Distilling the Key Risk Drivers without a Hypothesis

Distilling the Key Risk Drivers without a Hypothesis
Thought Leadership Webinar: Learn the core of unsupervised learning that is taking the industry by storm. See how risk management can be improved by switching to a fast hypothesis-less environment, trusting the data to speak for itself.
 


Presented By:
Irene Aldridge
President and Managing Director, Research
AbleMarkets


Date:
April 29, 2020


Time:
10:00 a.m. - 11:00 a.m. EDT
3:00 p.m. - 4:00 p.m. BST


Session Length:
60 minutes

 

About This Webinar

In modern risk management, most financial decisions are made with at least a few days of risk horizon. Such views ignore short-term risk events. The classic 10-Day VAR, a standard for banks, for example, misses many short-term risk events that end up costing financial institutions a pretty penny. The duration of the traditional risk measures was chosen as a balance between the application and the computational complexity. Many traditional models require extensive Monte-Carlo simulation that may take a very long time. In contrast, Big Data methods allow us to process large data sets quickly, without adding computing power, saving corporations millions of dollars by timely short-term identification of impending risk events.The Big Data Science hypothesis-less paradigm further allows to draw more reliable and efficient conclusions by removing researchers' subjectivity in posing the research question. This presentation discusses the details of implementation of the fast hypothesis-less paradigm in Big Data Science.

About Our Expert  

  
 
  Irene Aldridge is an internationally-recognized quantitative and Big Data Finance researcher, Adjunct Professor at Cornell University and President and Managing Director, Research, of AbleMarkets, a Big Data for Capital Markets company. Prior to AbleMarkets, She designed and ran high-frequency trading strategies in a $20-million cross-asset portfolio. Still previously, Aldridge was, in reverse order, a quant on a trading floor; in charge of risk quantification of commercial loans; Basel regulation team lead; technology equities researcher; lead systems architect on large integration projects, including web security and trading floor globalization. Aldridge started her career as software engineer in financial services.

Aldridge holds a BE in Electrical Engineering from Cooper Union, and MS in Financial Engineering from Columbia University, and an MBA from INSEAD. In addition, Aldridge studied in two PhD programs: Operations Research at Columbia University (ABD) and FInance (ABD). She is the author of multiple academic papers and several books. Most notable titles include “Big Data Science in Finance” (co-authored with Marco Avellaneda, Wiley, 2020), “Real-Time Risk: What Investors Should Know About Fintech, High-Frequency Trading, Flash Crashes” (co-authored with Steve Krawciw, Wiley, 2017), “High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems” (2nd edition, translated into Chinese, Wiley 2013), and “The Quant Investor’s Almanac 2011: A Road Map to Investing” (Wiley, 2010). Her recent academic publications include “Neural Networks in Finance: Design and Performance” (with Marco Avellaneda in the Journal of Financial Data Science, 2019), “Big Data in Portfolio Management” (Journal of Financial Data Science, 2019), “ETFs,High-Frequency Trading and Flash Crashes” (Journal of Portfolio Management, 2016), and “High-Frequency Runs and Flash Crash Predictability” (Journal of Portfolio Management, 2014). Aldridge presently serves on the Editorial Advisory Board for the Journal of Applied Data Science to Finance.
 

Continued Risk Learning Credits: 1

PRMIA Continued Risk Learning (CRL) programs provide you with the opportunity to formally recognize your professional development, documenting your evolution as a risk professional. Employers can see that you are not static, making you a highly valued, dynamic, and desirable employee. The CRL program is open to all Contributing, Sustaining, and Risk Leader members, providing a convenient and easily accessible way to submit, manage, track and document your activities online through the PRMIA CRL Center. To request CRL credits, please email learning@prmia.org.

  Registration  
  Membership Type Price  
       
  Sustaining, Corporate, and RIM Members $ FREE  
  Contributing Member $ 35  
  Non Member $ 75  
       

If this is your first time accessing the PRMIA website you will need to create a short user profile to register. Save on registration by becoming a member.

 
When
4/29/2020 10:00 AM - 11:00 AM
Where
Thought Leadership Webinar
 

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