{"id":8077,"date":"2022-06-23T14:00:29","date_gmt":"2022-06-23T18:00:29","guid":{"rendered":"https:\/\/www.math.columbia.edu\/mafn\/dev\/?page_id=8077"},"modified":"2023-06-15T10:06:15","modified_gmt":"2023-06-15T14:06:15","slug":"practitioners-seminar-2022","status":"publish","type":"page","link":"https:\/\/www.math.columbia.edu\/mafn\/practitioners-seminar-2022\/","title":{"rendered":"Practitioners\u2019 Seminar 2022"},"content":{"rendered":"

The seminar took place in the Spring of 2022, Tuesdays and Thursdays 7:40 pm \u2014 8:55 pm.<\/p>\n

Organizer: Lars Tyge Nielsen<\/p>\n

<\/a><\/p>\n

Past Presentations<\/h3>\n
\n
Tuesday, January 18, 2022<\/dt>\n
<\/dd>\n
Speaker<\/strong>: Alexander Fleiss, Rebellion Research<\/dd>\n
<\/dt>\n
RebellionResearch.com\u2019s CEO Alexander Fleiss has spoken about Artificial Intelligence Investing in the Wall Street Journal, New York Times, Fox News, BusinessWeek, Bloomberg News, MIT Technology Review, Yomiuri Shimbun, Wired, Geo Magazine, The Economist and Institutional Investor. Chapter 24 of Wall Street Journal Reporter Scott Patterson\u2019s book Dark Pools is on Mr. Fleiss. Mr. Fleiss instructs research at Cornell Financial Engineering, Rutgers MAQF & Fordham Gabelli School of Business and has guest taught at Amherst College for 15 years and Yale for 7 years, instructing seminars at both Yale Law & Yale SOM.<\/p>\n

Mr. Fleiss was a programmer at Neuberger Berman, and hedge funds Sloate Weisman Murray, KMF Partners & Bramwell Funds and was a research instructor at Amherst College where he published the college\u2019s first paper on applying artificial intelligence to the stock market.<\/p>\n<\/dd>\n

\n
Title<\/strong>: Deep Reinforcement Learning & Nowcasting\u2019s Fatal Flaw<\/div>\n<\/dd>\n<\/dl>\n
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Thursday, January 20, 2022<\/dt>\n
<\/dd>\n
\n
Speaker<\/strong>: Jeff Waldron and Samvel Gevorkyan, Freepoint Commodities<\/div>\n<\/dd>\n<\/dl>\n
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\n
Jeff Waldron has been involved in trading and markets businesses since 1990. After graduating from Rensselaer Polytechnic Institute, he joined UBS in Fixed Income Derivatives, where he rose to oversee USD Options and Exotics trading. In 2006, he moved to Commodities, to lead Derivative trading and Structured products. This led to a similar role at RBS Sempra, and a stint at JPMorgan leading Crude Oil and Products derivative trading. In 2015, after a sabbatical revived an interest in programming, he joined Freepoint Commodities, to develop an algorithmic trading effort, which he and Samvel have collaborated together on for the past two years.<\/div>\n<\/dd>\n
<\/dt>\n
\n
Title<\/strong>: ML applications in Trading<\/div>\n<\/dd>\n
<\/dt>\n
\n
Abstract<\/strong>
\nWe discuss in a question and answer format end-to-end systematic trading within commodities markets and talk more specifically about training reinforcement learning agents to trade futures contracts. We touch upon such challenges as the impact of latency, effects of overfitting on quality of produced alphas, data management, and software\/hardware requirements. We also talk more generally about best practices in transitioning from development to live trading and the numerous pitfalls associated with that process.<\/div>\n<\/dd>\n<\/dl>\n
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Tuesday, January 25, 2022<\/dt>\n
<\/dd>\n
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Speaker<\/strong>: Boris Lerner, Morgan Stanley<\/div>\n<\/dd>\n<\/dl>\n
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Boris Lerner is the Global Head of Quantitative Equity Research at Morgan Stanley, based in New York. Boris joined Morgan Stanley in 2003 and has worked on a broad range of projects over the years, including derivative research, quantitative modeling, data analytics, and structuring. In his current role, Boris is focused on the quantitative analysis of the equity markets, developing alpha models, evaluating alternative data sources, and applying quantitative techniques to traditional fundamental research. Prior to joining the Morgan Stanley Research department, Boris co-headed the Morgan Stanley Quantitative Investment Strategies (QIS) structuring team in North America, where he developed cross-asset, rules-based hedging and risk premia investment strategies for use in diversified institutional portfolios. Boris holds a Master\u2019s degree in Financial Mathematics from Columbia University, and a Bachelor\u2019s degree in Finance and Information Technology from the New York University Stern School of Business.<\/div>\n<\/dd>\n
<\/dt>\n
Title<\/strong>: The Rise of the Retail Trader \u2013 Estimating Retail Activity using Public Trading Data<\/dd>\n
<\/dt>\n
Abstract<\/strong><\/p>\n
The \u201cGame Stop frenzy\u201d and the broader \u201cmeme stock\u201d phenomenon that has developed over the last year have prompted many questions from clients on how to effectively estimate retail activity. To address these questions, we have developed a model using publicly available data. Our model seeks to estimate both the level and direction of retail trading activity (i.e., how much of the daily trading volume in each stock is attributable to retail traders, and whether they were net buyers or sellers of that volume).
\nWe find that insights into retail activity provided by our model can be used to generate alpha. We see a positive relationship between our estimate of the retail order imbalance and the subsequent stock returns. Stocks with a high buy imbalance tend to outperform stocks with a high sell imbalance over the subsequent one month. The signal can be further amplified by limiting the universe of stocks to those with high retail activity.<\/div>\n<\/dd>\n<\/dl>\n
\n
Thursday, January 27, 2022<\/dt>\n
<\/dd>\n
Speaker<\/strong>: Laura Simonsen Leal, Princeton University<\/dd>\n
<\/dt>\n
Laura Leal is a final-year PhD student in the Operations Research and Financial Engineering department at Princeton University. Her research interests are centered in high-frequency finance, using machine learning, deep neural networks, optimization, statistical and econometric methods to study high-frequency trading data. The main topics she has worked on include optimal execution, market making, identification of institutional activity, and tail risk.<\/dd>\n
<\/dt>\n
\n
Title<\/strong>: Optimal Execution with Quadratic Variation Inventories<\/div>\n<\/dd>\n
<\/dt>\n
\n
Abstract<\/strong>
\nWe describe and implement statistical tests arguing for the presence of a Brownian component in the inventories and wealth processes of individual traders. Using intra-day data from the Toronto Stock Exchange, we provide empirical evidence of this claim. Both for regularly spaced time intervals, as well as for asynchronously observed data, the tests reveal with high significance the presence of a non-zero Brownian motion component. Furthermore, we extend the theoretical analysis of an existing optimal execution model to accommodate the presence of Ito inventory processes, and we compare empirically the optimal behavior of traders in such fitted models, to the actual behavior read off the data.<\/div>\n<\/dd>\n<\/dl>\n
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Tuesday February 1, 2022<\/dt>\n
<\/dd>\n
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Speaker<\/strong>: Irene Aldridge, AbleMarkets, Cornell University and Cambridge University<\/div>\n<\/dd>\n<\/dl>\n
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Irene Aldridge is an internationally-recognized quantitative and Big Data Finance researcher, President and Managing Director, Research, of AbleMarkets, ETFPick and AbleBlox, and Adjunct Professor at Cornell University. Prior, 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 a software engineer in financial services. Irene is a co-author of \u201cBig Data Science in Finance\u201d (with Marco Avellaneda, Wiley 2021), \u201cReal-Time Risk: What Investors Should Know About Fintech, High-Frequency Trading, Flash Crashes\u201d (co-authored with Steve Krawciw, Wiley, 2017), and the author of \u201cHigh-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems\u201d (2nd edition, translated into Chinese, Wiley 2013), among other work.<\/div>\n<\/dd>\n
<\/dt>\n
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Title<\/strong>: SEC EDGAR Filings Over the Years: Where Is Alpha Today? (Joint work with Bojun Li, Cornell University)<\/div>\n<\/dd>\n
<\/dt>\n
\n
Abstract<\/strong>
\nSEC Edgar data was launched in 1984 as a convenient way to widely distribute information on publicly-traded companies. With the proliferation of machine learning and data science techniques, mining EDGAR has never been easier. This has indeed translated into market efficiency. In this research, we explore various alpha components of EDGAR over the years and show that, even though the alpha from EDGAR has become much harder to mine, the inefficiencies still exist and the filings still carry useful information for market practitioners.<\/div>\n<\/dd>\n<\/dl>\n
\n
Thursday February 3, 2022<\/dt>\n
<\/dd>\n
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Speaker<\/strong>: Samvel P. Gevorkyan, Freepoint Commodities<\/div>\n<\/dd>\n<\/dl>\n
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Samvel Gevorkyan is a Quantitative Researcher at Freepoint Commodities. He manages a crude oil and natural gas portfolio with strategies based on probabilistic machine learning: from data processing to alpha factor engineering to testing and deployment. He holds a Master\u2019s Degree in Financial Mathematics from Columbia University (2018), B.S. in Mathematics\/Economics from UCLA (2016).<\/div>\n<\/dd>\n
<\/dt>\n
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Title<\/strong>: Risk Premia and ML Applications<\/div>\n<\/dd>\n
<\/dt>\n
\n
Abstract<\/strong>
\nWe discuss various well known and time-tested risk premia, their implementations, and the benefits of portfolio construction. We then move on to empirical risk premia \u2013 those crafted from nonlinear pattern recognition and explained by data rather than economic intuition. More specifically, we look at how machine learning techniques can assist beyond predicting expected asset returns<\/div>\n<\/dd>\n<\/dl>\n
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Tuesday February 8, 2022<\/dt>\n
<\/dd>\n
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Speaker<\/strong>: Fabio Mercurio<\/div>\n<\/dd>\n<\/dl>\n
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Fabio Mercurio is the global head of Quantitative Analytics at Bloomberg LP, New York. His team is responsible for the research on and implementation of cross-asset analytics for derivatives pricing, XVA valuations and credit and market risk. Fabio is also an adjunct professor at NYU. He has co-authored the book \u201cInterest rate models: theory and practice\u201d and published extensively in books and international journals, including 20 cutting-edge articles in Risk Magazine. Fabio is the recipient of the 2020 Risk quant of the year award.<\/div>\n<\/dd>\n
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<\/div>\n

 <\/dt>\n

\n
Title<\/strong>: Libor Transition: Looking Forward to Backward-Looking Rates<\/div>\n<\/dd>\n
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 <\/dt>\n

\n
Abstract<\/strong>
\nIn this talk, we define and model forward risk-free term rates, which appear in the payoff definition of derivatives and cash instruments, based on the new interest-rate benchmarks that will be replacing IBORs globally. We show that the classic interest-rate modeling framework can be naturally extended to describe the evolution of both the forward-looking (IBOR-like) and backward-looking (setting-in-arrears) term rates using the same stochastic process. We then introduce an extension of the LIBOR Market Model (LMM) to backward-looking rates. This extension, which we call generalized forward market model (FMM), completes the LMM by providing additional information about the rate dynamics between fixing\/payment times, and by implying dynamics of forward rates under the classic money-market measure. Our FMM formulation is based on the concept of extended zero-coupon bonds, which proves to be very convenient when dealing with backward-looking setting-in-arrears rates. Thanks to this, not only the bonds themselves, but also the forwards and swap rates, along with their associated forward measures, can be defined at all times, even those beyond their natural expiries.
\nFinally, we complete the FMM by embedding into a Markovian HJM to allow for the generation of any forward-looking or backward-looking rates, and hence for the pricing of any exotic payoff or derivatives portfolio.<\/div>\n<\/dd>\n<\/dl>\n
\n
Thursday February 10, 2022
\nThis presentation takes place in Math 207, <\/span>not on Zoom<\/strong><\/dt>\n
<\/dd>\n
\n
Speaker<\/strong>: David-Antoine Fournie, Bank of America<\/div>\n<\/dd>\n<\/dl>\n
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David Fournie is a Managing Director af Bank of America, where he is Head of Equity Structured Products for Americas, overseeing Exotic Derivatives and QIS desks. He had previously worked at Deutsche Bank, Morgan Stanley and Deauville Capital Management. He got his Ph.D. from this very Mathematics department in 2010 for \u2013 functional extension of Ito calculus<\/div>\n<\/dd>\n
<\/dt>\n
\n
Title<\/strong>: Grid pricing models<\/div>\n<\/dd>\n
<\/dt>\n
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Abstract<\/strong>
\nWill go through risk-neutral pricing on discrete grids and link with finite difference schemes for parabolic PDEs<\/div>\n<\/dd>\n<\/dl>\n
\n
Tuesday February 15, 2022<\/dt>\n
<\/dd>\n
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Speaker<\/strong>: Andrey Itkin, Bank of America and NYU Tandon School of Engineering<\/div>\n<\/dd>\n<\/dl>\n
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Dr. Andrey Itkin is an Adjunct Professor at NYU, Department of Risk and Financial Engineering, and Director, Senior Quantitative Research Associate at Bank of America. He received his PhD in physics of liquids, gases and plasma followed by degree of Doctor of Science in computational physics. During his academic carrier he published 4 books and multiple papers on chemical, theoretical and astro physics, and computational and mathematical finance. Andrey occupied various research and managerial positions in financial industry and also is a member of multiple professional associations in finance and physics. He is an EIC of Annual Review of Modern Quantitative Finance and on editorial board of several journals.<\/div>\n<\/dd>\n
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<\/div>\n

 <\/dt>\n

\n
Title<\/strong>: Multilayer heat equations: Application to finance (joint work with Alex Lipton and Dmitry Muravey)<\/div>\n<\/dd>\n
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 <\/dt>\n

\n
Abstract<\/strong>
\nIn this paper, we develop a Multilayer (ML) method for solving one-factor parabolic equations. Our approach provides a powerful alternative to the well-known finite difference and Monte Carlo methods. We discuss various advantages of this approach, which judiciously combines sei-analytical and numerical techniques and provides a fast and accurate way of finding solutions to the corresponding equations. To introduce the core of the method, we consider multilayer heat equations, known in physics for a relatively long time but never used when solving financial problems. Thus, we expand the analytic machinery of quantitative finance by augmenting it with the ML method. We demonstrate how one can solve various problems of mathematical finance by using our approach. Specifically, we develop efficient algorithms for pricing barrier options for time-dependent one-factor short-rate models, such as Black-Karasinski and Verhulst. Besides, we show how to solve the well-known Dupire equation quickly and accurately. Numerical examples confirm that our approach is considerably more efficient for solving the corresponding partial differential equations than the conventional finite difference method by being much faster and more accurate than the known alternatives.<\/div>\n<\/dd>\n<\/dl>\n
\n
Thursday February 17, 2022<\/dt>\n
<\/dd>\n
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Speaker<\/strong>: Michael Pykhtin, U.S. Federal Reserve Board<\/div>\n<\/dd>\n<\/dl>\n
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\n
Michael Pykhtin is a manager in the Policy Research and Analytics function at the U.S. Federal Reserve Board. Prior to joining the Board in 2009 as a senior economist, he had a successful nine-year career as a quantitative researcher at Bank of America and KeyCorp. Michael has edited \u201cMargin in Derivatives Trading\u201d (Risk Books 2018), \u201cCounterparty Risk Management\u201d (Risk Books, 2014) and \u201cCounterparty Credit Risk Modelling\u201d (Risk Books, 2005); he is also a contributing author to several recent edited collections. Michael has published extensively in the leading industry journals; he has been an Associate Editor of the Journal of Credit Risk since 2007. Michael is a two-time recipient of Risk Magazine\u2019s Quant of the Year award (for 2014 and 2018). Michael holds a Ph.D. degree in Physics from the University of Pennsylvania and an M.S. degree in Physics and Applied Mathematics from Moscow Institute of Physics and Technology.<\/div>\n<\/dd>\n
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 <\/dt>\n

\n
Title<\/strong>: CVA Risk and Basel III CVA Framework<\/div>\n<\/dd>\n
\n
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 <\/dt>\n

\n
Abstract<\/strong>
\nThis presentation consists of two parts. In the first part, we are going to review the concepts of CVA and CVA risk. The second part is devoted to regulatory capital requirements for CVA risk by the Basel Committee on Banking Supervision and includes a thorough overview of both the current CVA framework and the revised CVA framework (the latter has been developed recently as part of the fundament review of the trading book, FRTB). A special emphasis will be placed on model foundations underlying the regulatory capital requirements, thus connecting the regulatory treatment to economic risk.<\/div>\n<\/dd>\n<\/dl>\n
\n
Tuesday February 22, 2022<\/dt>\n
<\/dd>\n
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Speaker<\/strong>: Ali Nejadmalayeri, University of Wyoming<\/div>\n<\/dd>\n<\/dl>\n
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\n
Ali Nejadmalayeri, Ph.D., CFA (or as his students call him \u201cDr. N\u201d) holds the John A. Guthrie Chair in Banking and Financial Services at the College of Business of the University of Wyoming (UW). He is also a fellow at UW\u2019s Center of Blockchain and Financial Innovations. Between 2015 and 2018, he held the ONEOK Chair in Finance at Spears School of Business of Oklahoma State University (OSU). Concurrently, he was also the finance Ph.D. program director. Between 2012 and 2015, he held the Jay and Fayenelle Helm Professor in Business at OSU. Nejadmalayeri has published in major finance and economics journals, including the Review of Finance, the Journal of Business, the Journal of Banking and Finance, the Journal of Corporate Finance, the Journal of Real Estate Finance and Economics, and the Journal of the Academy of Marketing Science. His research generally deals with the interaction of corporate decisions and the capital (bond) markets. His recent work investigates the current and potential future structure of corporate bond ownership. His research has been recognized with numerous awards and accolades including the \u201c2016 OSU-Tulsa President\u2019s Outstanding Researcher of the Year\u201d award. He is an associate editor at Global Finance Journal, an editorial review board member at Multidisciplinary Business Review, and a special edition\u2019s editor at the Journal of Financial and Risk Management. Nejadmalayeri holds a Bachelor of Science degree in Electrical Engineering from the University of Tehran, an M.B.A. degree from Texas A&M University and a Ph.D. in Finance from the University of Arizona. Prior to joining academia, he worked as an electrical engineer in the Oil and Gas industry. He has consulted with multinationals (Barclays Global) and local governments (the City of Tulsa).<\/div>\n<\/dd>\n
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 <\/dt>\n

\n
Title<\/strong>: Taxation Channels and Municipal Bond Yield Spreads<\/div>\n<\/dd>\n
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 <\/dt>\n

\n
Abstract<\/strong>
\nI analytically model and empirically examine how through property, income, and sales tax channels, household income and housing wealth permeates onto municipal bonds spreads. Theoretically, higher wages should attenuate yield spreads, especially through income and sales tax channels. Consistent with analytical predictions, household income attenuates yield spreads through sales and income tax channels. In theory, rising housing prices only narrow spreads if sales taxes due to housing wealth effect exceed the after-tax cost of mortgaging the housing wealth. Predictably, housing wealth attenuates yield spreads through property tax channel. Unpredictably though, household income also narrows spread through a pronounced property tax channel. Also unpredictably, housing wealth narrows spreads through sales and income tax channels. Counter theoretically, both household income and housing wealth extenuate yield spreads. Outside interactions with wages and housing wealth, none of local\/state level taxes have standalone impacts on municipal yield spreads.<\/div>\n<\/dd>\n<\/dl>\n
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Thursday February 24, 2022<\/dt>\n
<\/dd>\n
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Speaker<\/strong>: Igor Halperin<\/div>\n<\/dd>\n<\/dl>\n
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Igor Halperin is an AI Research Associate at Fidelity Investments. His research focuses on using methods of reinforcement learning, information theory, and physics for financial problems such as portfolio optimization, dynamic risk management, and inference of sequential decision-making processes of financial agents. Igor has an extensive industrial and academic experience in statistical and financial modeling, in particular in the areas of option pricing, credit portfolio risk modeling, and portfolio optimization. Prior to joining Fidelity, Igor worked as a Research Professor of Financial Machine Learning at NYU Tandon School of Engineering. Before that, Igor was an Executive Director of Quantitative Research at JPMorgan, and a quantitative researcher at Bloomberg LP. Igor has published numerous articles in finance and physics journals, and is a frequent speaker at financial conferences. He has co-authored the books \u201cMachine Learning in Finance: From Theory to Practice\u201d (Springer 2020) and \u201cCredit Risk Frontiers\u201d (Bloomberg LP, 2012). Igor has a Ph.D. in theoretical high energy physics from Tel Aviv University, and a M.Sc. in nuclear physics from St. Petersburg State Technical University. In 2021, Igor was named the Buy-Side Quant of the Year by RISK magazine.<\/div>\n<\/dd>\n
<\/dt>\n
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Title<\/strong>: Towards Non-Perturbative Finance: How Quantitative Finance can benefit from insights from Reinforcement Learning and Physics<\/div>\n<\/dd>\n
<\/dt>\n
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Abstract<\/strong>
\nIn this talk, I will discuss non-linear models of market dynamics originating from the presence of market flows and frictions. Viewing these dynamics as decision making by external investors along the lines of the reinforcement learning methods, and using ideas from quantum mechanics, one ends up with a tractable non-linear model for the dynamics of the market or a single stock which is able to capture both benign and crises regimes (or alternatively regimes of small and large fluctuations). The resulting model is able to accurately match the market prices of options using a single volatility parameter, which stands in stark contrast to most of the models in the literature that usually consider more complicated models of noise such as local, stochastic or rough volatility models.<\/div>\n<\/dd>\n<\/dl>\n
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Tuesday March 1, 2022<\/dt>\n
<\/dd>\n
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Speaker<\/strong>: Alec Schmidt, NYU School of Engineering<\/div>\n<\/dd>\n<\/dl>\n
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Dr Anatoly (Alec) Schmidt is Adjunct Professor at the Financial Engineering Departments of the NYU Tandon School and Stevens Institute of Technology. He was also Visiting Professor at Nanyang Technological University and Moscow Financial Academy. Alec holds a PhD in Physics and has worked in the financial industry for more than 20 years, most recently as Lead Research Scientist at a market data analytics company, Kensho Technologies. Alec published three books, \u201cQuantitative Finance for Physicists\u201d (Elsevier, 2004), \u201cFinancial Markets and Trading: Introduction to Market Microstructure and Trading Strategies\u201d (Wiley, 2011), and \u201cModern Equity Investing Strategies\u201d (World Scientific, 2021).<\/div>\n<\/dd>\n
<\/dt>\n
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Title<\/strong>: Expanding the mean-variance paradigm: portfolio diversification and optimal ESG portfolios<\/div>\n<\/dd>\n
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Abstract<\/strong><\/div>\n<\/dd>\n<\/dl>\n
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Thursday March 3, 2022<\/dt>\n
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Speaker<\/strong>: Bryan Jianfeng Liang<\/div>\n<\/dd>\n<\/dl>\n
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Bryan Liang is a senior quant researcher at Bloomberg L.P. and adjunct assistant professor at Columbia University. He joined the Bloomberg quant research team in 2011 and has been working extensively on various aspects of derivatives modelling, including pricing, hedging, risk management, structuring, market making, trading strategies, parallel computing and applications of machine learning techniques in derivatives. He is also an adjunct professor at the Courant Institute NYU. Prior to joining Bloomberg, He worked for derivatives analysis group at Goldman Sachs, as an interest rate derivatives quant. Bryan received his Ph.D. in mathematics from University of Michigan and was a faculty member at Northwestern University and UC Davis before he moved to finance.<\/div>\n<\/dd>\n
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 <\/dt>\n

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Title<\/strong>: Dual-Primal Simulation Algorithm for Pricing American Options<\/div>\n<\/dd>\n
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\n
Abstract<\/strong>
\nPricing American options is one of the most classical problems of quantitative finance. In this talk, we present a novel simulation-based algorithm to this classical problem using deep learning techniques. In the standard primal-dual approach, one starts with finding the optimal strategy (primal) from cross-sectional regression and then extracting the martingale component (dual) from the corresponding value function. Instead, we propose a dual-primal approach. We start by solving the dual problem directly where replication martingales are parameterized by a neural network and optimized through the minimization of residual risk. The same neural network is applied to the temporal data at each time step so that the evaluation and training of the network can be done in a highly efficient time-distributed fashion. Then an exercise strategy can be constructed from the estimated optimal martingale by solving a classification problem. Both problems can be solved efficiently without relying on recursive schemes commonly used in such problems. In addition, the hedges obtained from the dual problem significantly reduce the variances of both lower and upper bound estimates and produce accurate confidence interval for the true price.<\/div>\n<\/dd>\n<\/dl>\n
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Tuesday March 8, 2022<\/dt>\n
<\/dd>\n
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Speaker<\/strong>: Edith Mandel, Greenwich Street Advisors, LLC<\/div>\n<\/dd>\n<\/dl>\n
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\n
As a principal at Greenwich Street Advisors, LLC, Edith provides her clients with Fixed Income quant trading know-how and signal research tools, advises them on applications of machine learning and data science in finance, and develops innovative analytics infrastructure solutions.
\nEdith is CEO and Co-Founder of INFIO (https:\/\/www.inf.io) and an adjunct professor at NYU Tandon School of Engineering.
\nPrior to starting her own advisory firm in 2015, Edith Mandel was the head of Fixed Income Mid-Frequency Trading at KCG (formerly GETCO). While there, she spearheaded a development of a new quantitative and systematic business within the Fixed Income group.
\nEdith started her career at Goldman Sachs in 1996, where she held a number of positions in the Fixed Income division. As a Managing Director, Edith ran a team of quantitative desk strategists responsible for US Rates trading.
\nPrior to joining KCG in 2012, Edith Mandel worked at Citadel for 3 years as a Managing Director, Head of Fixed Income Quantitative Research. There she was instrumental to a significant revamp and expansion of the Fixed-Income Asset Management business.<\/div>\n<\/dd>\n
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 <\/dt>\n

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Title<\/strong>: Machine Learning in Fixed Income: Applications in Quantitative Trading & Execution Optimization<\/div>\n<\/dd>\n
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Abstract<\/strong><\/div>\n<\/dd>\n<\/dl>\n
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Thursday March 10, 2022
\nThis presentation takes place in Math 207, <\/span>not on Zoom<\/strong><\/dt>\n
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Speaker<\/strong>: Matthew S. Rothman, Millennium Investment and MIT Sloan School of Management<\/div>\n<\/dd>\n<\/dl>\n
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Matthew Rothman is a member of the senior management team of the Quantitative Strategies business unit at Millennium Management functioning as the deputy head of the team and the Head of Quantitative Services. He is on the finance faculty at the MIT Sloan School of Management.
\nMatthew has held a variety of positions both in academia and industry. Prior to joining Millennium, Matthew was a Managing Director at Goldman Sachs where he was the global head of Quantitative Strategies and Solutions for the Securities Division. Immediately prior to that position, Matthew was the global head of Quantitative Equity Research at Credit Suisse. Previously, Matthew was the director of Global Quantitative Macro research at Acadian Asset Management in Boston, a purely quantitative asset management firm with approximately $100 billion in assets under management. He was also the global head of quantitative research at Lehman Brothers and then Barclays Capital, post the Lehman bankruptcy. He has also held senior research positions at Sanford C. Bernstein and Goldman Sachs Asset Management.
\nMatthew has served on the faculty in the Economics Department at Brown University and in the Finance Department at the Samuel Curtis Johnson Graduate School of Management at Cornell University.
\nMatthew is involved with a number of philanthropic institutions, including having served for over a decade on the Board of Directors of the Innocence Project, a non-for-profit committed to the exoneration of the wrongfully convicted through DNA evidence. He also is deeply committed to funding and working with photographers documenting social conflict and civil unrest both domestically and globally.
\nMatthew earned a BA in philosophy from Brown University, an MA in statistics from Columbia University, and a PhD in finance from the University of Chicago Booth School of Business.<\/div>\n<\/dd>\n
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 <\/dt>\n

\n
Title<\/strong>: Real career advice and how to get a job that no one will tell you<\/div>\n<\/dd>\n
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 <\/dt>\n

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Abstract<\/strong><\/div>\n<\/dd>\n<\/dl>\n
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Tuesday, March 15, 2022 \u2014 Spring Recess, no seminar<\/strong><\/dt>\n<\/dl>\n
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Thursday, March 17, 2022 \u2014 Spring Recess, no seminar<\/strong><\/dt>\n<\/dl>\n
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Tuesday March 22, 2022
\nThis presentation takes place in Math 207, <\/span>not on Zoom<\/strong><\/dt>\n
<\/dd>\n
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Speaker<\/strong>: Alberto Botter, AQR Capital Management<\/div>\n<\/dd>\n<\/dl>\n
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Alberto Botter is a Managing Director within the Portfolio Management department at AQR Capital Management. In this role, he oversees the construction, optimization and management of AQR\u2019s Equities Long-Short and Tax-Managed products. Previously, Mr. Botter was a Quant in the Wealth Strategies group at Morgan Stanley. Mr. Botter earned a B.S. and an M.S. in economics from the University of Bologna, as well as an M.A. in financial mathematics from Columbia University.<\/div>\n<\/dd>\n
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<\/div>\n

 <\/dt>\n

\n
Title<\/strong>: Fundamentals of Active Investing<\/div>\n<\/dd>\n
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 <\/dt>\n

\n
Agenda<\/strong><\/div>\n