Introduction to research in empirical corporate finance/applied microeconomics. The course covers theorical and empirical papers on various topics in corporate finance and related fields, with a focus on assessing the empirical execution and empirical methods. The objective of the course is to prepare graduate students to be able to implement sophisticated empirical methods and write state-of-the-art research papers on topics using methods from applied microeconomics. The course is most useful for PhD students in their second year of course work.

Advanced asset pricing and corporate finance including a selection from: models of financial crises and bubbles; interaction between finance and macroeconomics, derivative pricing in incomplete markets; tests of asset pricing models and associated anomalies; models of investor behavior; financial econometrics, including tests of asset pricing models and methods for high frequency data. Pre-requisites: ECO 525 and 526 (526 may be taken concurrently).

Major topics in modern corporate finance. We study investment policy (investment decision rules, project valuation, cost of capital) and financial policy (mostly capital structure decisions). Additional topics (private equity, bankruptcy and reorganization, merger and acquisitions) are covered if time permits.

A derivative is a financial instrument whose value depends on the value of other financial assets. Derivatives are actively traded on financial markets and are used by many firms to hedge financial and non-financial risks. Examples of derivatives include options, futures, interest rate and commodity derivatives. The course combines mathematical models for pricing and hedging derivatives with practical applications. This course is technical by nature, and makes extensive use of calculus, statistics, and spreadsheets.

In this course, students carry out research advised by an affiliated faculty member of the BCF. The objective is to study a problem in finance or economics, with initial data collection and its subsequent analysis including the implementation of methods potentially useful to solve the problem. Students should write a final report that will be graded by the advisor.

Traditional economics and finance typically use the simple "rational actor" model, where people perfectly maximize, and efficient financial markets. We will present models that are psychologically more realistic than this standard model. About 30% of the course will be devoted to economics, 70% to finance. Applications to economics will include decision theory, happiness, fairness, and neuroeconomics. Applications to finance will include theory and evidence on investor psychology, predictability of the stock market and other markets, limits to arbitrage, bubbles and crashes, experimental finance, and behavioral corporate finance

The course is a broad introduction to the techniques of machine learning in context of quantitative finance. Topics include parametric & non-parametric regression, supervised learning and natural language processing & image recognition techniques from computer science to collect new insights from unstructured text & image data. Methods covered include regularized linear models in high dimensions (LASSO family) dimension reduction techniques, Ensemble methods (Bagging & Boosting) Regression Trees/Random Forests/Boosted Trees, Neural Networks/Deep Learning, Classification methods, Clustering & text analysis. Examples take from financial models.

The course focuses on two aspects of entrepreneurial finance: (i) Private Equity and its use in funding the acquisition and growth of mature private companies (in addition to techniques used to create value in and monetize private company investments); (ii) Venture Capital and its use in managing and funding the development of new and innovative companies (in addition to techniques used in determining company valuations, negotiating term sheets and assessing strategic and financial options). This course is taught concurrently with ECO 461.

This course explores the increased weight of Asia in global markets and its implications. It frames the discussion in the context of the globalization of financial markets, with emphasis on concepts of economic development, institutional reform of markets, and public & private market investments. Discussions and Investment case studies combine analysis of historical trends & recent data with insights from practical experience in Asian markets. Course considers the constraints in China's shift toward a capital market-based financial system, potential revival of the Japanese corporate sector, & the expansion of Indian capital markets.

This political-economy course delves into the factors that contribute to financial crises, including financial innovations. Students study concrete examples from both traditional finance (TradFi) and decentralized finance (DeFi) -- such as the Great Financial Crisis of 2007-09, the "Crypto Winter" of 2022 and the failure of several mid-sized US banks in 2023 (e.g., Silicon Valley Bank) - in order to show how risk builds and crystalizes. The goal is to hone your critical capacity to identify risks to the financial system and assess regulatory responses.

Econometric and statistical methods as applied to finance. Topics include: Asset returns and efficient markets, linear time series and dynamics of returns, volatility models, multivariate time series, efficient portolios and CAPM, multifactor pricing models, portfolio allocation and risk assessment, intertemporal equilibrium models, present value models, simulation methods for financial derivatives, econometrics of continuous time finance.

The course covers the pricing and hedging of advanced derivatives, including topics such as exotic options, greeks, interest rate and credit derivatives, as well as risk management. The course further covers basics of stochastic calculus necessary for finance. Designed for Masters students.

An introduction to the theory and practice of high frequency trading in modern electronic financial markets. We give an overview of the institutional landscape and basic empirical features of modern equity, futures, and fixed income markets. We discuss theoretical models for market making and price formation. Then we dig into detailed empirical aspects of market microstructure and how these can be used to construct effective trading strategies. Course work is a mixture of theoretical and data-driven problems. Programming environment is a mixture of the R statistical environment, with the Kdb database language.