Morgan Downey is the CEO of Money.Net. Prior to Money.Net, Morgan was Global Head of commodities at Bloomberg, LP. Morgan managed development of the Bloomberg Professional terminal. At Bloomberg, Morgan used his market experience to build a suite of revolutionary, unique, and innovative products. Before Bloomberg, Morgan spent 15 years running trading desks, as manager and head trader, for banks including Citibank, Bank of America and Standard Chartered, in the US, UK, Australia, and Singapore. Morgan is the author of the book ‘Oil 101‘, a best-seller explaining the oil industry.
RavenPack is a leading provider of Big Data Analytics for financial applications. Financial firms rely on RavenPack to abbreviate the tasks of categorizing and managing unstructured content, turning it into structured data for ease of analysis and deployment.
RavenPack has proven applications across financial services firms, from quantitative trading to retail broking, from risk management to market surveillance. The company’s clients include some of the best performing hedge funds, banks and investment managers in the world.
For more than 40 years, MSCI’s research-based indexes and analytics have helped the world’s leading investors build and manage better portfolios. Clients rely on our offerings for deeper insights into the drivers of performance and risk in their portfolios, broad asset class coverage and innovative research.
Our line of products and services includes indexes, analytical models, data, real estate benchmarks and ESG research.
MSCI serves 97 of the top 100 largest money managers, according to the most recent P&I ranking. For more information, visit us at www.msci.com.
By Irene Aldridge
What is Big Data Finance? For many financial practitioners, “big data” is still just a buzzword and “finance” is business as usual. However, looking at the hottest-financed areas of business, one uncovers particular trends that move beyond buzz into billion-dollar investments. According to Informilo.com, for instance the fastest-growing areas of big data in finance in 2015 were:
- Payment services
- Online loans
- Automated investing
- Data analytics
Each of these areas, in turn, translates into automation. The payment services businesses, such as TransferWise, harness technology to commoditize counterparty risk computations. Counterparty risk is a risk of payment default by a money-sending party. Some twenty years ago, counterparty risk was managed by human traders, and all settlements took at least three business days to complete, as multiple levels of verification and extensive paper trails were required to ensure that transactions indeed took place as reported. Fast-forward to today, and ultra-fast technology enables transfer and confirmation of payments in just a few seconds, fueling a growing market for cashless transactions.
Similarly, the loan markets used to demand labor-intensive operations. When I took my first executive job running a quant analysis of the commercial loan business for a Canadian bank, my “old-time” peers still judged the creditworthiness of the bank’s business borrowers during a round of golf and drinks with the company’s executives. Of course, quantitative credit-rating models such as the one by Edward Altman of New York University, have proved invariably superior for predicting defaults than most human experts, enabling faster online loan approvals. Online loan firms now harness these quantitative credit-modeling approaches to produce fast reliable estimates of credit risk and to determine the appropriate loan pricing.
Big Data Finance enables fast market-risk estimation and the associated custom portfolio management. For example, investors of all stripes can now choose to forgo expensive money managers in favor of democratic investing platforms, such as Motif Investing. For as little as $9.95, investors can buy baskets of ETFs pre-selected on the basis of particular themes. Companies like AbleMarkets.com offer real-time risk evaluation of markets, aiding judgement of market-making and execution traders with real-time inferences from the market data, including proportion of high-frequency traders and institutional investors present in the markets at any given time.
Underlying all these developments are the advances in scalable architecture and data management. Ultra-fast computation and data processing are critical enablers of other forms of Big Data Finance investing. Several companies have lately generated multi-billion dollar valuations by providing analytics in the Software-as-a-Service (SaaS). For instance, Kensho is delivering the power of human-language queries in customers’ data, and have been rolled out across Goldman Sachs.
The upcoming Big Data Finance conference at NYU Courant brings together some of the top innovators, business leaders and regulators in the space to profile a detailed picture of the exciting (and well-financed) future of this industry. Notable presenters include Prof. Marco Avellaneda of NYU Courant, the conference Chair and an expert in risk and portfolio optimization, Sebastian Ceria, CEO of Axioma, a risk-management technology company and Prof. Bud Mishra, the expert in the blockchain technology, one of the hottest engines of automated payments. The future of finance is here, and its name is Big Data Finance.
Marco Avellaneda (PhD Univ. of Minnesota, 1985) specializes in applied mathematics, probability and statistics. Most of his research of the last 10-15 years involves applications of mathematics and statistics to financial markets, derivatives, portfolio management and risk management. His work gets published in specialized journals such as Quantitative Finance , Risk Magazine, International Journal of Theoretical and Applied Finance, and other publications read by practitioners as well as theoreticians. He was named *Quant of the Year 2010* by Risk Magazine, for an article on hard-to-borrow stocks and their effect on equity options pricing. Marco is associated with the consulting firm Finance Concepts, which he founded in 2003. His current interests are in internet-delivered financial risk-management systems for buy-side firms.
Deltix provides software and services to buy-side and sell-side firms for quantitative research and algorithmic trading. We cover data collection and aggregation, advanced analytics, model development, back-testing, simulation and live trading.
Deltix’s expertise includes applying complex mathematics to big data sets to help clients gain actionable insights and perform “intelligent trading”.
MCObject’s eXtremeDB Financial Edition delivers the most powerful solution for managing time series data (including market data) while maximizing developer productivity through open, developer-preferred languages including industry-standard SQL, Python, C/C++, Java, and C#.
Learn more by visiting MCObject’s website right now: http://financial.mcobject.com/
Bud Mishra is a professor of computer science and mathematics at NYU’s Courant Institute of Mathematical Sciences, professor of human genetics at Mt Sinai School of Medicine, and a professor of cell biology at NYU School of Medicine. Bud has a degree in Sciences from Utkal University, in Electronics and Communication Engineering from Indian Institute of Technology (IIT), Kharagpur, and MS and PhD degrees in Computer Science from Carnegie-Mellon University. Bud is also a visiting scholar at CSHL’s Center for Quantitative Biology. From 2001-04, he was a professor at the Watson School of Biological Sciences, Cold Spring Harbor Lab (CSHL) and from 2003-2006, a Visiting Professor at Tata Institute of Fundamental Research (TIFR).
Bud is an IIT, Kharagpur Distinguished Alumnus, NYSTAR Distinguished Professor, AAAS Fellow (engineering: robotics, hardware verification and computational biology), IEEE fellow (robotics and automation) and a fellow of the ACM (computational biology and symbolic computation).
His other research activities, outside of computational and systems biology, take place in the newly created Laboratory for Entrepreneurship in Data Sciences (LEDS) focusing on challenges from Finance, Advertising and Ad Technology, Philanthropy, Biomedicine and Engineering. Somewhat immodestly (and with apologies to Albert Arnold “Al” Gore), the laboratory aims to reinvent the Internet of the future.
Suite LLC, vendor of the ALib™ fixed-income analytic pricing/risk library, sponsors Big Data Finance 2016 conference.
Suite LLC’s analytics are relied on by the industry’s leading banks, hedge funds and brokers for dealer-grade pricing and risk management of an extensive range of cash and derivatives instruments:
- Advanced and proven yield-curve generation routines for trade/portfolio pricing and sensitivity analysis.
- Broad asset coverage including broad support for government and corporate bonds, Swaps and other Rates Derivatives, listed and OTC instruments alike.
- Credit Derivatives.
- Deploy as standalone desktop solution interoperable with Excel® or MATLAB®, or connect to in-house or 3rd-party vendor applications through well-documented API’s.
- Reliable performance in HPC/HFT environments coupled with transparency to prove-out any calculation.
Please visit suitellc.com for more information.
Sebastian Ceria is Chief Executive Officer of Axioma and founded the company in 1998. Prior to Axioma, Sebastian was an Associate Professor of Decision, Risk and Operations at Columbia Business School from 1993 to 1998.
Sebastian has worked extensively in the area of optimization and its application to portfolio management. He is the author of many articles in publications including Management Science, Mathematical Programming, Optima and Operations Research. Most recently, Sebastian’s work has focused on the area of robust optimization in portfolio management. He has co-authored numerous papers on the topic, including, “Incorporating Estimation Errors into Portfolio Selection: Robust Portfolio Construction,” published in the Journal of Asset Management, “To Optimize or Not to Optimize: Is That the Question?” published in the Oxford Handbook of Quantitative Management, and “Factor Alignment Problems and Quantitative Portfolio Management,” published in the Journal of Portfolio Management. He is a recipient of the Career Award for Operations Research from the National Science Foundation.
Sebastian completed his PhD in Operations Research at Carnegie Mellon University’s Tepper School of Business, and his undergraduate degree in Applied Math at the University of Buenos Aires, Argentina.