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EVENT DETAILS |
Quantopian's Quantitative Finance & Algorithmic Trading Conference
Level the Playing Field
The 4th annual QuantCon NYC will feature expert workshops & talks, with a clear focus on algorithmic trading & portfolio optimization, & how data science, alternative data sets, & machine learning, can help you craft & improve on your trading strategies.
Exclusive access to videos & presentations from both QuantCon NYC 2017 & 2018 are included with every ticket purchase.
Due to high demand we have added an additional day of workshops!
These will be held on Thursday, April 26th.
The QuantCon Keynote Talks
April 28th
'The 7 Reasons Most Machine Learning Funds Fail'
by Dr. Marcos Lpez de Prado, CEO of True Positive Technologies
The rate of failure in quantitative finance is high, & particularly so in financial machine learning. The few managers who succeed amass a large amount of assets, & deliver consistently exceptional performance to their investors. However, that is a rare outcome, for reasons that will become apparent in this presentation. Over the past two decades, I have seen many faces come & go, firms started & shut down. In my experience, there are 7 critical mistakes underlying most of those failures.
'IQ from IP: Simplifying Search in Portfolio Choice'
by Dr. Lauren H. Cohen, L.E. Simmons Professor at Harvard Business School
Using a novel database that tracks web traffic on the SEC's EDGAR servers between 2003 & 2016, we show that mutual funds exert effort to reduce the dimensionality of their portfolio selection problem. Specifically, we show that mutual fund managers' gather information on a very particular subset of firms & insiders, & their surveillance stays largely unchanged over time.
This tracking has powerful implications for their portfolio choice, & its information content. An institution that downloaded an insider-trading filling by a given firm last quarter increases its likelihood of downloading an insider-trading filing on the same firm by more than 41.3 % this quarter, which is 8 times larger than the unconditional probability of an institution downloading at least one insider trading filing in a quarter from any firm in her existing portfolio (4.8%). Moreover, the average tracked stock that an institution sells generates 7.5% annualized DGTW-adjusted alpha, whereas the sale of an average non-tracked stock has close to zero DGTW adjusted alpha.
The outperformance of tracked trades continues for a number of quarters following the tracked insider/institution sale & does not reverse within the sample period. Collectively, these results suggest that the information in tracked trades is important for fundamental firm value, & is only revealed following the information-rich dual trading by insiders & linked institutions.
QuantCon NYC 2018 welcomes everyone who wants to learn about algorithmic trading, quantitative finance, data science, machine learning, & Python.
Quants, analysts, data scientists, programmers, researchers, C-level executives, portfolio managers, hedge fund professionals, traders, & students are all among past attendees.
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