Past courses - 2022

The course focuses on econometric programming in Stata. The course introduces students to fundamental and advanced programming techniques using Stata. Applications to real datasets will be covered with the goal of identifying causal relations in empirical corporate finance. The course will cover panel data, diff-in-diff estimators, randomized inference as well as an introduction to machine learning among other things.

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The primary topics of the course concern the interaction between the firm’s real decisions and its financial decisions in different equilibrium industrial organization settings. This course involves a fair amount of microeconomics and industrial organization in addition to finance. Both theoretical articles and empirical articles will be covered and discussed. We will emphasize the link between theoretical and empirical research in both industrial organization and corporate finance. Moreover, the course will deal with big data and computational linguistics

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This course offers a doctoral level introduction to “Investor Attention and Financial Markets” by providing an overview of cutting-edge research on the effects of investor attention on investment and corporate decisions, valuations, as well as stock returns.

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This course will cover research in household finance. We will discuss results and methods at the frontier of this field of finance and economics. We will consider several important economic decisions made by households concerning investments, leverage, consumption, and labor. We will examine how households approach these important decisions, what errors they might make in doing so, and which frictions they face when attempting to solve these problems.

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Admittance is competitive. To apply PhD students should send an email with CV and a brief recommendation letter (either by supervisor or responsible of PhD program) by May 8 at paul.ehling (@) bi.no.

Behavioral Finance is an approach to the analysis of asset prices, corporate finance, and financial decision-making in general, that relaxes some key assumptions of neoclassical financial economics. Namely, it considers a larger class of objective functions, not always compatible with expected utility. Second, it entertains the possibility that expectations are sometimes biased, and not updated in a Bayesian manner. Third, it considers how various financial market frictions can form strong 'limits to arbitrage'. This course is designed to introduce doctoral students to these concepts, and to support them in developing behavioral research ideas of their own. 

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The course gives the student a fundamental understanding of both elementary and advanced statistical and machine learning methods used in finance, with a focus on applications and research methods. The course gives the students a thorough introduction to topics such as ridge regression, the Lasso, Adaptive Lasso, Bridge regression, Regression Trees, Random Forest, and Neural Networks. The course gives a thorough understanding of methods such as the ordinary least squares, its shortcomings, and how, for example, machine learning can improve the estimates and their errors. Machine learning typically prescribes a vast collection of high-dimensional models attempting to predict quantities of interest while imposing regularization methods.

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This PhD mini course exposes students to recent developments in the literature on the theory and management of hedge funds. It is intended for doctoral students in finance and related fields. Topics include hedge fund trading strategies, compensation, performance evaluation, liquidity management, operational risk, and the role of arbitrageurs in capital markets.