2017 Symposium

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Our conference series explores innovation in data science, highlighting applications to risk management. This year’s topics are Impact Investing and Fintech, and confirmed speakers include Kay Giesecke, Adair Morse, Alex Papanicolaou, Alex Shkolnik, Philip Stark, and Kewei Tang.

The third annual CDAR Symposium, presented in partnership with State Street, will convene on October 27, 2017 from 8:45am to 5:30pm at UC Berkeley’s Memorial Stadium.

The Consortium for Data Analytics in Risk (CDAR) supports research into innovation in data science and its applications to portfolio management and investment risk. Based in the Economics and Statistics Departments at UC Berkeley, CDAR was co-founded with State Street, Stanford, and the Berkeley Institute for Data Science (BIDS). CDAR organizes conferences, workshops, and research programs, bringing together academic researchers from the physical and social sciences, and industry researchers from financial management firms and technology development companies large and small.

Please send inquiries to soum@berkeley.edu.

Agenda

The 2017 Symposium will take place on Friday, October 27th, 2017 from 8:45am to 5:30pm. *Subject to change

8:45 – 9:30 a.m. Breakfast
9:20 – 9:30 a.m. Welcome
9:30 – 10:10 a.m. CDAR Year 3: Growth and Expansion

Contributors:

    Lisa Goldberg, CDAR
    Jingmei Zhao, SWUFE
10:10 – 11:10 a.m. “Impact Investing”
Talk abstract: Impact funds, defined as venture or growth equity funds with dual objectives of generating financial returns and positive externalities, perform 7.8% below traditional VC funds in univariate statistics, or 3-4% lower returns in a willingness-to-pay (WTP) utility framework adjusting for investor choice covariates. These results need to be cast in the broader spectrum of understanding the landscape of opportunities and incentives for responsible investing. Development organizations, banks, and public pension funds have the greatest WTP, while endowments and private pensions have negative WTP. Mapping WTP to investor tastes and hindrances which could affect utility for impact, we find that Europeans, mission-oriented investors, and investors facing political pressure (often for local investing) have greater WTP for impact while legal restrictions (e.g. ERISA) lower investor WTP for impact.
Adair Morse, Associate Professor of Finance at the Haas School of Business
11:10 – 11:25 a.m. Break
11:25 – 12:25 p.m. Panel

    Alex Papanicolaou
    Alex Shkolnik
12:25 – 1:25 p.m. Lunch
1:25 – 2:25 p.m. “Don’t Bet on Your Random Number Generator”
Talk abstract: Pseudo-random numbers are used in countless contexts, including jury selection, electronic casino games, physical and chemical simulations, bank stress tests, portfolio risk simulation, numerical integration, random sampling, Monte Carlo methods, stochastic optimization, and cryptography. They are used in scientific fields from finance to particle physics to sociology. It’s tempting to think that the pseudo-random number generators (PRNGs) in common software packages and general-purpose programming languages in are “good enough” for most purposes. However, pigeonhole arguments and empirical results show that those PRNGs are not adequate even for basic statistical purposes such as random sampling, generating random permutations, and the bootstrap – even for relatively small data sets. Cryptographers have developed cryptographically secure pseudo-random number generators (CS-PRNGs), which provide a far better approximation to truly random numbers, as manufacturers of gambling machines are well aware. For most purposes, the incremental computational cost of using a CS-PRNG is negligible. Statistical packages and general-purpose programming languages should use CS-PRNGs by default.
Professor Philip Stark, Associate Dean UC Berkeley Math/Physical Sciences
2:25 – 3:25 p.m. Fintech Speaker: “Internet Techniques and Big Data in China’s Fintech Industry: Practices of Ant Financial”

Kewei Tang, Ant Financial

3:25 – 3:40 p.m. Break
3:40 – 4:50 p.m. Fintech Panel: Frontiers of Financial Technology

Moderator/Panelist: Kay Giesecke, Stanford

4:50 – 5:00 p.m. Closing Remarks

Bob Anderson, CDAR

5:00 – 6:30 p.m. Reception

 

Featured Speakers

Adair Morse, Associate Professor of Finance at the Haas School of Business
Adair Morse is Associate Professor at the Haas School of Business at the University of California at Berkeley, where she teaches New Venture Finance. She is on the Expert Panel (for oversight of the Oil Fund) to the Ministry of Finance of Norway, the board of the Haas Impact Investing Network, the faculty advisor to Haas FinTech Club, faculty mentor to Gender Equity Initiative, and faculty co-director of the Haas Impact Research Prize. She holds a Ph.D. in finance from the University of Michigan. Adair’s research spans three areas of finance: household finance, corruption, and asset management, with the unifying theme that she tries to choose topics useful for leveling economic playing fields. She has won a number of top finance research prizes, and her various works have been directly implemented into policy via U.S. Congress Acts, U.S. and Canadian state banking regulations, and Greek Parliament tax reform. Adair’s work on asset management includes work on factor investing of delegated asset managers, objectives of sovereign wealth funds, and impact investing. Her recent work studies many aspects of fintech on the lending and equity investing sides, and she has been invited to give a number of keynote addresses on the future of FinTech for consumers and investors.

 

“Impact Investing”
Talk abstract: Impact funds, defined as venture or growth equity funds with dual objectives of generating financial returns and positive externalities, perform 7.8% below traditional VC funds in univariate statistics, or 3-4% lower returns in a willingness-to-pay (WTP) utility framework adjusting for investor choice covariates. These results need to be cast in the broader spectrum of understanding the landscape of opportunities and incentives for responsible investing. Development organizations, banks, and public pension funds have the greatest WTP, while endowments and private pensions have negative WTP. Mapping WTP to investor tastes and hindrances which could affect utility for impact, we find that Europeans, mission-oriented investors, and investors facing political pressure (often for local investing) have greater WTP for impact while legal restrictions (e.g. ERISA) lower investor WTP for impact.

 

Professor Philip Stark, Associate Dean UC Berkeley Math/Physical Sciences
Philip Stark’s research centers on inference (inverse) problems, especially confidence procedures tailored for specific goals. Applications include the Big Bang, causal inference, the U.S. census, climate modeling, earthquake prediction and seismic hazard analysis, election auditing, endangered species stressors, evaluating and improving teaching and educational technology, food web models, health effects of sodium, the geomagnetic field, geriatric hearing loss, information retrieval, Internet content filters, nonparametrics (constrained confidence sets for functions and probability densities), risk assessment, the seismic structure of Sun and Earth, spectroscopy, spectrum estimation, and uncertainty quantification for computational models of complex systems. He developed methods for auditing elections that have been incorporated into laws in California and Colorado. Methods he developed or co-developed for data reduction and spectrum estimation are part of the Øersted geomagnetic satellite data pipeline and the Global Oscillations Network Group (GONG) helioseismic telescope network data pipeline. Read his full bio »

 

“Don’t Bet on Your Random Number Generator”
Talk abstract: Pseudo-random numbers are used in countless contexts, including jury selection, electronic casino games, physical and chemical simulations, bank stress tests, portfolio risk simulation, numerical integration, random sampling, Monte Carlo methods, stochastic optimization, and cryptography. They are used in scientific fields from finance to particle physics to sociology. It’s tempting to think that the pseudo-random number generators (PRNGs) in common software packages and general-purpose programming languages in are “good enough” for most purposes. However, pigeonhole arguments and empirical results show that those PRNGs are not adequate even for basic statistical purposes such as random sampling, generating random permutations, and the bootstrap – even for relatively small data sets. Cryptographers have developed cryptographically secure pseudo-random number generators (CS-PRNGs), which provide a far better approximation to truly random numbers, as manufacturers of gambling machines are well aware. For most purposes, the incremental computational cost of using a CS-PRNG is negligible. Statistical packages and general-purpose programming languages should use CS-PRNGs by default.

 

Kewei Tang, Senior Risk Expert Consultant, Micro Credit Infomation Service Co, Ant Financial; CEO, Fulin Tech Inc; A Fintech company focusing on AI Driven risk management solutions

Prior to his current position, Kewei served as Managing Director of platform risk management at Mybank, Ant Financial. His major responsibilities included applying cutting edge machine learning techniques to an online lending portfolio serving subprime customer or customers with no credit history. He is also an adjunct professor at Southwestern University of Finance and Economics in Chengdu, China. He has served as an adviser to several Chinese government committees and as a Director of the Sino-European Finance Association, London. His work has been published in several top domain journals and he presents frequently in China’s top risk management forums.

Kewei obtained his PhD from Nottingham University, England, focusing on applying artificial intelligence techniques to financial risk management. He has also worked as a quant risk executive at RBS and Barclays. During that period he played a leading roles in a few large risk management activities, organized by IMF and ECB, involving top banks.

Kay Giesecke, Associate Professor of Management Science & Engineering at Stanford University
Kay Giesecke is Associate Professor of Management Science & Engineering at Stanford University and the Paul Pigott Faculty Scholar in the School of Engineering. He is the Director of the Advanced Financial Technologies Laboratory and the Quantitative Finance Certificate Program. He is the Co-Chair of the Mathematical and Computational Finance Program. Kay is a member of the Institute for Computational and Mathematical Engineering. He serves on the Governing Board and Scientific Advisory Board of the Consortium for Data Analytics in Risk.

Kay is a financial engineer. He develops stochastic financial models, designs statistical methods for analyzing financial data, examines simulation and other numerical algorithms for solving the associated computational problems, and performs empirical analyses. Much of Kay’s work is driven by important applications in areas such as credit risk management, investment management, and, most recently, housing finance. His research has been funded by the National Science Foundation, JP Morgan, State Street, Morgan Stanley, American Express, and several other organizations.

Directions

The recommended parking lot is the Maxwell Field Stadium Lot.

For assistance with parking, please email Sang Oum.

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