The discretionary buy side world is currently undergoing a massive shift away from simply leveraging beta towards having to generate consistent idiosyncratic alpha. In order to achieve results in this new reality, the smartest firms are attempting to put in place a process to become more quantitative in their decision making and use new unique alpha generating data sets.
At the same time, an arms race is taking place within the systematic world where firms are searching for uncorrelated sources of alpha in new data sets, and employing new quantitative analysis techniques to find it.
L2Q is a conference designed to explore both of these important trends. The main track is focused on teaching discretionary PMs, analysts, and traders the basics of quantitative research so that they can collaborate with the quants on their desk. Quants who work along side with them will also benefit from the main track as we explore the best ways to run the difficult process of melding fundamental analysis and quantitative decision making. The second track features topics in advanced quantitative analysis and use cases for new unique data sets within both fully systematic models and discretionary books.
Segments are taught by preeminent buy side, sell side, and unique data experts with vast quantitative and discretionary investment experience.
Prior to starting Dristi Capital Partners, Parag was CIO of Blackstone Senfina Advisors. He spent 14 years managing an equity long/short portfolio as an industrials & transportation coverage Sector Head at Ziff Brothers Investments. Parag also spent 2 years as an IB analyst and 1 year as a merchant banking analyst at Lehman Brothers.
Michael is building a new data science team that will leverage large, unstructured, novel data to evaluate the health of business. The primary, initial focus for the data science team is international equities. He holds a PhD in Neuroscience from University College London.
Max runs the online lecture series at Quantopian and is responsible for workshop curriculums and educational content. In addition to having experimented with algorithmic trading of cryptocurrencies and Bayesian estimation of covariance matrices, Max has published work in theoretical mathematics.
Jessica heads up Quantopian’s Portfolio Implementation team. Previously she worked as an equity quant analyst at StarMine and as a Director of Quant Product Strategy for Thomson Reuters. Dr. Stauth holds a PhD from UC Berkeley in Biophysics.
Neal Parikh is Co-Founder of SevenFifty, a NYC-based technology company. He received a PhD in computer science from Stanford University, previously worked at Goldman Sachs, and has been Visiting Lecturer in machine learning at Cornell Financial Engineering Manhattan.
Dan Furstenberg is the Global Head of Hedge Fund Distribution and Head of Data Strategy at Jefferies. Dan began his career in investment banking, focusing on leveraged finance at Merrill Lynch before joining the tech banking team at Credit Suisse First Boston.
Javed is responsible for alpha signal, Big Data, ESG, and small-cap research. Previously he was the US Head of Quantitative Strategy at Deutsche Bank and held roles at Macquarie Capital, CIBC World Markets, and IBM Consulting.
In the Applied Research group, Melissa generates insights into risk trends by analyzing data on market and portfolio risk which can be found in Axioma Insight: Quarterly Risk Review. Previously Brown held roles at Wintrust Capital Management and Goldman Sachs Asset Management.
Prior to founding Estimize, Leigh ran Surfview Capital, a New York based quantitative investment management firm trading medium frequency momentum strategies.
Previously, Omer served as SVP, Research at Two Sigma Investments where he built Two Sigma’s global equity research analyst survey platform (“TAP”) and managed their quantitative risk arbitrage system.
Christine is a corporate earnings expert, previously holding director-level roles at both Thomson Reuters and S&P Capital IQ, and widely quoted by financial media.
Lauren is responsible for analyzing market research and determining the direction of the FactSet content, cloud and technology strategy. Since joining FactSet in 2006, Lauren worked as a Consultant until 2008, and then as an Economic Specialist until 2013.
Prior to founding Adaptive Management, Brad spent over 12 years as an equity investor, most recently working as a PM at Tiger Management. He was also a co-founder of a data analytics company which he formed after receiving his BS from MIT.
In his role, Alan builds products for quantitative and fundamental investors using 7Park Data’s Leading Performance Indicators. Previously he was a Senior Research & Quant Analyst at Neuberger Berman; he also created quantitative models and analytical systems at GLG Partners / MAN Group, and was the team leader for front office development at Beauchamp Financial Technology.
Prior to leading TickerTags, Mark was a Senior Analyst specializing in data-driven semiconductor equity research at firms such as Pacific Crest Securities, Thomas Weisel Partners, Auriga, and Avian Securities. In addition to sell-side research, Mark has held several finance positions at Intel, Wind River Systems, and Omega Morgan.
Christine Short , SVP of Media and PR at Estimize
Leigh Drogen and Parag Pande discuss Parag’s evolution from getting caught offsides on momentum in 2016 to being a full believer in using data and quantitative processes to do stock selection, risk management, and more generally design an investment process.
Adaptive Management, 7Park Data
Leigh Drogen of Estimize shares the latest on how machine learning and other non-linear methods are rewriting everything we thought we knew about how to do quantitative research and produce alpha factors.
Max Margenot from Quantopian teaches discretionary managers what they need to know in the quantitative research process and provide a high-level overview of linear regression modeling, in and out of sample analysis, python, and R.
Jess Stauth of Quantopian talks about data modeling robustness and the dangers of overfitting and other common pitfalls that stop a good backtest from being a good live trading strategy.
Omer Cedar from OmegaPoint and Melissa Brown from Axioma share with attendees what factors they might be exposed to, how to deal with exposure, and how to include this in their workflow.
Neal Parikh will provide an introduction to key ideas in machine learning and discuss how to think about it relative to other quantitative tools.
Dan Furstenberg of Jefferies chats about the sell side’s role in the transition to using more data and being more quantitative on the discretionary side.
Javed Jussa of Wolfe Research talks about predicting takeover targets and their applications.
Michael Recce, Chief Data Scientist at Neuberger Berman talks about using machine learning and AI to provide a real-time look at how companies are doing.
520 Madison Avenue, New York, NY 10022
November 16, 2018
9:00AM - 4:00PM
Estimize is an open financial estimates platform which facilitates the crowdsourcing of fundamental estimates from professionals (buy-side, independent, and sell-side analysts) as well as non-professionals (private investors, students, academics.) By sourcing estimates from a diverse community of individuals, Estimize provides both a more representative consensus and one that is more accurate than the sell-side 74% of the time. Currently, nearly 45,000 analysts contribute to Estimize, resulting in coverage on over 2,000 stocks each quarter.