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EVENT DETAILS |
HOSTED BY
Quantopian
Break Down Wall Street's Barriers with Us
QuantCon, a disruptive quant trading event, will break down the existing walls to algorithmic trading by giving you an inside look at tools and content sets typically available only to Wall Street.
Through key experts leading innovative talks and workshops, you will learn how to improve your investment performance by exploring algorithmic trading strategies and applying open-sourced ethos to your investment ideas.
Attend and come away feeling empowered and prepared to advance your investment strategies.
Workshops & Talks
More talks and workshops will be added in the coming days - stay tuned! Please see below for full speaker line-up.
Beware of Low Frequency Data, Ernest Chan, Managing Member, QTS Capital Management, LLC.
It is commonly believed that low frequency strategies require only low frequency data for backtesting. We will show that using low frequency data can lead to dangerously inflated backtest results even for low frequency strategies. Examples will be drawn from a closed end fund strategy, a long-short stock strategy, and a futures strategy.
Market Outlook 2015: How to Spot Bubbles, Avoid Market Crashes and Earn Big Returns, Mebane Faber, Co-founder and Chief Investment Officer of Cambria Investment Management
Attend Mebane's Meeting and Learn...
- Why a traditional 60/40 allocation will not get you to 8%
- How to value international stock markets
- How to avoid market bubbles and buy when blood is in the streets
- How to create a trading system to always invest in the cheapest markets
Investment bubbles and speculative manias have likely existed for as long as humans have been involved in markets. How can investors identify and avoid these bubbles' bursts and losses, and even profit from these crashes? Building on Graham and Dodd's work, Robert Schiller popularized CAPE, his version of the cyclically adjusted price-to-earnings ratio, in the late 1990s to give timely warnings of poor stock returns. Mebane Faber applies this valuation metric across more than 30 foreign markets and finds it both practical and useful. This presentation will describe a trading system to build global stock portfolios based on valuation, which can lead to significant outperformance by selecting markets based on relative and absolute valuation.
Case Studies in Creating Quant Models from Large Scale Unstructured Text, Sameena Shah, Director of Research, Thomson Reuters
SEC filings provide a window into the health of the company and are immensely important for investors. Historically, the only feasible way to read and interpret filings has been manually, where domain experts interpret filings and provide guidance to public. However, advances in big data technologies and Natural Language processing have enabled its automation. Sameena will discuss how her team created predictive models from text in filings and social media.
10 Ways Backtests Lie, Tucker Balch, Co-founder and CTO of Lucena Research
"I've never seen a bad backtest - Dimitris Melas, head of research at MSCI. Quantitative Analysts rely heavily on backtests as a means of validating their trading strategies. All too often, strategies look great in simulation but fail to live up to their promise in live trading. There are a number of reasons for these failures, some of which are beyond the control of a quant developer. But other failures are caused by common but insidious mistakes. In this talk I'll review a list of 10 pitfalls in strategy development and testing that can result in optimistic backtests. I'll also present methods for detecting and avoiding them. This talk will be of interest to quant developers and also non-quants who are interested to know what to look out for when presented with remarkably successful backtests.
Staying Ahead of the Game, Sarah Biller, Chief Operating Officer for Innovation at State Street Global Exchange
Across the past decade, trading volumes have grown exponentially. Technology has advanced at light speed. Sources of investable information have exploded. Enabled to invest faster with data and insights from alternative sources that lead many to question the relevance of fundamental data, equity investors today differ from those of the past. With this massive, permanent change to the investment environment comes opportunity. Sarah will discuss new sources of data and investment analytics that help quant investors make sense of the change.
The Machine Learning Approach, Michael Kearns, Professor of Computer and Information Science, UPenn
Traditional financial markets have undergone rapid technological change due to increased automation and the introduction of new exchanges and mechanisms. Such changes have brought with them challenging new problems in algorithmic trading, many of which invite a machine learning approach. In this talk I will examine several algorithmic trading problems, focusing on their novel ML aspects, including limiting market impact, dealing with censored data, and incorporating risk considerations.
The Genesis of An Order Type, Dan Aisen, Co-founder and Quantitative Developer at IEX
Abstract coming soon.
The Mobile Revolution and the Future of Modern Data Collection, Joe Reisinger, Co-founder and CTO of Premise
Orchestrating the collection and refinement of large-scale geospatial, economic and human development data -- data which form critical inputs for businesses, investors, policy-makers, regulators and strategists looking to get a timely and accurate read for what's happening right now on the ground -- is slow, difficult, and expensive. In many countries, such data pipelines don't exist yet at all. The proliferation of internet-enabled smartphones, with users spanning from globe from Mississippi to Mozambique, is rapidly changing our capabilities in this sector -- and Premise is upending the model by blending modern technology with human intelligence to map reality on the ground faster and more precisely than ever before. In this talk, Premise CTO Joe Reisinger will talk about the evolution of modern data collection on a global scale, why the new frontier of mobile technology is the conduit for the future of business and economics, and the role of alternative economic data as it relates to collection of official government statistics.
Moving Data Science from Pain to Unicorns on Rainbows, Brian Granger, a Leader of the IPython project, co-founder of Project Jupyter
Data scientists experience various pain points when working with code and data. The current generation of software tools inhibits collaboration, confuses users, burdens users with high cognitive loads, exhibits poor visual and interaction design, is overly proprietary and expensive, prevents reproducibility and limits the creation of coherent data-driven narratives. Project Jupyter, (formerly IPython) a set of open-source software projects for interactive and exploratory computing, is attempting to address the above pain points. This talk will focus on user interfaces and visualizations, the collaboration and sharing of computational narratives and the deployment of our software within companies, research groups and the open internet. The reduction of pain will be illustrated by giving numerous examples of how different organizations are leveraging the Jupyter Notebook. However, I will be honest about the vast amount of work we have left to do before delivering on our promise of Unicorns on Rainbows.
Probabilistic Programming in Quantitative Finance, Thomas Wiecki, Data Lead Scientist at Quantopian
There exist a large number of metrics to evaluate the performance-risk trade-off of a portfolio. Although those metrics have proven to be useful tools in practice, most of them require a large amount of data and implicitly assume returns to be normally distributed. Bayesian modeling is a statistical framework that allows great flexibility in modeling financial returns as well as risk metrics. In addition, uncertainty of these metrics can be directly quantified in terms of the posterior distribution.
In this talk, Thomas will briefly provide an overview of Bayesian statistics and how Probabilistic Programming frameworks like PyMC can be used to build and estimate complex statistical models. He will then show how several common financial risk metrics like the Sharpe ratio can be expressed as a probabilistic program. Using real-world data from anonymized algorithms running on Quantopian, he will demonstrate how the normality assumption can strongly bias the Sharpe ratio and how heavy-tailed distributions can remedy this problem.
Leveraging Quandl, Tammer Kamel, co-founder and CEO of Quandl.com
This will be a demonstration-based working session on how to leverage financial data via Quandl from various tools including Quantopian, R and Python. The talk will cover basic and advanced data access methods and also present an overview of the free and commercial data available on Quandl.com.
Turkey or Trader? Jeremiah Lowen, Director of Risk Management at Kokino LLC
Backtests can be treacherous. Though most quants quickly acknowledge that past performance is not indicative of future returns, many still evaluate algorithms solely through backtests. In this talk, we will discuss common pitfalls of interpreting and extrapolating hypothetical results. We adopt a skeptic's view of quantitative investing and examine the various biases and errors that quants can introduce even as they follow best practices. Ultimately, we will try to understand the degree to which we can gain confidence that a backtest is representative of an algorithm's quality.
Finding Alpha from Stock Buyback Announcements in the Quantopian Research Platform, Anju Marempudi is the founder and CEO of IntelliBusiness and creator of EventVestor and Seong Lee, Client Engineer at Quantopian
Stock buybacks are at record levels and several studies have established windows of alpha opportunity around stock buyback announcements. In this talk EventVestor founder Anju Marempudi and Quantopian client engineer Seong Lee will discuss buyback trends, analyzing share buybacks data for insights, conducting an event study to measure excess returns around buyback announcements, and finally building a trading algorithm with back-testing using the Quantopian Research Platform.
Democratized Investing, Akhil Lodha, Co-founder of Sliced Investing, and Mesh Lakhani, Founder of FutureInvestor.co
In an ideal world an investor has access to a range of investment opportunities that allow her to create a Balanced portfolio based on her risk/return objectives. Unfortunately we don't live an in ideal world and a lot of the investment opportunities have only been available to the Institutional Investor. That trend has started to change as technology and innovation by startups like AngelList, Wealthfront & Sliced Investing among others are lowering the barrier to access and allowing more individuals to create a balanced portfolio that meets their investment objectives. In this talk we'll focus on the need for a balanced portfolio, the investing tools for the 'new-age' investor and the future of individual investing.
Using Domain Expertise to Improve Text Analysis, Evan Schnidman, Founder and CEO of Prattle Analytics
It is widely acknowledged that text analysis offers a view into a massive world of unstructured data. This data offers a goldmine of tradable information ranging from corporate regulatory filings to central bank communications. But, like other areas of big data, this material is virtually useless without narrowing the focus. This talk will examine the ways in which deep domain expertise can help refine text analysis data into a powerful investing tool.
Speakers
Lisa Borland
Lisa is Head of Research and Co-Portfolio Manager at T2AM, responsible for research and manager due diligence. Prior to joining T2AM, Dr. Borland was Director of Derivatives Research at Evnine & Associates, Inc., a San Francisco-based quantitative hedge fund, where she developed equity and options trading strategies. Dr. Borland is the author of numerous papers within the fields of quantitative finance and econophysics, focusing largely on developing realistic models of volatility and correlations. She completed her PhD in Theoretical Physics at the University of Stuttgart, Germany. She is currently also a Lecturer at Stanford University in the Department of Management Sciences and Engineering teaching Big Financial Data and Algorithmic Trading.
Ernie Chan
Ernie is the Managing Member of QTS Capital Management, LLC., a commodity pool operator and trading advisor. He is the author of "Quantitative Trading: How to Build Your Own Algorithmic Trading Business" and "Algorithmic Trading: Winning Strategies and Their Rationale".
Mebane Faber
Mebane is a co-founder and the Chief Investment Officer of Cambria Investment Management. He has authored numerous white papers and three books: Shareholder Yield, The Ivy Portfolio, and Global Value.
Sameena Shah
Sameena is the Director of Research, NY for Thomson Reuters. She led the development of several large scale unstructured text based quant models for equities and commodities.
Tucker Balch
Tucker, Ph.D. is a former F-15 pilot, associate professor at Georgia Tech, and co-founder and CTO of Lucena Research, an investment software startup. His research focuses the challenges of applying Machine Learning to Finance.
Sarah Biller
Sarah, Sarah is the Chief Operating Officer for Innovation at State Street Global Exchange. She is responsible for identifying and commercializing new opportunities in data and analytics that will help the firm's clients make better, more forward-looking decisions and avoid downside risk.
Matthew Granade
Matthew, former Head of Research at Bridgewater Associates, is an investor, advisor, board member and creator of start-ups in areas where he has strategic and operational experience, including data analysis, finance, financial tech, and media. He is the co-founder of Domino, an early-stage company that provides a modeling platform for data scientists. He is currently involved with about a dozen companies, including Quantopian, Applied Academics, Premise, Opera Solutions, and Upworthy. In 2012, Matthew finished six years at Bridgewater Associates, one of the world's largest and best-performing hedge funds, where he was Co-Head of Research.
Michael Kearns
Michael is founder of UPenn's
Networked and Social Systems Engineering (NETS) program and director of Penn's Warren
Center for Network and Data Sciences. His research interests include topics in machine
learning, algorithmic game theory, social networks, and computational finance.
John "Fawce" Fawcett
Fawce is founder and CEO of Quantopian. Previous to that, he was a founder and CTO for Tamale Software, Inc. which was sold to Advent Software, Inc. in 2008. Fawce graduated Cum Laude from Harvard College with a degree in Engineering Sciences - Mechanics & Materials.
Joe Reisinger
Joe is co-founder and CTO of Premise. He holds a PhD in Computer Science from UT Austin and spent his academic career building natural language understanding systems at Google Research and IBM T.J. Watson. Prior to co-founding Premise, he was Chief Scientist at Metamarkets.
Dan Aisen
Dan is a co-founder and quantitative developer at IEX. He is responsible for building and evolving core functionality for the IEX trading venue, namely its matching engine, smart order router and its newest order type: Discretionary Peg. Forbes recognized Dan as one of their 2015 "30 Under 30".
Yoshiki Obayashi
Yoshiki, Managing Director at Applied Academics, oversees research collaborations with finance academics and commercialization of resulting intellectual property, such as systematic trading strategies, with institutions in the finance industry.
Karen Rubin
Karen, director of product at Quantopian, is focused on a new IPython Research Platform that will allow quants to access curated financial data in an interactive research environment.
Leigh Drogen
Leigh is the founder and CEO of Estimize. Prior to founding Estimize, Leigh ran Surfview Capital, a New York based quantitative investment management firm trading medium frequency momentum strategies.
Jeremiah Lowin
Jeremiah is the Director of Risk Management at Kokino LLC. Prior to that, he was the founder and Chief Scientist of the Lowin Data Company. He received his AM in statistics and AB in economics from Harvard University and is a CFA charter holder.
Jean Bredeche
Jean is co-founder and CTO of Quantopian. Previously, he worked as a software engineer at Kapost and prior to that, Hubspot. He started his career at Tamale Software, Inc. He graduated from Dartmouth with a degree in computer science.
Jessica Stauth
Jess is Quantopian's vice president of quant strategy. She holds a PhD from UC Berkeley in Biophysics and has worked as an equity quant analyst at the StarMine Corporation and as a Director of Quant Product Strategy for Thomson Reuters.
Tammer Kamel
Tammer Kamel is co-founder and CEO of Quandl.com the world's biggest open platform for financial and economic data. Prior to that, Tammer was one of the world's leading authorities on quantitative investment strategies and risk management.
Brian Risk
Brian is head of marketing at Quandl and founder of Geneffects LLC. His work focuses on helping the world discover the joy of Quandl's easy API and diverse data offerings.
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Akhil Lodha
Akhil co-founded Sliced Investing with the aim of adding accessibility, transparency and organization to the private fund investment process. He leads the Analytics and Technology team at Sliced Investing.
Brian Granger
Brian is an Associate Professor of Physics and Data Science at Cal Poly State University in San Luis Obispo, CA. He is a leader of the IPython project, co-founder of Project Jupyter and is an active contributor to a number of other open source projects focused on data science in Python.
Anju Marempudi
Anju is the founder and CEO of IntelliBusiness and creator of EventVestor - a powerful financial data platform providing event-driven insights for quant traders, institutional investors, and investor relations. He holds an MBA from The Wharton School of the University of Pennsylvania.
Evan A. Schnidman
Evan is the Founder and CEO of Prattle Analytics, a financial data company. Evan holds a PhD from Harvard University and is widely published in finance and political economy.
Thomas Wiecki
Thomas Wiecki received his PhD from Brown University where he developed Bayesian models to help understand brain disorders. He currently works as the data science lead at Quantopian. Among other projects, he is involved in the development of PyMC - a probabilistic programming framework written in Python.
Justin Lent
Justin is Quantopian's director of fund development. Previously, Justin spent time working at Blackrock/BGI, and at Palantir Technologies where he worked onsite with several large financial institutions in a consulting role.
Mesh Lakhani
Mesh recently launched FutureInvestor.co to teach retail investors how to properly invest using online platforms. He speaks frequently about the future of asset allocation using online platforms and has been an instructor at General Assembly NYC and DC.
Seong Lee
Seong Lee is a client engineer for Quantopian where he's wrangled with everything from machine learning algorithms to helping users solve their most unsolvable errors.
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