Introduction to corporate finance covering theories and empirical evidence about principal-agent models of firm managerial structure, takeover bids, capital structure, corporate governance; regulation of financial markets; financial markets and institutions with a focus on asymmetric information, transaction costs, or both; dynamic models of market making; and portfolio manager performance evaluation. Pre-requisite: ECO 525.

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 covers a number of advanced topics related to asset management and asset pricing. Topics covered include mean-variance analysis, dynamic optimization, CAPM, APT, market efficiency, active money management, indexing, stock return predictability, bubbles and crashes, mutual fund and professional managers' performance, hedge funds, security analysts, social interaction and investor behavior and fixed income portfolio management.

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.

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.

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 finance as a way to finance the growth of private companies and the acquisition of larger, established businesses as well as techniques used to create value in and monetize private investments. The part entails working with 6 cases. 2. Venture Capital finance as a way to finance and to build new innovative firms.

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 focuses on the two recent periods of intense financial market turbulence: The Great Financial Crisis of 2007-09; and the ongoing coronavirus pandemic. The course evaluates how the financial system fared during these two periods and the policymakers' response. Important questions include: What were the structural weaknesses of the US financial system that led to the severe recession of 2008-09? In the aftermath of the Great Financial Crisis, were these shortcomings adequately addressed? The course explores the state of the US financial system and monetary policy today in light of the experience of the past decade.

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.