Joseph Simonian

Joseph Simonian

Senior Investment Strategist

Acadian Asset Management

Joseph Simonian is a Senior Investment Strategist at Acadian Asset Management. Before joining Acadian, Joseph was the Director of Quantitative Research for the Portfolio Research and Consulting Group at Natixis Investment Managers. Prior to that, he was a principal research analyst in the Global Institutional Solutions Group at Fidelity Investments. He was also previously a vice president at J.P Morgan Asset Management and PIMCO. Joseph is a noted contributor to leading finance journals and is currently the co-editor of the Journal of Financial Data Science and Advisory Board member for the Financial Data Professional Institute.

Barry Hurewitz

Barry Hurewitz

Global Head of UBS Evidence Lab Innovations

UBS

Barry Hurewitz is the Global Head of Evidence Lab Innovations, prior to his current role, Mr. Hurewitz served as Global COO for UBS Investment Research. During Barry's tenure, UBS Research was voted the top global equity research provider in the world by Institutional Investor magazine. Before joining UBS, Mr. Hurewitz was the Global COO of Investment Research at Morgan Stanley, where he founded AlphaWise.

Feargal O'Sullivan

Feargal O'Sullivan

CEO

USAM Group

Feargal O'Sullivan, Chief Executive Officer, is a proven business leader with the rare ability to blend business acumen and deep technical knowledge. With over 20-years of experience at BlackRock, Reuters and NYSE, Feargal understands the needs of clients and vendors in equal measure.

Swagato Acharjee

Swagato Acharjee

Quantitative Strategist

RBC Capital Markets

Swagato is a quantitative researcher in the equities electronic trading team at RBC. His main responsibilities are making enhancements to the electronic trading platform and working with the electronic sales and sales trading team in improving trading performance for clients. After graduating with a PhD in Engineering from Cornell University, he was a quantitative researcher in the electronic trading teams at Citi and Bank of America Merrill Lynch. Prior to joining RBC, he was a researcher and trader at a quantitative hedge fund working on statistical arbitrage strategies and long term factor models.

Yimei Guo

Yimei Guo

Managing Director & Global Head of Investment Research Technology

Morgan Stanley

Yimei Guo is a Managing Director and the Global Head of Investment Research Technology at Morgan Stanley. As a senior technology leader, she is responsible for setting strategy and delivering innovative solutions to drive business outcomes. In recent years, Yimei has been leading strategic initiatives in machine learning, data and analytics, and digital transformation. She was recently named one of IBM’s 2019 Women Leaders in AI. Throughout her 25+ year career, Yimei has built expertise spanning various business and technology areas including Investment Research, Global Capital Markets, Client Relationship Management, Content Management, and Digital Marketing.

Robert Marsh

Robert Marsh

Former Chief Product Officer at Kensho (now S&P)

Robert has more than thirty years’ experience developing and using tools, frameworks and data to solve problems in markets and investing. Previously the Chief Product Officer at Kensho, he was exposed to the challenges vendors face in supporting a varied client base, leading teams of a different culture, and managing through the before and after of a successful acquisition. Prior to this, Robert spent twenty-six years at Tudor working across a range of functions (PM, Strategist, Developer) which resulted in a well-developed understanding of the requirements demanded by large, top performing clients in a high stakes environment.

Milind Sharma

Milind Sharma

CEO

QuantZ Machine Intelligence Technologies

Milind Sharma’s 23 years of market experience span running prop desks at RBC & Deutsche Bank (Saba unit) as well as hedge funds (QuantZ) & mutual funds (MLIM). His funds have won many awards over the years including those from Morningstar, Lipper, WSJ, Battle of the Quants & BattleFin. He was also a co-founder of Quant Strategies at MLIM (now BlackRock) & was co-architect of Raven TM (derivatives risk system) at Ernst & Young. His publications have appeared in JoIM, Risk Books, Elsevier, Wiley etc. In addition to dual MS degrees he was also in the Logic/ AI PhD program at Carnegie Mellon. Other education includes Oxford, Vassar & Wharton.

Bogdan Ianev

Bogdan Ianev

Director of Equity Derivatives Structuring

Credit Suisse

With over seven years experience, Bogdan has been responsible for pricing and engineering of various equity and cross-asset exotic derivatives as well as systematic investment strategies. Prior to this, Bogdan spent three years structuring corporate derivative solutions for insurers also at Credit Suisse. He holds a Master’s degree in Mathematics of Finance from Columbia University and a Bachelor's degree in mathematics from Wabash College.

Jib Wilkinson

Jib Wilkinson

Analytics Leader

Deloitte

Jib leads Deloitte Consultings’ Analytics offerings for the Investment Management sector in the U.S. helping buy side and asset servicing clients in major technology and business transformations including data modernization, cloud enablement, data analytics, and operational improvement.

John Arabadjis, PhD

John Arabadjis, PhD

Head of Macro Strategy Products & Analytics

Bank of New York Mellon

John is the Head of Macro Strategy Products & Analytics at Bank of New York Mellon, the group responsible for developing macroeconomic models and analytics for BNYM Markets business and their clients by applying the tools of data science to vast amounts of proprietary, market and macroeconomic data sets. Before joining BNYM, John headed teams of data scientists at State Street, guiding R&D in a number of areas including behavioral finance, alternative data, multi-asset risk management, sustainable investing, private equity and quantitative investment management.

Matthew Bergerman

Matthew Bergerman

Director

DTCC

Matthew Bergerman joined the DTCC Data Products team in July of 2015 as a Director charged with the development of solutions in the Equity, Fixed Income, and Reference Data space. He has worked in the Financial Information industry for over 20 years. He has worked on the vendor side previously in the production of data and the development of data applications at Thomson Financial and Pershing.

Armando Gonzalez

Armando Gonzalez

CEO

RavenPack

Armando Gonzalez is President & CEO of RavenPack, the leading provider of big data analytics for financial institutions. Armando is an expert in applied big data and artificial intelligence technologies. He has designed systems that turn unstructured content into structured data, primarily for financial trading applications. Armando is widely regarded as one of the most knowledgeable authorities on automated text and sentiment analysis.

Peter Hafez

Peter Hafez

Chief Data Scientist

RavenPack

Peter is a pioneer in the field of applied news analytics, bringing alternative data to banks and hedge funds. He has more than 15 years of experience in quantitative finance with companies such as Standard & Poor's, Credit Suisse First Boston, and Saxo Bank.

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Location

When:
Tuesday, September 10th, 2019
8:30 AM - 5:00 PM New York Time

A cocktail reception will be held at the conference venue from 5:00 PM.

Where:
Convene Midtown West
117 W 46th St, NYC, USA

The closest subways are 7th Ave Station, 49 Street Station, and 47-50 Streets - Rockefeller Ctr Station.

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Partner Sponsors

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Email events@ravenpack.com to know more.

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Big Data is the New Currency, London, May 2019

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