Snehal Banerjee
Snehal Banerjee

FINANCE
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 investor disagreement on asset prices and trading volume.


Areas of Expertise
Behavioral Economics
Behavioral Finance
Information Economics
  • Recent Media Coverage

    Economist Intelligence Unit: Executive Briefing: When investors disagree

    Economist Intelligence Unit: Executive Briefing: Agreeing to disagree

    See all Kellogg in the Media
Print Vita
Education
PhD, 2007, Finance, Stanford University
BA, 2002, Computer Science, Economics (summa cum laude), Mathematics, Brandeis University

Academic Positions
Assistant Professor, Finance Department, Kellogg School of Management, Northwestern University

Grants and Awards
Schiff Undergraduate Fellowship, 2000 - 2001
Phi Beta Kappa, 2001
Morris and Anna Feldberg Best Student in Economics Award, 2002
Stanford Graduate School of Business Fellowships, 2002 - 2006
The Review of Financial Studies Young Researcher Award, 2010

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


Articles
Armstrong, Christopher, Snehal Banerjee and Carlos Corona. Forthcoming. Factor-loading Uncertainty and Expected Returns. Review of Financial Studies.
Link
Banerjee, Snehal and Jeremy Graveline. Forthcoming. The Cost of Short Selling Liquid Securities. Journal of Finance .
AbstractLink
Banerjee, Snehal. 2011. Learning from Prices and the Dispersion in Beliefs. Review of Financial Studies. 24(9): 3025-3068.
AbstractLink
Banerjee, Snehal and Ilan Kremer. 2010. Disagreement and Learning: Dynamic Patterns of Trade. Journal of Finance. 65(4): 1269-1302.
AbstractLink
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.
AbstractLink
Working Papers
Banerjee, Snehal and Jeremy Graveline. 2011. Trading in Derivatives When the Underlying Is Scarce.

 
Print Teaching
Teaching Interests
Finance

Full-Time / Part-Time MBA
Finance I/II (FINC-440-0)

This course counts toward the following majors: Analytical Finance, Finance

This course combines the materials of FINC-430 and FINC-441 into an intensive one-quarter course available to One-Year students and first-year students interested in accelerating their studies of finance. Students choosing this option should expect the presentations, readings and other homework to be at least double those of the regular courses. By combining these two courses into one quarter, students are able to take more advanced finance electives during their first year and have the opportunity to include an extra finance elective in their course schedules. Please note that this course carries the weight of one course only. Prerequisites: Knowledge of (a) probability and statistics through linear regression and (b) financial accounting. Requirement (a) may be satisfied with prior or concurrent registration in DECS 434, sufficient previous course work in statistics. Requirement (b) may be satisfied with prior or concurrent registration in ACCT 430 or sufficient previous course work in financial accounting. MECN 430 is recommended.

Doctoral
Introduction to Financial Theory (FINC-485-0)

This course counts toward the following majors: Finance

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.