Snehal Banerjee
Snehal Banerjee

Associate Professor of Finance

Print Overview

Snehal Banerjee joined the Kellogg School of Management in 2007. He has a BA from Brandeis University (2002) and a PhD from Stanford University's Graduate School of Business (2007). His research interests include information, learning and disagreement in financial markets, liquidity, behavioral finance and asset pricing. His current research involves studying the effects of uncertainty about other investors on asset prices.

Areas of Expertise
Behavioral Economics
Behavioral Finance
Information Economics

  • Recent Media Coverage

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Print Vita
PhD, 2007, Finance, Stanford University
BA, 2002, Computer Science, Economics (summa cum laude), Mathematics, Brandeis University

Academic Positions
Associate Professor, Finance, Kellogg School of Management, Northwestern University, 2012-present
Assistant Professor, Finance Department, Kellogg School of Management, Northwestern University, 2007-2012

Grants and Awards
Best Discussant Award, Mitsui Finance Symposium, 2014
Excellence in Refereeing Award, American Economic Association, 2013
Young Researcher Award, The Review of Financial Studies, 2010
Stanford Graduate School of Business Fellowship, Stanford Graduate School of Business, 2002-2006
Morris and Anna Feldberg Best Student in Economics Award, Brandeis University, 2002
Phi Beta Kappa, Phi Beta Kappa Society, 2001
Schiff Undergraduate Fellowship, Brandeis University, 2000-2001

Editorial Positions
Ad-hoc Reviewer, American Economic Review, Econometrica, Journal of Economic Dynamics and Control, Journal of the European Economic Association, Journal of Economic Theory, Journal of Finance, Journal of Financial Economics, Journal of Political Economy, Management Science, Review of Economic Studies, Review of Financial Studies

Print Research
Research Interests
Information Economics, Liquidity, and Behavioral Finance.

Banerjee, Snehal and Brett Green. Forthcoming. Signal or noise? Uncertainty and learning about whether other traders are informed. Journal of Financial Economics.
Banerjee, Snehal and Jeremy Graveline. 2014. Trading in Derivatives When the Underlying Is Scarce. Journal of Financial Economics. 111(3): 589-608.
Banerjee, Snehal and Jeremy Graveline. 2013. The Cost of Short Selling Liquid Securities. Journal of Finance. 68(2): 637-664.
Armstrong, Christopher, Snehal Banerjee and Carlos Corona. 2013. Factor-loading Uncertainty and Expected Returns. Review of Financial Studies. 26(1): 158-207.
Banerjee, Snehal. 2011. Learning from Prices and the Dispersion in Beliefs. Review of Financial Studies. 24(9): 3025-3068.
Banerjee, Snehal and Ilan Kremer. 2010. Disagreement and Learning: Dynamic Patterns of Trade. Journal of Finance. 65(4): 1269-1302.
Banerjee, Snehal, Ron Kaniel and Ilan Kremer. 2009. Price Drift as an Outcome of Differences in Higher Order Beliefs. Review of Financial Studies. 22(9): 3707-3734.
Working Papers
Banerjee, SnehalJesse Davis and Naveen Gondhi. 2015. When Transparency Improves, Must Prices Reflect Fundamentals Better?.
Banerjee, Snehal and Qingmin Liu. 2014. Transparency versus Tone: Public Communication with Limited Commitment.

Print Teaching
Teaching Interests

Asset Pricing Theory, Corporate Finance

Introduction to Financial Theory (FINC-485-0)
This course is an introduction to asset pricing theory and portfolio choice. The first part of the course introduces arbitrage theory, including state prices, equivalent martingale measures, beta pricing and the associated mean-variance analysis. The second part deals with optimal consumption/portfolio choice of agents and competitive equilibrium in the context of general preferences. The third part considers more detailed preference structures, including the theories of fund separation and Gorman aggregation, and expected utility theory. Time permitting, the course concludes with an introduction to rational expectations models with asymmetric information. Although the course is self-contained, it is best appreciated by students with some knowledge of microeconomics. Proficiency in elementary linear algebra and probability theory is required, as is some knowledge of basic nonlinear optimization theory.