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Cryptocurrencies vs. real-world finance: Stability, liquidity, the ability to hedge, and the missing 99 percent...
Jonathan Levi - ICME Stanford University
Friday, April 10, 2015, 1:00-2:00 pm Calendar
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Abstract

In this session, “Cryptocurrencies vs. real-world finance: Stability,
liquidity, the ability to hedge, and the missing 99 percent”, Levi
will highlight issues from three of his popular recent presentations,
"Small Errors in Big Data: White Noise or White Lies", "Practical
Machine Learning: Theory, Practice, and the Challenges in Making These
Two Meet", and "Theoretical Financial Mathematics Meets Real Data."
The presentation will focus on the related challenges that should be
considered in the context of financial quantitative analysis and
crypto-currencies.

Bio

Bio + Abstract:
Jonathan Levi is a pioneer and visionary in the field of scientific
computing and complex quantitative financial systems. Levi's work as a
quantitative strategist in the financial services industry in London &
New York where he worked for eight years for Standard and Poor's,
Barclays Capital and Goldman Sachs on complex quantitative systems
revolutionized the field. Prior to that time, he worked for the
Israeli Defense Forces on mission critical, military grade
cryptographic systems with zero error tolerance and Cisco Systems
(Network Management Technology Group) on FIPS certification of the
crypto code-base for the National Security Agency.


He is currently a researcher in Computational & Mathematical
Engineering at Stanford University. Premised on the belief that no
single model can capture 100% of the complex attributes of live
securities markets - his current research focuses on analyzing large
scale financial market data empirically to build systems that perform
forensic analysis and learn about the intrinsic characteristics of
financial markets.

This talk is organized by Yupeng Zhang