Jules van Binsbergen conducts theoretical and empirical research in finance. His current work focuses on asset pricing, in particular the relationship between financial markets and the macro economy, and the organization, skill and performance of financial intermediaries. Some of his recent research focuses on the influence of financial market anomalies on real economic activity, measuring the skill of mutual fund managers and the term structure of cash flow growth and stock return predictability. Professor van Binsbergen’s research has appeared in leading academic journals, such as the American Economic Review, the Journal of Finance, the Journal of Financial Economics and the Journal of Monetary Economics. He received his PhD from the Fuqua School of Business at Duke University. After obtaining his PhD in 2008, he joined the faculty at Stanford’s Graduate School of Business, where he got tenure in 2014. He joined the Wharton School in 2014.
Abstract: The authors summarize the recent literature on mutual fund manager skill and performance. They discuss the latest contributions in the field and reinterpret them through the lens of the rational expectations framework (efficient market hypothesis). They further discuss the importance of (1) the choice of benchmark model and (2) the time-series and cross-sectional sample selected in performance studies. The article has three main conclusions. First, although net alpha is a measure of the abnormal return of an extra dollar invested in a particular fund (i.e., performance), it does not measure mutual fund manager skill. To measure the latter, the product of gross alpha and the size of the fund—value added—is needed. Second, the set of real-time available index funds is the relevant counterfactual to use when assessing the skill and performance of investment managers. Nontradable factors that are constructed with the benefit of hindsight are not a realistic benchmark. Third, the authors can think of no good reason to exclude high-quality mutual fund data either in the cross section or time series when making inferences regarding skill and performance.
Jules van Binsbergen, William Diamond, Marco Grotteria (Working), Risk Free Interest Rates (R&R, Journal of Finance).
Abstract: We document differing risk-free rates in a range of asset classes, providing a uniquely clean measure of segmentation between markets. The asset markets we consider are the government bond market, commodity markets for precious metals, exchange rate markets and option markets. We find that risk-free rates across markets can deviate for prolonged periods of time and we characterize market segmentation through the speed of convergence. We analyze how shocks propagate across rate spreads and develop an aggregate arbitrage index which captures the common variation of these spreads across markets. We further present a novel high-frequency measure of the convenience yield on government bonds, which equals 38 basis points on average and grows substantially during periods of financial distress. We argue that option-market-implied risk-free rates provide a convenience-yield-free and effectively credit-risk-free measure of time preference measured accurately at a minutely frequency. This makes such rates a strong candidate for the risk free benchmark rate and we explore a range of empirical asset pricing applications.
Abstract: We examine the importance of cross-sectional asset pricing anomalies (alphas) for the real economy. We develop a novel quantitative model of the cross-section of firms that features lumpy investment and informational inefficiencies, while yielding distributions in closed form. Our findings indicate that anomalies can cause material real inefficiencies, raising the possibility that agents that help to eliminate them add significant value to the economy. The framework reveals that the magnitude of alphas alone is a poor indicator of real implications, and highlights the importance of alpha persistence, the amount of mispriced capital, and the Tobin's q of firms affected.
Jules van Binsbergen and Jonathan Berk (2017), How Do Investors Compute the Discount Rate? They Use the CAPM, Financial Analyst Journal, 73, pp. 25-32.
Finance 611: Corporate Finance (Core)
This course serves as an introduction to business finance (corporate financial management and investments) for both non-majors and majors preparing for upper-level course work. The primary objective is to provide the framework, concepts, and tools for analyzing financial decisions based on fundamental principles of modern financial theory. The approach is rigorous and analytical. Topics covered include discounted cash flow techniques; corporate capital budgeting and valuation; investment decisions under uncertainty; capital asset pricing; options; and market efficiency. The course will also analyze corporate financial policy, including capital structure, cost of capital, dividend policy, and related issues. Additional topics will differ according to individual instructors.
Independent Study Projects require extensive independent work and a considerable amount of writing. ISP in Finance are intended to give students the opportunity to study a particular topic in Finance in greater depth than is covered in the curriculum. The application for ISP's should outline a plan of study that requires at least as much work as a typical course in the Finance Department that meets twice a week. Applications for FNCE 899 ISP's will not be accepted after the THIRD WEEK OF THE SEMESTER. ISP's must be supervised by a Standing Faculty member of the Finance Department.
This course exposes student to recent development in the asset pricing literature. The starting point for the course is the standard neo-classical rational expectations framework. We will then investigate where this frameworkhas succeeded and where it has not. Recently documented deviations from the framework in the literature are discussed and placed in context. The course will also focus on hypothesis development, recent research methods, and research writing. The ultimate objective is for students to develop their own hyoptheses and research ideas, resulting in a paper.
A new machine-learning model can predict how the prices of stocks will behave based on whether or not analyst forecasts are too optimistic or too pessimistic, says Wharton’s Jules H. van Binsbergen.Knowledge @ Wharton - 2020/10/13