Please join us for our 5th Annual CDAR Symposium on Friday, October 11, 2019, from 8:30 am to 6:30 pm at UC Berkeley’s Memorial Stadium. Our conference will feature new developments in data science, highlighting applications to finance and risk management. The program features Yuan (Alan) Qi from Ant Financial, Pete Kyle from the University of Maryland, Roberto Rigobon from MIT, Jeff Bohn from Swiss Re, Solomon Hsiang from UC Berkeley and CDAR’s co-Director Robert Anderson.
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 is in partnership with Stanford, Berkeley Institute for Data Science (BIDS), Southwestern University of Finance and Economics (SWUFE), Swiss Re based in Switzerland, AXA Rosenberg, and Innovation Centre Denmark (ICDK). 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 firstname.lastname@example.org.
The 5th Annual Symposium 2019 will take place on Friday, October 11th, 2019 from 8:30am to 6:30pm. *Subject to change
|8:30 – 9:00 a.m.||Registration & Breakfast|
|9:00 – 9:10 a.m.||CDAR Year 5
Lisa Goldberg, co-Director, CDAR
|9:10 – 10:00 a.m.||“Machine learning for
Yuan (Alan) Qi, Ant Financial
Talk Abstract: Machine learning is a critical driving force for inclusive finance. In this talk, I will discuss some important challenges we face in addressing inclusive finance problems, and present several machine learning technologies we have developed to overcome these challenges and their applications in microlending, risk control, insurance claiming process, marketing and customer service.
|10:00 – 10:50 a.m.||“Aggregate Confusion: Divergence in ESG Ratings”
Roberto Rigobon, MIT
Talk Abstract: This paper investigates the divergence of environmental, social, and governance (ESG) ratings. First, the paper documents the disagreement between the ESG ratings of five prominent rating agencies. The paper proceeds to trace the disagreement to the most granular level of ESG categories that is available and decomposes the overall divergence into three sources: Scope divergence related to the selection of different sets of categories, measurement divergence related to different assessment of ESG categories, and weight divergence related to the relative importance of categories in the computation of the aggregate ESG score. We find that measurement divergence explains more than 50 percent of the overall divergence. Scope and weight divergence together are slightly less important. In addition, we detect a rater effect, i.e., the rating agencies’ assessment in individual categories seems to be influenced by their view of the analyzed company as a whole. The results allow investors, companies, and researchers to understand why ESG ratings differ.
|10:50 – 11:20 a.m.||BREAK|
|11:20 – 12:10 p.m.||“Pricing Risks from Climate Change”
Solomon Hsiang, UC Berkeley
Talk Abstract: Understanding the social and economic consequences of climate change is fundamentally a risk analysis, since climate change alters the underlying probabilities that different types of natural events (e.g. hurricanes) occur. I will describe many of the advances we have made in applying risk analysis to evaluate the potential impacts of climate change on society and the economy, from affecting civil wars and human health to slowing GDP growth and accelerating asset depreciation. I will discuss how these analyses affect what we understand about the value of investments that mitigate climate change or help us adapt to its effects.
|12:10 – 1:10 p.m.||LUNCH|
|1:10 – 2:00 p.m.||“Long-History PCA Estimation of Stock Return Factor Models”
Robert Anderson, CDAR
Talk Abstract: Traditionally, practitioners have believed that stock return factor models are not stable over histories longer than a medium horizon of TM = 250 or TM = 500 trading days (one or two years). We first show that Principal Components Analysis (PCA) consistently estimates factor loadings under essentially arbitrary variable volatility of factors. Then, we demonstrate empirically that the use of long histories such as TL = 1750 trading days, or seven years, improves the performance of optimized portfolios, compared to using a medium history of TM days, even when corrected for excess dispersion as proposed by Goldberg, Papanicalaou and Shkolnik (GPS). We find that the factor loadings of individual stocks and portfolios on long-horizon PCA factors are, in fact, relatively stable. We estimate factor loadings with a long history TL and estimate the current covariance matrix of the factors with a short data history (half-life TS = 40 days).
|2:00 – 2:30 p.m.||BREAK|
|2:30 – 3:20 p.m.||“Dimensional Analysis, Leverage Neutrality, and Market Microstructure Invariance”
Albert (Pete) Kyle, University of Maryland
Talk Abstract: This paper combines dimensional analysis, leverage neutrality, and a principle of market microstructure invariance to derive scaling laws expressing transaction costs functions, bid-ask spreads, bet sizes, number of bets, and other financial variables in terms of dollar trading volume and volatility. The scaling laws are illustrated using data on bid-ask spreads and number of trades for Russian and U.S. stocks. These scaling laws provide practical metrics for risk managers and traders; scientific benchmarks for evaluating controversial issues related to high frequency trading, market crashes, and liquidity measurement; and guidelines for designing policies in the aftermath of financial crisis.
|3:20 – 6:00 p.m.||RECEPTION|
|3:50 – 4:50 p.m.||“Exploring elusive end-to-end implementations of transformative artificial intelligence (AI) & machine learning (ML)”
Mallik Tatipamula, Ericsson North America
Nicole Hu, One Concern
Stuart Evans, CMU-Emirates iLab
The collection of widely available artificial intelligence (AI) & machine learning (ML) tools has become a catalyst for new approaches to transform processes ranging from image recognition to self-driving vehicles. Some of these AI & ML tools have revolutionized the way advertising targets consumers. Despite these successes, AI & ML tools have yet to materially transform most large corporations. Regardless, most large firms continue to invest heavily in these areas. These investments tend to be skewed toward algorithm development with insufficient investment in data collection & curation and organizational education & design. Little investment focuses on how best to make productive use of insights arising from these tools. This lack of focus on end-to-end implementations of these AI & ML tools is one hypothesis for why these new tools have had so little impact on corporate profitability. Another hypothesis for failure relates to mis-application of particular approaches/algorithms to use cases.
This panel will explore these hypotheses and discuss the state-of-the-art in AI & ML for large firms with a focus on what can be done to realize value from investments in workflow areas where these tools should produce more value.
Areas where AI & ML tools should be more productive (but are not—despite considerable investment):
|4:50 – 5:00 p.m.||Closing Remarks
Lisa Goldberg, CDAR
Professor Albert S. (Pete) Kyle is a Distinguished University Professor at the Smith School of Business, University of Maryland, where he has been the Charles E. Smith Chair Professor of Finance sincce 2006. He earned is B.S. degree in mathematics from Davidson College (summa cum laude, 1974), studied philosophy and economics at Oxford University as a Rhodes Scholar from Texas (1974-1977), and completed his Ph.D. in economics at the University of Chicago in 1981. He has been a professor at Princeton University (1981-1987), the University of California Berkeley (1987-1992), and Duke University (1992-2006).
His current research focusses on market microstructure invariance and smooth trading. More generally, his research area are market microstructure, including topics such as high frequency trading, informed speculative trading, market manipulation, price volatility, the informational content of market prices, market liquidity, and contagion.
His teaching interests include market microstructure and institutional asset management, and asset pricing.
He is the 2018 recipient of The CME Group–MSRI Prize in Innovative Quantitative Applications, a fellow of the American Finance Association (2014), and a fellow of the Econometric Society (since 2002). He holds an honorary doctoral degree from the Stockholm School of Economics (2013). He has been a board member of the American Finance Association (2004-2006), a staff member of the Presidential Task Force on Market Mechanisms (Brady Commission, 1987), a consultant to the SEC’s Office of Inspector General, a member of NASDAQ’s economic advisory board (2005-2007), a member of the FINRA economic advisory committee (2010 to present), and a member of the CFTC’s Technology Advisory Committee (2010-2011).
Jeff Bohn, Swiss Re Institute
Dr. Bohn is the Chief Research & Innovation Officer and Head of Research & Engagement at the Swiss Re Institute. Most recently, he served as Chief Science Officer and Head of GX Labs at State Street Global Exchange in San Francisco. Before moving back to California, he established the Portfolio Analytics and Valuation Department within State Street Global Markets Japan in Tokyo. (He is fluent in Japanese.) He previously ran the Risk and Regulatory Financial Services consulting practice at PWC Japan.
Past appointments for Dr. Bohn include Head, Portfolio Analytics and Economic Capital at Standard Chartered Bank in Singapore and General Manager, Financial Strategies group at Shinsei Bank in Tokyo where he supervised implementation of best-practice risk and capital analytics. Before moving to Asia, he led Moody’s KMV’s (MKMV’s) Global Research group and MKMV’s Credit Strategies group.
Dr. Bohn often conducts seminars on topics ranging from credit instrument valuation & portfolio management to machine learning. He has published widely in the area of credit risk. He co-authored with Roger Stein Active Credit Portfolio Management in Practice (Wiley, 2009). His recent research focuses on reinforcement learning, causal inference, factor modeling, and large-scale risk simulations. Dr. Bohn is an affiliated researcher at U.C. Berkeley’s Center for Risk Management Research and serves as a board member for the Consortium for Data Analytics in Risk (CDAR) spanning U.C. Berkeley, Stanford and several industry partners. On occasion, he teaches financial engineering at U.C. Berkeley, National University of Singapore’s Risk Management Institute, and Tokyo University.
Nicole Hu, One Concern
Ms. Hu serves as the CTO and Co-Founder of One Concern, a Menlo Park-based benevolent AI company with a mission to save lives and livelihoods before, during and after natural disasters. As the CTO for One Concern, Ms. Hu leads the company’s diverse team of technology specialists and hazard scientists, as they combine AI and human learning to predict the impact of natural disasters including earthquakes, floods and fires. In 2016, Forbes named Ms. Hu one of the world’s top innovators in its “30 Under 30” edition. Prior to launching One Concern with her co-founders, Ahmad Wani and Tim Frank, Ms. Hu, who is of Chinese descent and grew up India, was one of the first AI software developers for India’s Amazon competitor, Flipkart. Ms. Hu received her Master’s degree in machine learning from Stanford, where she studied under noted AI pioneer, Andrew Ng.
Roberto Rigobon is the Society of Sloan Fellows Professor of Management and a Professor of Applied Economics at the MIT Sloan School of Management. He is also a research associate of the National Bureau of Economic Research, a member of the Census Bureau’s Scientific Advisory Committee, and a visiting professor at IESA.
Roberto is a Venezuelan economist whose areas of research are international economics, monetary economics, and development economics. Roberto focuses on the causes of balance-of-payments crises, financial crises, and the propagation of them across countries—the phenomenon that has been identified in the literature as contagion. Currently he studies properties of international pricing practices, trying to produce alternative measures of inflation. He is one of the two founding members of the Billion Prices Project, and a co-founder of PriceStats.
Roberto joined the business school in 1997 and has won both the “Teacher of the Year” award and the “Excellence in Teaching” award at MIT three times.
Robert M. Anderson is a Co-Director of the Consortium for Data Analytics in Risk at UC Berkeley. He is also Professor of the Graduate School, Coleman Fung Professor Emeritus of Risk Management, and Professor Emeritus of Economics and Mathematics at UC Berkeley. He received his B.Sc. in Mathematics from the University of Toronto in 1973 and his Ph.D. from Yale University in Mathematics in 1977, under the supervision of Shizuo Kakutani. He spent a year as McMaster Fellow at McMaster University in 1977-78, and then went to Princeton as Assistant Professor of Economics of Mathematics from 1978 to 1982 and Associate Professor of Economics in 1982-83. He has been at Berkeley since 1983. He was named an Alfred P. Sloan Research Fellow in 1982 and a Fellow of the Econometric Society in 1988. His research has ranged from the intersection between probability theory and logic, to general equilibrium theory, to mathematical finance. His current research focuses on the determination of portfolio returns. He has been active in University governance, having served as President of the Student’s Administrative Council at the University of Toronto in 1973-74, as Chair of the Economics Department at Berkeley, and as Parliamentarian of the Berkeley Division of the Academic Senate. He has taken on numerous assignments for the University of California system Academic Senate, including Vice Chair and Chair of the UC Academic Senate and Faculty Representative to the Board of Regents in 2010-12. He received the Berkeley Faculty Service Award in 2009 and the Berkeley Social Science Service Award in 2013.
Solomon Hsiang combines data with mathematical models to understand how society and the environment influence one another. In particular, he focuses on how policy can encourage economic development while managing the global climate. His research has been published in Nature, Science, and the Proceedings of the National Academy of Sciences.
Hsiang earned a BS in Earth, Atmospheric and Planetary Science and a BS in Urban Studies and Planning from the Massachusetts Institute of Technology, and he received a PhD in Sustainable Development from Columbia University. He was a Post-Doctoral Fellow in Applied Econometrics at the National Bureau of Economic Research (NBER) and a Post-Doctoral Fellow in Science, Technology and Environmental Policy at Princeton University. Hsiang is currently the Chancellor’s Associate Professor of Public Policy at the University of California, Berkeley and a Research Associate at the NBER.
Stuart Evans, CMU-Emirates iLab
Dr. Stuart Evans is a board member, educator, author, and expert on dynamic high-tech ventures. As a Distinguished Service Professor, he shares his expertise by teaching related coursework for our degree programs in Silicon Valley, M.S. in Software Management and M.S. Technology Ventures. Additionally, Stuart is the Director of the CMU-Emirates iLab, a partnership between III and Emirates Airlines for innovative education and research specialized for the airline industry.
Yuan (Alan) Qi
Dr. Yuan (Alan) Qi is Chief AI Scientist of Ant Financial Services Group, and lead of Alibaba DAMO Academy Financial Intelligence. Before joining Alibaba and Ant Financial, he obtained his PhD from MIT and tenured associate professorship in Computer Science and Statistics from Purdue University. He previously served as associate editor of Journal of Machine Learning Research and area chair of International Conference on Machine Learning. At Ant Financial, he leads the AI department to build AI tools and solutions to address various financial problems, empowering both internal and external business partners.