Shimon Kogan
- Adjunct Associate Professor of Finance
Contact Information
- Primary Email:
skogan@wharton.upenn.edu - Office Phone:
215-898-7622
- office Address:
2419 Steinberg-Dietrich Hall
3620 Locust Walk
Philadelphia, PA 19104
Research Interests: Behavioral Finance, Empirical Asset Pricing, FinTech, Machine Learning and NLP
Links: Personal Website
Overview
Prof. Kogan is an Associate Professor of Finance, with positions at IDC Herzliya and the Wharton School. He was on the faculty at MIT Sloan, Carnegie Mellon University, University of Texas at Austin, and Duke. He held several investment management positions and board advisory roles. He earned his MBA and PhD from the University of California at Berkeley and his BA from Tel Aviv University.
Dr. Kogan’s research focuses on behavioral finance with application to capital markets. He is interested in understanding how information is processed in markets and his approach is interdisciplinary, integrating tools and insights from machine learning and AI. His research appeared in some of the profession’s top journals such as the Review of Financial Studies, Journal of Finance, and the American Economic Review, and he was invited to present his work in leading conferences and universities, such as MIT, Wharton, Harvard, and Yale.
His teaching is focused on machine learning, data science, and blockchain, and their implications for finance. He has been teaching Fintech, Data Science for Finance, Investment, Portfolio Management, Derivatives, and Behavioral Finance. He is involved with several startups in the fintech space and is often invited to speak about these issues in both academic and practitioners’ conferences.
Research
« Distinguishing Overconfidence from Rational Best-Response on Information Aggregation », Review of Financial Studies, 2009, 22(5), pp. 1889-1914.
« Predicting Risk from Financial Reports with Regression », with Dimitry Levin, Bryan Rout- ledge, Jacob Sagi, and Noah Smith, Proceedings of the North American Association for Computational Linguistics Human Language Technologies Conference, Boulder, CO, May/June 2009.
« Securities Auctions under Moral Hazard: Theory and Experiments », with John Morgan, Review of Finance, 2010, 14 (3), pp. 477-520.
« Coordination in the Presence of Asset Markets », with Anthony Kwasnica and Roberto Weber, American Economic Review, 2011, 101(2) , pp. 927-947.
« Investor Inattention and the Market Impact of Summary Statistics », with Thomas Gilbert, Lars Lochstoer, and Ataman Ozyildirim, Management Science, Special Issue on Behavioral Economics and Finance, 2012, 58(2), pp. 336-350.
« Trading Complex Assets », with Bruce Carlin and Richard Lowery, Journal of Finance, 2013, 68(5), 1937-1960.
« Business Microloans for U.S. Subprime Borrowers », with Cesare Fracassi, Mark J. Garmaise, and Gabriel Natividad, Journal of Financial and Quantitative Analysis, 2016, 51 (1), pp. 55-83.
« Is Investor Rationality Time Varying? Evidence from the Mutual Fund Industry », with Vincent Glode, Burton Hollifield, and Marcin Kacperczyk, Behavioral Finance: Where do Investors Biases Come From?, Itzhak Venezia [ed.], World Scientific Publishing Co., 2016, pp. 67-113.
« Information, Trading, and Volatility: Evidence from Firm-Specific News», with Jacob Boudoukh, Ronen Feldman, and Matthew Richardson, AQR Research Excellence Award Finalist. The Review of Financial Studies, Volume 32, Issue 3, 1 2019, Pages 992–1033, https://doi.org/10.1093/rfs/hhy083
« Social Media and Financial News Manipulation », with Toby Moskowitz and Marina Niessner. Review of Finance, Oct 2022, https://doi.org/10.1093/rof/rfac058.
Shimon Kogan (Draft), Pure Momentum in Cryptocurrency Markets.
Abstract: Momentum is one of the most widespread, persistent, and puzzling phenomenon in asset pricing. The prevailing explanation for momentum is that investors under-react to new information, and thus asset prices tend to drift over time. We use a unique feature of cryptocurrency markets: the fact that they are open 24/7, and report returns over the last 24 hours. Thus, the one-day return is subject to predictable fluctuations based on the removal of lagged information. We show that investors respond positively to changes in reported returns that are unrelated to any new release of information, or change in the asset fundamentals. We call this behavioral anomaly "Pure Momentum".
Description: Joint with Cesare Fracassi.
Shimon Kogan (Draft), Are Cryptos Different? Evidence from Retail Trading.
Abstract: Trading in cryptocurrencies has grown rapidly over the last decade, primarily dominated by retail investors. Using a dataset of 200,000 retail traders from eToro, we show that they have a different model of the underlying price dynamics in cryptocurrencies relative to other assets. Retail traders in our sample are contrarian in stocks and gold, yet the same traders follow a momentum-like strategy in cryptocurrencies. Individual characteristics do not explain the differences in how people trade cryptocurrencies versus stocks, suggesting that our results are orthogonal to differences in investor composition or clientele effects. Furthermore, our findings are not explained by inattention, differences in fees, or preference for lotterylike stocks. We conjecture that retail investors hold a model of cryptocurrency prices, where price changes imply a change in the likelihood of future widespread adoption, which in turn pushes asset prices further in the same direction.
Description: Joint with Igor Makarov, Marina Niessner, and Antoinette Schoar.
Teaching
All Courses
ECON4999 - Independent Study
Individual study and research under the direction of a member of the Economics Department faculty. At a minimum, the student must write a major paper summarizing, unifying, and interpreting the results of the study. This is a one semester, one c.u. course.
FNCE2800 - FinTech
(Formerly FNCE 385) The course exposes students to this fast-growing and exciting intersection between finance (Fin) and technology (Tech) while emphasizing the role data and analytics play. The course is structured around three main FinTech areas: (i) Lending/Banking services, (ii) Clearing (iii) Trading. It provides specific coverage and examples of developments from(1) market-place lending, (2) blockchain and distributed ledgers, (3) quantitative trading and its use of non-standard inputs. In each of these areas, we start by analyzing the marketplace, the incumbents, and then proceed to analyze the impact of the most relevant technologies have on the business. The course is built around data/code examples, cases, guest lectures, and group projects. Student are thus expected to work in teams and demonstrate a high level of independent learning and initiative.
FNCE7370 - Data Science For Finance
This course will introduce students to data science for financial applications using the Python programming language and its ecosystem of packages (e.g., Dask, Matplotlib, Numpy, Numba, Pandas, SciPy, Scikit-Learn, StatsModels). To do so, students will investigate a variety of empirical questions from different areas within finance by way of data labs, or case studies that rely on data and analytics. Some of the areas that may be covered in the course, subject to time constraints, include: FinTech, investment management, corporate finance, corporate governance, venture capital, private equity. The course will highlight how big data and data analytics shape the way finance is practiced. Some programming and experience is helpful though knowledge of Python is not assumed.
FNCE7800 - FinTech
(Formerly FNCE 885) The course exposes students to this fast-growing and exciting intersection between finance (Fin) and technology (Tech) while emphasizing the role data and analytics play. The course is structured around three main FinTech areas: (i) Lending/Banking services, (ii) Clearing (iii) Trading. It provides specific coverage and examples of developments from(1) market-place lending, (2) blockchain and distributed ledgers, (3) quantitative trading and its use of non-standard inputs. In each of these areas, we start by analyzing the marketplace, the incumbents, and then proceed to analyze the impact of the most relevant technologies have on the business. The course is built around data/code examples, cases, guest lectures, and group projects. Student are thus expected to work in teams and demonstrate a high level of independent learning and initiative.
FNCE8990 - Independent Study
Independent Study Projects require extensive independent work and a considerable amount of writing. ISP in Finance are intended to give students the opportunity to study a particular topic in Finance in greater depth than is covered in the curriculum. The application for ISP's should outline a plan of study that requires at least as much work as a typical course in the Finance Department that meets twice a week. Applications for FNCE 8990 ISP's will not be accepted after the THIRD WEEK OF THE SEMESTER. ISP's must be supervised by a Standing Faculty member of the Finance Department.