Shimon Kogan

• Marius Guenzel, Shimon Kogan, Marina Niessner, Kelly Shue, AI Personality Extraction from Faces: Labor Market Implications.
• Shimon Kogan (2024), Are Cryptos Different? Evidence from Retail Trading, . Abstract

  Trading in cryptocurrencies grew rapidly over the last decade, dominated by retail investors. Using data from eToro, we show that retail traders are contrarian in stocks and gold, yet the same traders follow a momentum-like strategy in cryptocurrencies. The differences are not explained by individual characteristics, investor composition, inattention, differences in fees, or preference for lottery-like assets. We conjecture that retail investors have a model where cryptocurrency price changes affect the likelihood of future widespread adoption, which leads them to further update their price expectations in the same direction.

  Description
  Joint with Igor Makarov, Marina Niessner, and Antoinette Schoar.
• Shimon Kogan, AI Prediction of Labor Outcomes: Personality Extraction from Faces. Abstract

  Recent developments in machine learning and artificial intelligence have enabled the inference of Big 5 personality traits from a single photograph of an individual. This study evaluates the ability of the novel Photo Big 5 personality measure to forecast labor market outcomes. Using the universe of LinkedIn data for MBA graduates, as well as detailed admissions and matriculation records from a top-tier MBA program, we show that the Photo Big 5 is highly predictive of initial compensation. Compared to older survey-based personality measures, the Photo Big 5 is readily available to all employers and less prone to manipulation, and thus has the potential to be widely adopted in firm hiring and promotions processes.

  Description
  with Marks Guenzel, Marina Niessner, and Kelly Shue.
• Shimon Kogan, Avoiding Idiosyncratic Volatility: Flow Sensitivity to Individual Stock Returns. Abstract

  Despite positive and significant earnings announcement premia, we find that institutional investors reduce their exposure to stocks before earnings announcements. A novel result on the sensitivity of flows to individual stock returns provides a potential explanation. We show that extreme announcement returns for an individual holding lead to substantial outflows, controlling for overall performance, and they increase the probability of managers leaving the fund. Reducing the exposure to these stocks before the announcement mitigates the outflows. We build a model to describe and quantify this tradeoff. Overall, the paper identifies a new dimension of limits to arbitrage for institutions.

  Description
  with Marco Di Maggio, Francesco A. Franzoni, and Ran Xing.
• Shimon Kogan (2023), Social Media and Financial News Manipulation, Review of Finance. Abstract

  We examine an undercover Securities and Exchange Commission (SEC) investigation into the manipulation of financial news on social media. While fraudulent news had a direct positive impact on retail trading and prices, revelation of the fraud by the SEC announcement resulted in significantly lower retail trading volume on all news, including legitimate news, on these platforms. For small firms, volume declined by 23.5% and price volatility dropped by 1.3%. We find evidence consistent with concerns of fraud causing the decline in trading activity and price volatility, which we interpret through the lens of social capital, and attempt to rule out alternative explanations. The results highlight the indirect consequences of fraud and its spillover effects that reduce the social network’s impact on information dissemination, especially for small, opaque firms.

  Description
  with Toby Moskowitz and Marina Niessner.
• 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, Fee the People: Retail Investor Behavior and Trading Commission Fees. Abstract

  We show retail investors are highly responsive to changes in trading commission fees. Using a triple-difference research design around the removal of fees for retail investors on the international retail broker platform, eToro, we show investors responded by trading approximately 30% more frequently, in smaller order sizes, and increasing portfolio turnover. Removing fees also spurred retail investors to reallocate their portfolios and diversify. Retail investors’ gross return performance did not significantly change around the fee removal despite trading more often, but retail investors earned significantly higher returns on a net basis after accounting for fees incurred in the pre-period. Finally, using demographic information, we show removing fees disproportionately affected inexperienced investors with lower deposit amounts and lesser technological sophistication both by expanding the extensive margin of investors and changing trading activity for the intensive margin of investors. Together, our results suggest commission fees play an influential role as a speed bump for retail investor participation, trading activity, and diversification.

  Description
  with Omri Even-Tov, Kimberlyn George, and Eric C. So.
• Shimon Kogan, The Asymmetry in Responsible Investing Preferences. Abstract

  We design an experiment to understand how social preferences affect investment decisions through stock allocations and probability assessments. The major preference channel is asymmetric in social outcomes – although negative and positive responsible investment (RI) externalities have the same magnitudes, negative externalities have greater impact on investment choices. The effect is persistent, but heterogenous. We also find asymmetries in belief formation and learning constitute a secondary channel. Overall, our results are consistent with important stylized empirical facts and the predictions of recent RI theories that social preferences lead to different investment choices, but our analyses also suggest important future modeling directions.

  Description
  with Jacquelyn Humphrey, Jacob Sagi, and Laura Starks.
• Shimon Kogan (2018), Information, Trading, and Volatility: Evidence from Firm-Specific News, The Review of Financial Studies. Abstract

  What moves stock prices? Prior literature concludes that the revelation of private information through trading, and not public news, is the primary driver. We revisit the question by using textual analysis to identify fundamental information in news. We find that this information accounts for 49.6% of overnight idiosyncratic volatility (vs. 12.4% during trading hours), with a considerable fraction due to days with multiple news types. We use our measure of public information arrival to reinvestigate two important contributions in the literature related to individual s of stock returns on aggregate factors.

  Description
  with Jacob Boudoukh, Ronen Feldman, and Matthew Richardson.
• Shimon Kogan, “Is Investor Rationality Time Varying? Evidence from the Mutual Fund Industry”. In Behavioral Finance: Where do Investors Bi- ases Come From?, edited by, (2016) Abstract

  We provide novel evidence that mutual fund returns are predictable after periods of high market returns but not after periods of lowmarket returns. The asymmetric conditional predictability in relative performance cannot be fully explained by time-varying differences in transaction costs, in style exposures, or in survival probabilities of funds. Performance predictability is more pronounced for funds catering to retail investors than for funds catering to institutional investors, suggesting that unsophisticated investors make systematic mistakes in their capital allocation decisions.

  Description
  with Vincent Glode, Burton Hollifield, and Marcin Kacperczyk.
• All Research from Shimon Kogan »

Past 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

  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.

• FNCE3990 - Independent Study

  Integrates the work of the various courses and familiarizes the student with the tools and techniques of research.

• 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

  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.

Knowledge @ Wharton

• College Football Playoffs with Bill Connelly, Knowledge @ Wharton - 01/08/2025
• The Real Estate Market Survived Until 2025. What’s Next?, Knowledge @ Wharton - 01/07/2025
• AI in 2025: What Challenges Lie Ahead?, Knowledge @ Wharton - 01/07/2025
• Jeremy Siegel: 2025 Outlook for Stocks and the Economy, Knowledge @ Wharton - 01/07/2025
• What’s Ahead for Real Estate in 2025?, Knowledge @ Wharton - 01/07/2025
• Confidence in Cryptocurrency | David Reibstein and John Zhang (Part 1), Knowledge @ Wharton - 01/07/2025
• What’s Ahead for the Economy in 2025, Knowledge @ Wharton - 01/06/2025
• How Did AI Grow in 2024 and What’s in Store for 2025?, Knowledge @ Wharton - 01/02/2025
• NFL Data Science with Tom Bliss, Knowledge @ Wharton - 12/25/2024
• Overwhelmed by Too Many Choices When Shopping? Wharton Retail Expert Weighs In, Knowledge @ Wharton - 12/19/2024