Consortium for Data Analytics in Risk - CDAR

07/28/2017

The Consortium for Data Analytics in Risk (CDAR) invites applications for a postdoctoral fellowship to begin on or after September 1, 2017. The successful applicant will join a dynamic research group of financial economists, statisticians, mathematicians and engineers dedicated to deepening our understanding of financial markets. We welcome applications from individuals who have outstanding computing skills, a track record of scholarship in one of data science or financial economics, and an interest in applying data science to financial economics. Application due: August 25, 2017.

Lionel Martellini (EDHEC-Risk Institute), Mass Customisation versus Mass Production in Retirement Investment Management: Addressing a “Tough Engineering Problem” Tuesday, July 25, 2017

07/27/2017

The Consortium for Data Analytics in Risk (CDAR) is delighted to announce a new founding member: the Southwestern University of Finance and Economics (SWUFE) based in Chengdu, Sichuan, China. SWUFE joins a growing network of faculty and researchers from UC Berkeley, Stanford, State Street, and financial management firms and technology development companies large and small.

Lionel Martellini (EDHEC-Risk Institute), Mass Customisation versus Mass Production in Retirement Investment Management: Addressing a “Tough Engineering Problem” Tuesday, July 25, 2017

06/09/2017

Is the hot hand phenomenon real? The shooting records of Splash Brothers Steph Curry and Klay Thompson tell us the answer https://goo.gl/Bpek2W

Alon Daks, Nishant Desai, Lisa Goldberg* June 1, 2017 All basketball fans know about the hot hand: pass to a teammate on a scoring streak because he is likely to make the next basket. This venerated principle was discredited in 1985 by the legendary Israeli psychologist, Amos Tversky and two co-aut...

04/19/2017

Join us for our next seminar: John Arabadjis, State Street on April 25 @ 11am in 639 Evans Hall http://bit.ly/2pSxqSq

04/11/2017

Tomorrow, 11 am @ UC Berkeley ”Minimum Conditional Expected Drawdown Portfolios” by Alex Papanicolaou. From the abstract:"In this talk, I will present ongoing work aimed at computations for Conditional Expected Drawdown, a recently developed extreme risk measure on maximum drawdown, look at risk-based asset allocation under CED and how it compares with other risk measures, CED risk attribution, and more" http://bit.ly/2o2cLuL

03/15/2017

Join us for the Berkeley Statistics Annual Research Symposium (BSTARS) on March 23rd from 1:30pm-8:30pm at The Alumni House, UC Berkeley.

We will explore the latest research developments to solve statistical problems encountered in industry. This conference consists of keynote lectures, introductions by PhD students about their thesis work, and presentations of industrial research.
More information here: http://statistics.berkeley.edu/bstars-2017

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03/14/2017

Calling all RESEARCHERS and DATA SCIENCE buffs!!
The Berkeley Institute for Data Science - BIDS invites applications for their Fellows Program. Join a network of faculty, postdoctoral researchers, students, staff, and alumni connected by the passion to advance data analysis in the research sciences.

More info at: https://bids.berkeley.edu/news/call-fellows%E2%80%94spring-2017

We are excited to invite applications for our next cohort in the BIDS Data Science Fellow Program. Successful applicants will join our current cohort of fellows in helping advance data analysis in the research sciences. BIDS data science fellows are postdoctoral scholars, graduate student researche...

02/21/2017

Feb 21, 11am @ UC Berkeley**
Paul Kaplan, Morningstar: A Popularity Asset Pricing Model

Abstract: This paper presents a formal model for theory of popularity as laid out informally by Idzorek and Ibbotson in their seminal paper, “Dimensions of Popularity (Journal of Portfolio Management, 2014). The paper does this by extending the capital asset pricing model (CAPM) to include security characteristics that different investors regard differently. This leads to an equilibrium in which: 1) The expected excess return on each security is a linear function of its beta and its popularity loadings which measure the popularity of the security based on its characteristics relative to the those of the beta-adjusted market portfolio; 2) Each investor holds a different portfolio based on his attitudes toward security characteristics; and 3) The market portfolio is not on the efficient frontier. I call this extended model the Popularity Asset Pricing Model, or PAPM for short.

**CDAR hosts Risk Seminars and other learning opportunities that are free to the public. More info @ http://cdar.berkeley.edu/events/

This paper presents a formal model for theory of popularity as laid out informally by Idzorek and Ibbotson in their seminal paper, “Dimensions of Popularity (Journal of Portfolio Management, 2014). The paper does this by extending the capital asset pricing model (CAPM) to include security characteri...

01/19/2017

The inaugural Artificial Intelligence in Fintech Forum hosted at Stanford School of Engineering is today!

Join academic and industry professionals to:
- share latest technology advancements and research
- discuss use cases and technical trends/challenges
- establish a model for ongoing technical dialog and partnerships

Please register for this free event at: https://icme.stanford.edu/events/ai-fintech-forum

The bios of the speakers at available at: https://icme.stanford.edu/node/1658

Thursday, January 19, 2017 This event is now sold out, but click here if you would like to be added to the waitlist. This event is free of charge. The inaugural Artificial Intelligence in Fintech Forum at Stanford School of Engineering is sponsored by the Stanford Institute for Computational & Mat...

11/28/2016

November 29 @ 11:00 am - 1:00 pm, 639 Evans Hall at UC Berkeley***

CDAR Co-Director Robert M. Anderson speaks on PCA with Model Misspecification.

Principal Component Analysis (PCA) relies on the assumption that the data being analyzed is IID over the estimation window. PCA is frequently applied to financial data, such as stock returns, despite the fact that these data exhibit obvious and substantial changes in volatility. We show that the IID assumption can be substantially weakened; we require only that the return data is generated by a single distribution with a possibly variable scale parameter.

***CDAR hosts Risk Seminars (open to the public) every Tuesday. For more info, visit: http://cdar.berkeley.edu/fall-2016-risk-seminars/

11/14/2016

November 15 @ 11:00 am - 1:00 pm, 639 Evans Hall at UC Berkeley***

TruValue Labs' Jim Hawley & Hendrik Bartel will speak on Big Data Analytics and ‘Non-Financial’ Sustainability Information—uses of and initial findings from TruValue Labs’ first years.

TruValue Labs generates real-time ESG/sustainability data using natural language processing, machine learning and elements of AI to quantify unstructured (text) information sources. We present an overview of how this quantification process works and follow on by examining a variety of techniques used to analyze this data output.

***CDAR hosts Risk Seminars (open to the public) every Tuesday from 11:00 am - 1:00 pm @ 639 Evans Hall, UC Berkeley. For more info, visit: http://cdar.berkeley.edu/fall-2016-risk-seminars/

Jim Hawley & Hendrik Bartel, TruValue Labs: Big Data Analytics and ‘Non-Financial’ Sustainability Information—uses of and initial findings from TruValue Labs’ first yearsWe present an overview of the current state of ESG…

11/11/2016

November 13 @ 8:00 am - November 16 @ 5:00 pm

Integral Development Corp.'s Alex Papanicolaou, a CDAR Postdoctoral Researcher, will speak at The 2016 INFORMS Annual Meeting at the Music City Center & Omni Nashville Hotel.

will feature plenaries and keynotes; panel discussions; tutorials; and thousands of oral and poster presentations from leading academics, industry experts, students, and representatives of government agencies. Alex will be presenting on Machine Learning for Finance.