Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
A model of investor sentiment1We are grateful to the NSF for financial support, and to Oliver Blanchard, Alon Brav, John Campbell (a referee), John Cochrane, Edward Glaeser, J.B. Heaton, Danny Kahneman, David Laibson, Owen Lamont, Drazen Prelec, Jay Ritter (a referee), Ken Singleton, Dick Thaler, an anonymous referee, and the editor, Bill Schwert, for comments.1
19982.0k citationsNicholas Barberis, Andrei Shleifer et al.Journal of Financial Economicsprofile →
Prospect Theory and Asset Prices
20011.2k citationsNicholas Barberis, Mingxin Huang et al.profile →
Stocks as Lotteries: The Implications of Probability Weighting for Security Prices
Countries citing papers authored by Nicholas Barberis
Since
Specialization
Citations
This map shows the geographic impact of Nicholas Barberis's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Nicholas Barberis with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nicholas Barberis more than expected).
Fields of papers citing papers by Nicholas Barberis
This network shows the impact of papers produced by Nicholas Barberis. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Nicholas Barberis. The network helps show where Nicholas Barberis may publish in the future.
Co-authorship network of co-authors of Nicholas Barberis
This figure shows the co-authorship network connecting the top 25 collaborators of Nicholas Barberis.
A scholar is included among the top collaborators of Nicholas Barberis based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Nicholas Barberis. Nicholas Barberis is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Barberis, Nicholas, Robin Greenwood, Lawrence J. Jin, & Andrei Shleifer. (2014). X-CAPM: An extrapolative capital asset pricing model. Journal of Financial Economics. 115(1). 1–24.395 indexed citations breakdown →
5.
Barberis, Nicholas. (2013). Thirty Years of Prospect Theory in Economics: A Review and Assessment. The Journal of Economic Perspectives. 27(1). 173–196.778 indexed citations breakdown →
Barberis, Nicholas, Huang Ming, & Richard H. Thaler. (2006). Individual Preferences, Monetary Gambles, and Stock Market Participation: A Case for Narrow Framing. American Economic Review. 96(4). 1069–1090.358 indexed citations breakdown →
Barberis, Nicholas & Andrei Shleifer. (2003). Style investing. Journal of Financial Economics. 68(2). 161–199.748 indexed citations breakdown →
18.
Barberis, Nicholas. (2000). Investing for the Long Run when Returns Are Predictable. The Journal of Finance. 55(1). 225–264.861 indexed citations breakdown →
Barberis, Nicholas, Andrei Shleifer, & Robert W. Vishny. (1998). A model of investor sentiment1We are grateful to the NSF for financial support, and to Oliver Blanchard, Alon Brav, John Campbell (a referee), John Cochrane, Edward Glaeser, J.B. Heaton, Danny Kahneman, David Laibson, Owen Lamont, Drazen Prelec, Jay Ritter (a referee), Ken Singleton, Dick Thaler, an anonymous referee, and the editor, Bill Schwert, for comments.1. Journal of Financial Economics. 49(3). 307–343.2035 indexed citations breakdown →
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.