The Journal of Finance publishes leading research across all the major fields of finance. It is one of the most widely cited journals in academic finance, and in all of economics. Each of the six issues per year reaches over 8,000 academics, finance professionals, libraries, and government and financial institutions around the world. The journal is the official publication of The American Finance Association, the premier academic organization devoted to the study and promotion of knowledge about financial economics.
AFA members can log in to view full-text articles below.
View past issues
Search the Journal of Finance:
Search results: 2.
Attention‐Induced Trading and Returns: Evidence from Robinhood Users
Published: 09/30/2022 | DOI: 10.1111/jofi.13183
BRAD M. BARBER, XING HUANG, TERRANCE ODEAN, CHRISTOPHER SCHWARZ
We study the influence of financial innovation by fintech brokerages on individual investors’ trading and stock prices. Using data from Robinhood, we find that Robinhood investors engage in more attention‐induced trading than other retail investors. For example, Robinhood outages disproportionately reduce trading in high‐attention stocks. While this evidence is consistent with Robinhood attracting relatively inexperienced investors, we show that it is also driven in part by the app's unique features. Consistent with models of attention‐induced trading, intense buying by Robinhood users forecasts negative returns. Average 20‐day abnormal returns are −4.7% for the top stocks purchased each day.
A (Sub)penny for Your Thoughts: Tracking Retail Investor Activity in TAQ
Published: 05/03/2024 | DOI: 10.1111/jofi.13334
BRAD M. BARBER, XING HUANG, PHILIPPE JORION, TERRANCE ODEAN, CHRISTOPHER SCHWARZ
We placed 85,000 retail trades in six retail brokerage accounts from December 2021 to June 2022 to validate the Boehmer et al. algorithm, which uses subpenny trade prices to identify and sign retail trades. The algorithm identifies 35% of our trades as retail, incorrectly signs 28% of identified trades, and yields uninformative order imbalance measures for 30% of stocks. We modify the algorithm by signing trades using the quoted spread midpoints. The quote midpoint method does not affect identification rates but reduces the signing error rates to 5% and provides informative order imbalance measures for all stocks.