Akash Bandyopadhyay
Akash Bandyopadhyay

FINANCE
Clinical Assistant Professor of Finance

Print Overview

Akash Bandyopadhyay is a Senior Lecturer in the Finance Department. Before joining the Kellogg faculty, he was a faculty member of Finance at the Graduate School of Business of the University of Chicago (now known as Booth School of Business). He also served as a Visiting Assistant Professor of Finance at the University of Illinois at Urbana-Champaign.

While finishing his PhD, Bandyopadhyay made a leap to Wall Street through his research on equity derivatives pricing models for Banc of America Securities and seminar presentation of his models at Goldman Sachs. After graduation, he continued working as a research quant in the investment banks (at the equity derivatives risk management group of Deutsche Bank, at the derivative analytics group for the corporate and institutional clients of Merrill Lynch, and at the market risk management group of Société Générale) before returning to academia.

Akash Bandyopadhyay’s research interests are in the areas of asset pricing theory, models, and empirical tests. His current focus is incomplete markets and markets with differential information and/or partial liquidity, and financial economics beyond standard models.

Students of the University of Chicago’s Graduate School of Business (now Booth School of Business) rated Akash Bandyopadhyay as the school's "most enthusiastic finance professor" when he taught there. His finance classes were among the most popular and demanding courses at Chicago GSB (Booth). He received numerous citations and awards for teaching excellence.

Bandyopadhyay received his PhD in Theoretical Physics from the University of Illinois at Urbana-Champaign in 2001.

Print Vita
Education
PhD, 2001, Theoretical Physics, University of Illinois at Urbana-Champaign
MS, 1996, Theoretical Physics, University of Notre Dame
MSc, 1992, Physics, University of Calcutta, India
BSc, 1989, Physics, Mathematics and Statistics, Presidency College, University of Calcutta, India

Academic Positions
Senior Lecturer, Finance, Kellogg School of Management, Northwestern University, 2009-present
Adjunct Assistant Professor, Finance, Booth School of Business, University of Chicago, 2003-2008
Visiting Assistant Professor, Finance, College of Business, University of Illinois at Urbana-Champaign, 2001-2003

 
Print Research
Research Interests
Asset Pricing Theory, Models, and Empirical Tests
Derivative Securities and Markets
Equity Valuation, Investments and Portfolio Choice
Financial Econometrics
Financial Engineering
Financial Risk Management
Fixed Income Securities and Markets


 
Print Teaching
Full-Time / Part-Time MBA
Investments (FINC-460-0)

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

This comprehensive study of financial investments will cover active portfolio strategies in stocks and bonds, optimal portfolio selection from the perspective of individual and institutional investors, and the role of style and performance benchmarks in portfolio management. Special topics such as performance evaluation, role of options and futures, liquidity and trading costs, and potential investment strategies to exploit mispricing in financial assets (such as, those used by the hedge funds) will also be covered. Instead of focusing on pure theoretical models, the emphasis is given on the empirical facts observed in asset prices in worldwide capital markets, understanding whether they manifest new dimension of systematic risk, and how to design smart portfolios to take advantage of multiple dimensions of systematic risk. This is a quantitative course. The course develops an applied analytical framework of financial investments. Therefore students interested in this course are expected to have sound knowledge of basic statistics and regression analysis.

Empirical Methods in Finance (FINC-970-0)

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

This advanced course is designed for students with strong quantitative skills who want to learn state-of-the-art principles of investments and statistical techniques of portfolio management. You will learn programming tools and econometrics for analyzing financial markets, study state-of-the-art financial models from contemporary research, and examine whether markets are beat-able. Students who plan to work in financial industry under a technical capacity, such as, as a quantitative portfolio manager or as a risk manager in hedge funds or investment banks, are expected to find this course immensely rewarding.

Topics include (but not limited to) (1)multifactor models, such as Fama-French factors, momentum strategy, and liquidity factors, (2)return predictability, (3)random nature of volatility and structuring options portfolio to receive variance risk premium, (4)term structure of riskfree rates, yield curve modeling and risk-factors in bond premium, and (5)effects of illiquidity or transaction costs. Necessary statistical methods, such as, Fama-MacBeth regression, factor extraction via principal component analysis, stochastic calculus (with jumps), Itô’s lemma, bootstrapping of yield curve, and techniques of Monte Carlo simulation will be developed within the course.

Expertise in running regression and doing statistical analysis in EXCEL (or in another platform) is required. Hedge funds seem impressed with MATLAB abilities. We will organize tutorial sessions on MATLAB.

Course grade will be based on 5 bi-weekly spreadsheet/data-analysis assignments (4 will be counted towards course grade) and a final take-home project. Students will have unrestricted access to the WRDS database of financial data for working on the assignments and test.

Prerequisites: Finance/Economics at the level of FINC 460-0, and ideally, students must be comfortable with basic differential and integral calculus (including Taylor series expansion), elementary matrix algebra, linear regression analysis, and statistics at the level of hypotheses testing. Setup an appointment with the instructor in case you don’t have the prerequisites but tremendously interested in taking this class.

Doctoral
Special Topics in Finance: Empirical Methods in Finance (FINC-530-0)
This is a course on empirical asset pricing with a focus on topics of current research interest and dynamic models in continuous-time. We will examine empirical evidence of existing theory and provide a roadmap of ideas for future theoretical inquiry. Since intriguing empirical work is always guided by theory, we will cover the necessary asset pricing theory along the way.

The course has a broad coverage and a long reading list. The emphasis will be on studying the current literature. Necessary econometric methods, such as, Fama-MacBeth procedure, GMM, time-series processes, methods for continuous-time processes, such as, Itô calculus (including jumps), martingales and changes-of-measure, and techniques of Monte Carlo simulation will be developed within the course. Topics include (but not limited to) (1)systematic risk factors (including Fama-French factors, momentum factor, liquidity factors, jumps and statistical factors, and macro-factors) in cross-sectional and time-series behavior of asset returns, (2)return predictability, (3)consumption bases asset pricing models, (4)stochastic nature of volatility and existence of variance risk premium, and (5)models for term-structure of riskfree rates and factors affecting bond risk premia. Time permit, we will also discuss current state of research on long-standing empirical puzzles confronting canonical theory, such as, equity premium puzzle.

This course is intended for PhD students in finance and economics who have successfully completed at least one quarter of econometrics and financial economics. Expertise in running regression and doing statistical analysis in MATLAB (or in another platform, such as, STATA) is a plus. We will organize tutorial sessions on MATLAB. Course grade will be based on weekly assignments, writing referee report(s), and class presentation of a research paper.