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.

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 gives a broad introduction to the techniques of machine learning, and places those techniques within the context of computational finance. Topics include parametric and non-parametric regression, and supervised learning techniques. Methods covered include regularized linear models in high dimensions (LASSO family), Ensemble methods (Bagging and Boosting), Regression Trees/Random Forests/Boosted Trees, Neural Networks/Deep Learning, Classification methods, Clustering. We also discuss the implementation of dimension reduction techniques, including principal components analysis. Examples are taken from financial models.

The course focuses on two aspects of entrepreneurial finance: 1. Private Equity as a way to fund the growth of private companies and the acquisition of larger, established businesses in addition to techniques used to create value in and monetize private investments. 2. Venture Capital as a source of funding and business expertise in the development of new and innovative companies.

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 China's gradual shift toward a capital market-based financial system, the potential revival of Japanese capital markets, and the development of Indian capital markets.

This course covers innovation in financial services, including recent advances that have yielded novel business models and uses of technology, such as BigTech and decentralized finance (DeFi). It explores the financial service industry's purpose to society and gains from major financial innovations. Concrete examples such as the Great Financial Crisis of 2007-09 and the 'crypto winter' of 2022 are used to show how financial risks build up and motivate policymaker responses. The goal is for students to hone their critical capacity to assess the net benefits that financial innovations can pose to the financial system and 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 microstructure of modern electronic financial markets and high frequency trading strategies. Topics include market structure and optimization techniques used by various market participants, tools for analyzing limit order books at high frequency, and stochastic dynamic optimization strategies for trading with minimal market impact at high and medium frequency. The course makes essential use of high-frequency futures data, accessed using the Kdb+ database language. Graduate credit requires completion of extended and more sophisticated homework assignments.