Shimon Kogan

Shimon Kogan
  • Visiting Associate Professor

Contact Information

  • office Address:

    SH-DH 2454
    Suite 2300
    Wharton Finance Department

Research Interests: Behavioral Finance, Empirical Asset Pricing

Links: Personal Website

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.

Teaching

Past Courses

  • FNCE385 - ASP - FIN-TECH

    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 alalyze 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.

  • FNCE885 - ASP - FIN-TECH

    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 alalyze 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.

Knowledge@Wharton

Bob Iger: Why Disney Is Betting Big on Streaming

During a recent visit to Wharton, Disney CEO Bob Iger said acquiring household names like Pixar, Marvel and Star Wars was key to the company’s strategy for making its new streaming service stand out in a crowded market.

Knowledge @ Wharton - 2019/11/12
Why Older Entrepreneurs Have the Edge

Research co-authored by Wharton's Daniel Kim busts the popular myth that the most successful startups are founded by whiz kids.

Knowledge @ Wharton - 2019/11/12
Medicare for All: Would It Work? And Who Would Pay?

Standardized health plans and a centralized clearinghouse would substantially ease the path for Medicare for All, says Obamacare advisor and Penn professor Zeke Emanuel.

Knowledge @ Wharton - 2019/11/12