Shimon Kogan

Shimon Kogan
  • Adjunct Associate Professor of Finance
  • Reichman University (IDC Herzliya)

Contact Information

  • 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.

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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 Compu- tational Linguistics Human Language Technologies Conference, Boulder, CO, May/June 2009.

« Securities Auctions under Moral Hazard: Theory and Experiments », with John Morgan, Re- view 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 Eco- nomics 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 Bi- ases 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

  • Vincent Glode, Burton Hollifield, Marcin Kacpercyzk, Shimon Kogan (2016), Is Investor Rationality Time Varying? Evidence from the Mutual Fund Industry, Behavioral Finance: Where do Investors’ Biases Come From?.

Teaching

Past Courses

  • ECON199 - 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. Please see the department for permission.

  • FNCE280 - 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.

  • FNCE737 - 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.

  • FNCE780 - 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.

  • FNCE899 - 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 899 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.

  • 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.

Activity

Latest Research

Vincent Glode, Burton Hollifield, Marcin Kacpercyzk, Shimon Kogan (2016), Is Investor Rationality Time Varying? Evidence from the Mutual Fund Industry, Behavioral Finance: Where do Investors’ Biases Come From?.
All Research