Working Papers

Research on the Impact of Institutional Investors on Asset Prices

Illustration of the payout-induced trading mechanism - Pfizer announces a dividend. This reveals fundamental information and changes asset prices. The dividend is paid to shareholders with a 45 day lag. The example fund is benchmarked; hence it reinvests the cash into its portfolio. I call these purchases payout-induced trading. Payout-induced trading pushes up Lyft's stock price relative to industry peers. Then, Lyft responds to the higher stock price by increasing investment.

This paper shows that when firms pay dividends, repurchase shares, or are acquired, institutional shareholders preferentially invest the cash proceeds into their existing portfolios, creating price pressure spillover effects of firm payouts on other stocks held in the same portfolios of financial institutions. This price pressure effect identifies an asset demand elasticity of 1.25. Using payout-induced trading as an instrument for stock returns, I document a releveraging market feedback effect on investment, where firms respond to an exogenous stock price increase by issuing debt and use the funds to invest.

The price impact of mutual fund flow-induced fire sales - this heatmap shows the stock return caused by a 1% demand shock generated by extreme surprise flows that are in the top x or bottom y percentile.

I propose a new method to isolate a plausibly exogenous component of mutual fund flows in order to estimate the price impact of fire sales. The method addresses a potential reverse causality problem: instead of mutual fund outflows inducing fire sales, which drive down prices, poor stock returns reduce mutual fund returns, which in turn trigger outflows. The solution is to construct a new instrument from high-frequency surprise flows. Using surprise flows to reexamine important findings in the literature, I find equity markets are deeper and less distortive than suggested.

The effect of HFT on price informativeness - difference in differences analysis with multiple events in event time.

"High-Frequency Trading and Price Informativeness" (with J. Gider and C. Westheide) (SSRN)

We study how the informativeness of stock prices changes with the presence of high-frequency trading (HFT). Our estimate is based on the staggered start of HFT participation in a panel of international exchanges. With HFT presence, market prices are a less reliable predictor of future cash flows and investment, even more so for longer horizons. Further, firm-level idiosyncratic volatility decreases, and the holdings and trades by institutional investors deviate less from the market-capitalization weighted portfolio as a benchmark. Our results document that the informativeness of prices decreases subsequent to the start of HFT. These findings are consistent with theoretical models of HFTs’ ability to anticipate informed order flow, resulting in decreased incentives to acquire fundamental information.

Research on Asset Pricing Anomalies

The anomaly network - two anomalies are connected if they form an interaction strategy that generates statistically significant (correcting for multiple testing) alpha beyond standard asset pricing factors and the factors corresponding to the underlying individual anomalies.

"Interacting Anomalies" (with K. Müller) (SSRN) (more results and data available on the project website)

An extensive literature studies interactions of stock market anomalies using double-sorted portfolios. But given hundreds of known candidate anomalies, examining selected interactions is subject to a data mining critique. In this paper, we conduct a comprehensive analysis of all possible double-sorted portfolios constructed from 102 underlying anomalies. We find hundreds of statistically significant anomaly interactions, even after accounting for multiple hypothesis testing. An out-of-sample trading strategy based on double-sorted portfolios performs on par with state-of-the-art machine learning strategies, suggesting that simple combinations of characteristics can capture a similar amount of variation in expected returns.

Work in Progress

"Cryptocurrencies, Return Chasing, and Return Predictability," with S. Nagel and Z. Wang

We combine the full information from the BTC blockchain with addresses of real entities and address clustering algorithms to construct a partially deanonymized BTC transaction history. We find that unsophisticated investors chase past BTC returns and that BTC purchases of sophisticated investors predict BTC returns.

"Machine Learning Institutional Trading and Return Predictability"

I train machine learning models to predict how financial institutions trade and use the predictions to construct expected excess demand. A long-short, front-running trading strategy exploiting this signal generates significant excess returns.

"Predicting Financial Crises: a Comprehensive Assessment," with L. Liu and K. Müller

We provide a comprehensive evaluation of which variables are reliable predictors of financial crises. We evaluate thousands of candidate predictors and a large set of predictive models and find that variables related to credit, external imbalances, and macroeconomic conditions have robust forecasting ability out-of-sample.

"Demand System Asset Pricing and Unconventional Monetary Policy"

I use confidential securities holdings microdata from the Bundesbank to estimate an asset demand system model of the bond market. I use the model in counterfactual experiments to investigate the impact of any asset purchase program on asset prices, the wealth distribution, and risk exposures of institutional investors.