About This Group
The Quantitative Investing Group brings together professionals seeking to incorporate cutting edge quantitative investment techniques and alternative data sets in their investing and risk processes. Members include (but are not limited to) discretionary and systematic portfolio managers, risk managers, traders and fundamental analysts, data strategists, quantitative researchers, and others. The topic covered range from quantitative alpha generation, big data as well as alternative datasets, quantamental signals i.e. the intersection of fundamental analysis and quantitative decision making, mathematical and statistical aspects of modern quantitative analysis, use of programming languages or quant tools, Natural Language Processing, machine learning for investing and risk management, theory and implementation of AI in finance and more.
Chair: Richard V. Rothenberg
Richard V. Rothenberg
Richard V. Rothenberg is an Executive Director at Global A.I. Corporation, a Big Data and Artificial Intelligence company that provides quantitative research, data-driven signals and alternative data for Institutional clients, including Hedge Funds and Governments. Previously, Richard worked as a quantitative portfolio manager and researcher at multi-billion dollar hedge funds and global investment banks, including Deutsche Bank, Man investments and other leading institutions. Richard is a research affiliate at the Lawrence Berkeley National Laboratory – one of the world’s largest supercomputing laboratories – and an advisor at the Defense Advanced Research Projects Agency (DARPA). Richard is a member of the Task Force on data for the Sustainable Development Goals at the United Nations Conference on Trade and Development and member of the United Nations Science, Technology, and Innovation Expert Group. Richard holds a bachelor’s degree in Economics and Computational Finance from the Monterrey Institute of Technology, a Certificate of Quantitative Finance from the CQF Institute, and a Master’s in Management and Quantitative Finance from Columbia University in New York City.
Peg DiOrio, CFA
Head of Quantitative Equity Portfolio Management
Peg DiOrio is the head of quantitative equity portfolio management at Voya Investment Management and serves as a portfolio manager for the active quantitative strategies. Prior to joining the firm, she was a quantitative analyst with Alliance Bernstein/Sanford C. Bernstein for sixteen years where she was responsible for multivariate and time series analysis for low volatility strategies, global equities, REITs and options. Previously, she was a senior investment planning analyst with Sanford C. Bernstein. Peg received an MS in Applied Mathematics, Statistics and Operations Research from the Courant Institute of Mathematical Sciences, NYU and a BS from SUNY Stony Brook. She holds the Chartered Financial Analyst® designation. She formerly served as president of the Society of Quantitative Analysts and continues to serve on the board of directors. Peg is on the external advisory board for the Applied Math and Statistics Department of Stony Brook University.
Ron Khan, PhD
Petter Kolm, PhD
Gordon Ritter, PhD
Marcos lopez de Prado, PhD
Miquel Noguer Alonso, PhD
Matthew Dixon, PhD
Igor Halperin, PhDHamid Rashid, PhD
Petter Kolm, PhD
Petter Kolm is the Director of the Mathematics in Finance Master’s Program and Clinical Professor at the Courant Institute of Mathematical Sciences, New York University and the Principal of the Heimdall Group, LLC. Previously, Petter worked in the Quantitative Strategies Group at Goldman Sachs Asset Management where his responsibilities included researching and developing new quantitative investment strategies for the group’s hedge fund. Petter has coauthored four books: Financial Modeling of the Equity Market: From CAPM to Cointegration (Wiley, 2006), Trends in Quantitative Finance (CFA Research Institute, 2006), Robust Portfolio Management and Optimization (Wiley, 2007), and Quantitative Equity Investing: Techniques and Strategies (Wiley, 2010). He holds a Ph.D. in Mathematics from Yale, an M.Phil. in Applied Mathematics from the Royal Institute of Technology, and an M.S. in Mathematics from ETH Zurich.
Petter is a member of the editorial boards of the International Journal of Portfolio Analysis and Management (IJPAM), Journal of Financial Data Science (JFDS), Journal of Investment Strategies (JoIS), Journal of Machine Learning in Finance (JMLF) and Journal of Portfolio Management (JPM). He is an Advisory Board Member of Betterment (one of the largest robo-advisors) and Alternative Data Group (ADG). Petter is also on the Board of Directors of the International Association for Quantitative Finance (IAQF) and Advisory Board Member of Artificial Intelligence Finance Institute (AIFI).
As a consultant and expert witness, Petter has provided his services in areas including alternative data, data science, econometrics, forecasting models, high frequency trading, machine learning, portfolio optimization w/ transaction costs and taxes, quantitative and systematic trading, risk management, robo-advisory and investing, smart beta strategies, transaction costs, and tax-aware investing.
Marcos lopez de Prado, PhD
Prof. Marcos López de Prado is the CIO of True Positive Technologies (TPT), and Professor of Practice at Cornell University’s School of Engineering. He has over 20 years of experience developing investment strategies with the help of machine learning algorithms and supercomputers. Marcos launched TPT after he sold some of his patents to AQR Capital Management, where he was a principal and AQR’s first head of machine learning. TPT is currently engaged by clients with a combined AUM in excess of $1 trillion. Marcos also founded and led Guggenheim Partners’ Quantitative Investment Strategies business, where he managed up to $13 billion in assets, and delivered an audited risk-adjusted return (information ratio) of 2.3.
Concurrently with the management of investments, since 2011 Marcos has been a research fellow at Lawrence Berkeley National Laboratory (U.S. Department of Energy, Office of Science). He has published dozens of scientific articles on machine learning and supercomputing in the leading academic journals, is a founding co-editor of The Journal of Financial Data Science, has testified before the U.S. Congress on AI policy, and SSRN ranks him as the most-read author in economics. Marcos is the author of several graduate textbooks, including Advances in Financial Machine Learning (Wiley, 2018) and Machine Learning for Asset Managers (Cambridge University Press, 2020).
Marcos earned a PhD in financial economics (2003), a second PhD in mathematical finance (2011) from Universidad Complutense de Madrid, and is a recipient of Spain’s National Award for Academic Excellence (1999). He completed his post-doctoral research at Harvard University and Cornell University, where he is a faculty member. Marcos has an Erdős #2 according to the American Mathematical Society, and in 2019, he received the ‘Quant of the Year Award’ from The Journal of Portfolio Management.
Miquel Noguer Alonso, PhD
Miquel Noguer i Alonso is a financial markets practitioner with more than 20 years of experience in asset management, he is the Founder of Artificial Intelligence Finance Institute. Head of Development at Global AI and co-Editor of the Journal of Machine Learning in Finance.
He worked for UBS AG (Switzerland) as Executive Director. He is member of European Investment Committee for the last 10 years. He worked as a Chief Investment Office and CIO for Andbank from 2000 to 2006. He started his career at KPMG.
He is Visiting Professor at NYU Courant Institute of Mathematical Sciences and the CQF institute. He has been Adjunct Professor at Columbia University teaching Asset Allocation, Big Data in Finance and Fintech. He is also Professor at ESADE teaching Hedge Fund, Big Data in Finance and Fintech. He taught the first Fintech and Big Data course at the London Business School in 2017.
He received an MBA and a Degree in business administration and economics in ESADE in 1993. In 2010 he earned a PhD in quantitative finance with a Summa Cum Laude distinction (UNED – Madrid Spain). He completed a Postdoc in Columbia Business School in 2012. He collaborated with the Mathematics department of Fribourg during his PhD. He also holds the Certified European Financial Analyst (CEFA) 2000. He also holds the ARPM certificate.
His research interests range from asset allocation, big data, machine learning to algorithmic trading and Fintech. His academic collaborations include a visiting scholarship in Columbia University in 2013 in the Finance and Economics Department, in Fribourg University in 2010 in the mathematics department, and giving presentations in Indiana University, ESADE and CAIA and several industry seminars like the Quant Summit USA 2019 and 2010.
Matthew Dixon, PhD
Matthew Dixon, Ph.D, FRM, began his career as a quant in structured credit trading at Lehman Brothers. He has consulted for numerous investment management, trading and financial technology firms in machine learning and risk analytics. He is the author of the 2020 textbook “Machine Learning in Finance: From Theory to Practice” and has written over 20 peer reviewed papers on machine learning and computational finance, including SIAM J. Financial Mathematics and the Journal of Computational Finance. He is the recipient of an Illinois Tech innovation award, and his research has been funded by Intel and the NSF. Matthew has recently contributed to the CFA syllabus on machine learning and he currently serves on the CFA advisory committee for quantitative trading. He has been invited internationally to give talks at prestigious seminars organized by investment banks and universities in addition to being quoted in the Financial Times and Bloomberg Markets. He holds a Ph.D. in Applied Math from Imperial College, has held visiting academic appointments at Stanford and UC Davis, and is a tenure-track Assistant Professor at Illinois Tech.
Hamid Rashid, PhD
Dr. Hamid Rashid – Chief, Global Economic Monitoring at the United Nations Department of Economic and Social Affairs (UNDEA) – is the lead author of the UN flagship report, the World Economic Situation and Prospects, jointly published by UNDESA, UNCTAD, the UN Regional Commissions and UN-WTO. Previously, he served as the Senior Adviser for Macroeconomic Policy in UNDESA, advising finance, central banks and planning authorities in developing countries on fiscal and monetary policy issues. Prior to joining UN/DESA in 2010, Dr. Rashid served as a Senior Adviser in UNDP’s Bureau for Development Policy and also as the Director General for Multilateral Economic Affairs at the Ministry of Foreign Affairs in Bangladesh.
Dr. Rashid earned his Ph.D. in economics and finance from Columbia University in New York under the supervision of Nobel Laureate Prof. Joseph Stiglitz. In a recent co-authored paper, Prof. Stiglitz and Dr. Rashid warn against the looming risks of a catastrophic debt crisis in developing countries. He obtained a Bachelor of Science degree from the University of Texas. Dr. Rashid’s research interest includes macroeconomic policies, international finance, debt management, technological change, financial market liberalization and their impact on economic growth and sustainable development.
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