Letter From the Chair
Finance is at the core of making informed business decisions. Columbia GSB’s finance division provides a complete finance training with a carefully integrated core curriculum and over 100 elective courses to train students to manage their own finances as well as for career success in asset management, investment banking, real estate, financial technology firms, management consulting, and for roles in central banks and government.
Taught by award-winning faculty from all areas of finance, our professors bring a combination of research-based insights, theoretical frameworks, and practice-based understanding to the classroom. The curriculum focuses on merging the theory and practice of finance along three dimensions: understanding finance principles, an ability to use state-of-the-art data-analytical tools, and a deep knowledge of financial markets and institutions. The core curriculum provides the foundation, and then expansive electives provide more advanced and concentrated courses in the main areas of finance: investment management, investment banking, private equity, venture capital, and real estate.
Central to Columbia GSB’s finance training are Centers and Programs that curate the curriculum, connect students with alumni and industry, and mentor students along their career journeys. These include the Eugene Lang Entrepreneurship Center, Heilbrunn Center for Graham & Dodd Investing, and Paul Milstein Center for Real Estate, which anchor our training in entrepreneurial finance, value investing, and real estate, respectively. These programs host many conference and events, and offer significant executive education, connecting our alumni and industry practitioners with new developments and insights.
Together, our program has a long track record of producing transformative business leaders, with Warren Buffett and Henry Kravis as two leading examples who’ve revolutionized the asset management and private equity industries. Our goal is to teach and mentor the next generation of business leaders in finance.
Michael Johannes
Ann F. Kaplan Professor of Business
Chair of Finance Division
In the Media
Interview: Omid Malekan, Columbia Business School Professor and Crypto Writer
Bitcoin Was Supposed to Hedge Against Inflation-Here's Why it Hasn't Worked that Way
Materiality, Scope 3 Emissions Elicit Debate in SEC Climate Rule Comments
Research
Privacy and the Value of Data
How does protecting the privacy of consumers affect the value of their personal data? We model an intermediary that uses consumers' data to influence the price set by a seller. When privacy is protected, consumers choose whether to disclose their data to the intermediary. When privacy is not protected, the intermediary can access consumers' data without their consent. We illustrate that protecting consumers' privacy has complex effects. It can increase the value of some consumers' data while decreasing that of others.
Proximity Bias: Motivated Effects of Spatial Distance on Probability Judgments
Welfare Consequences of Sustainable Finance
We model the welfare consequences of portfolio mandates that restrict investors to hold firms with net-zero carbon emissions. To qualify for these mandates, value-maximizing firms have to accumulate decarbonization capital. Qualification lowers a firm’s required rate of return by its decarbonization investments divided by Tobin’s q, i.e., the dividend yield shareholders forgo to address the global-warming externality.
The Value of Data Records
Many e-commerce platforms use buyers' personal data to intermediate their transactions with sellers. How much value do such intermediaries derive from the data record of each single individual? We characterize this value and find that one of its key components is a novel externality between records, which arises when the intermediary pools some records to withhold the information they contain. Ignoring this can significantly bias the evaluations of data records.
Thompson Sampling with Information Relaxation Penalties
We consider a finite-horizon multi-armed bandit (MAB) problem in a Bayesian setting, for which we propose an information relaxation sampling framework. With this framework, we define an intuitive family of control policies that include Thompson sampling (TS) and the Bayesian optimal policy as endpoints. Analogous to TS, which, at each decision epoch pulls an arm that is best with respect to the randomly sampled parameters, our algorithms sample entire future reward realizations and take the corresponding best action.
