Hi, I am Sebastian Hillenbrand.

I am an Instructor in the Finance Unit at Harvard Business School. I graduated with a PhD in Finance from NYU Stern in September 2022.

Research Areas: Asset Pricing, Macro-Finance, Monetary Policy

Contact: shillenbrand@hbs.edu

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Working Papers:

WFA Brattle Group Ph.D. Award for Outstanding Research

Abstract: Stock prices aggregate the beliefs of different investors. Using this insight, we estimate the fraction of stock market investors holding survey beliefs. We find that 42% of investors hold beliefs matching those of equity analysts and 25% hold beliefs as observed in individual investor return surveys. Together with risk aversion proxies and rational cash flow forecasts constructed using machine learningtechniques, survey beliefs explain 87% of stock market fluctuations. Because investors likely form their beliefs by extrapolating prices and cash flows, we find stock prices would fluctuate 50% less if all investors held rational beliefs. Allowing for investor heterogeneity and using a price-driven price-to-earnings ratio reconciles prior studies.

Abstract: Stock valuation ratios contain expectations of returns, yet, their performance in predicting returns has been rather dismal. This is because of an omitted variable problem: valuation ratios also contain expectations of cash flow growth. Time-variation in cash flow volatility and a structural shift towards repurchases have magnified this omitted variable problem. We show theoretically and empirically that scaling prices by a forward measure of cash flows can overcome this problem and yields optimal return-predictors. We then construct a new measure of the forward price-earnings ratio for the S&P index based on earnings forecasts using machine learning techniques. The out-of-sample explanatory power for predicting one-year aggregate returns with our forward price-to-earnings ratio ranges from 7% to 11%, thereby beating all other predictors (Welch and Goyal, 2008).

The Fed and the Secular Decline in Interest Rates

MFA Best Doctoral Paper Award, WFA Brattle Group Ph.D. Award for Outstanding Research

Abstract: In this paper I document a striking fact: a narrow window around Fed meetings fully captures the secular decline in U.S. Treasury yields since 1980. By contrast, yield movements outside this window are transitory and wash out over time. This is surprising because the forces behind the secular decline are thought to be independent of monetary policy. However, it is possible that the bond market learns about these forces from the Fed. Two additional facts support this interpretation: (i) long-term yields drop immediately following Fed announcements, and (ii) the Fed’s expectation about the long-run level of the federal funds rate – revealed through the dot plot – has a strong impact on long-term yields. To explain these facts, I present a dynamic term structure model in which the Fed learns from the yield curve and the market learns from Fed meetings. The model rules out alternative explanations such as business cycle information and risk premia. It further implies that the Fed possesses important information about the long-run neutral interest rate. This can explain why Fed announcements have a powerful impact on the valuations of long-lived assets like the stock market.

R&R Journal of Finance

Best Paper Award Muenster Banking Workshop

Abstract: We challenge theories that lead arrangers retain shares of syndicated loans to overcome information asymmetries. Lead arrangers frequently sell their entire loan stake—in over 50 percent of term and 70 percent of institutional loans. These selloffs usually occur days after origination, with lead arrangers retaining no other borrower exposure in 37 percent of selloff cases. Counter to theories, sold loans perform better than retained loans. Our results imply that information asymmetries could be lower than commonly assumed or mitigated by alternative mechanisms such as underwriting risk. We also provide guidance for Dealscan users on how to approximate loan ownership after origination.

Abstract: We document three new facts about nonbank lending in the syndicated loan market. First, lending by nonbanks is about three times as cyclical as lending by banks, even after controlling for borrower demand and loan characteristics. Second, the cyclicality of nonbanks - as opposed to bank health - explains the majority of the decline in originations during both the Great Recession and the COVID-19 crisis. Third, we study the main nonbank investors in the market - CLOs and loan mutual funds. Cyclicality in flows to these institutional investors explains cyclicality in nonbank lending. We provide evidence that time-series variation in the benefit from securitization (i.e., the "CLO arbitrage") and fragility in loan mutual fund redemptions contribute to the cyclicality of nonbanks.

Published Papers:

Journal of International Economics, 129(103418), March 2021

SCI Data Replication

Abstract: We use de-identified data from Facebook to construct a new and publicly available measure of the pairwise social connectedness between 170 countries and 332 European regions. We find that two countries trade more when they are more socially connected, especially for goods where information frictions may be large. The social connections that predict trade in specific products are those between the regions where the product is produced in the exporting country and the regions where it is used in the importing country. Once we control for social connectedness, the estimated effects of geographic distance and country borders on trade decline substantially.