Consortium for Data Analytics in Risk

CDAR Year 3: Growth and Expansion

Lisa Goldberg, co-Director, CDAR

Jingmei Zhao, Dean, School of Finance, SWUFE

“Web Media and Stock Markets : A Survey and Future Directions from a Big Data Perspective”
Qing Li, School of Economic Information Engineering, SWUFE

Talk abstract: Stock market volatility is influenced by information release, and public acceptance. With the increasing volume and speed of social media, the effects of Web information on stock markets are becoming increasingly salient. However, studies of the effects of Web media on stock markets lack both depth and breadth due to the challenges in automatically acquiring and analyzing massive amounts of relevant information. In this study, we systematically reviewed 229 research articles on quantifying the interplay between Web media and stock markets from the fields of Finance, Management Information Systems and Computer Science. In particular, we first categorized the representative works in terms of media type and then summarized the core techniques for converting textual information into machine-friendly forms. Finally, we compared the analysis models used to capture the hidden relationships between Web media and stock movements. Our goal is to clarify current cutting-edge research and its possible future directions to fully understand the mechanisms of Web information percolation and its impact on stock markets from the perspectives of investors’ cognitive behaviors, corporate governance, and stock market regulation.

The Dispersion Bias: Correcting a Large Source of Error in Minimum Variance Portfolios
Alex Shkolnik, Postdoc, CDAR
Alex Papanicolaou, Postdoc, CDAR

Abstract: Estimation error has plagued quantitative finance since Markowitz launched modern portfolio theory in 1952. Using random matrix theory, we characterize a source of bias in the sample eigenvectors of financial covariance matrices. Unchecked, the bias distorts weights of minimum variance portfolios and leads to risk forecasts that are severely biased downward. To address these issues, we develop an eigenvector bias correction. Our approach is distinct from the regularization and eigenvalue shrinkage methods found in the literature. We provide theoretical guarantees on the improvement our correction provides as well as estimation methods for computing the optimal correction from data.  

“Don’t Bet on Your Random Number Generator”
Philip Stark, Department of Statistics, UC Berkeley

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. 

Fintech Speaker: “Internet Techniques and Big Data in China’s Financial Industry: Practices in Ant Financial”
Kewei Tang, CEO, Fulin Tech

Talk abstract: In China, internet techniques continue to improve efficiency, increase capability and expand boundaries for China’s financial industry, including banking, wealth management, mobile payments and insurance. Ant Financial, as the world-leading Fintech Giant, has built several strong internet-only business arms that are largely data-driven financial services. In its first decade, with only 7000 employees, it serves 500M customers domestically and 1.2B globally. This presentation shows how internet techniques and big data are helping to reshape China’s financial industry, with Ant Financial as a good example. 

Financial Technology Panel

Moderator: Kay Giesecke, Professor, Stanford University

Panelists:
John Arabadjis, Head of GX Labs, State Street
Jeffrey Bohn, Head of the Swiss Re Institute
Warren Pennington, Principal, Vanguard
Robert Sears, Executive Vice President, BBVA

This panel will discuss recent developments in the area of financial technologies. Topics include data and AI, online and mobile payments, blockchain, cryptocurrencies, and roboadvisory.