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.

This course applies topics from microeconomics (ECO 305) and corporate finance (ECO 318) to study corporate restructuring. Topics include mergers, acquisitions, joint ventures, divestiture and share repurchases. Each of these is discussed in the context of the relevant economic theory, institutional and regulatory environment, and with a focus on shareholder value. Meets concurrently with ECO 464.

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.

Under the direction of a Bendheim affiliated faculty member, students carry out a master's project, write a report, and present the results in the form of a poster or an oral presentation in front of an examining committee.

Financial institutions play an increasingly dominant role in modern finance. This course studies the financial system and its protagonists, with a focus on efficiency and stability. It covers important theoretical concepts and recent developments in asset pricing under asymmetric information, market microstructure, and financial intermediation. Topics include market efficiency, liquidity crises, asset price bubbles, herding, risk management, market design and financial regulation.

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 growth and acquisition of mature private companies (in addition to techniques used to create value in and monetize private company investments) (ii) Venture Capital and its use of funding and managing 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.

Course explores the increasing weight of Asia in global financial 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 and private market investments. Discussions and investment case studies combine analysis of historical trends and 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, and growth 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 to 2009, 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.