Job Market Paper

The Natural Experiments: Timeline of the Market Maker Programs - For identification, we use two market maker programs that provide abrupt exogenous variation in MM activity as well as liquidity provision obligations and incentives. In 2018 and 2019, the Brazilian exchange implemented centralized programs assigning 2 to 5 out of 13 DMMs to each included stock. The DMMs are largely HFTs. The programs impose liquidity provision obligations: DMMs have to maintain continuous, two-sided quotes within maximum bid-ask spreads and at minimum lot sizes. In return, DMMs receive incentives to voluntarily provide liquidity: B3 grants DMMs stock-specific trading fee discounts on all trades in their designated stocks. B3 decided program stock selection, obligations, and incentives two months in advance and quasi-randomly conditional on few observable variables.

How can exchanges and regulators improve the liquidity and stability of modern financial markets through liquidity provision obligations and incentives? We exploit two market maker programs as natural experiments using unique message-level trade and quote data from the Brazilian stock exchange that reveal market participants' identities. We find the combination of obligations and incentives improves and stabilizes liquidity which attracts volume and lifts asset prices. In normal times, these positive effects are driven by the program incentives, while tight obligations constrain market makers and decrease market quality. In crises, however, the results flip: stocks with larger incentives experience worse liquidity dry-ups because voluntary liquidity providers withdraw; in contrast, tight obligations mitigate liquidity dry-ups because mandatory intermediaries step in as the liquidity providers of last resort. Finally, which market makers are assigned to which stocks is consequential: market makers' cross-asset hedging behavior causes excess co-movement of returns, liquidity, and volume, highlighting a trade-off between liquidity and excess co-movement. Overall, our results suggest that exchanges and regulators should combine incentives with countercyclical liquidity provision obligations.

Working Papers

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 Karsten Müller (SSRN)

More results and data available on the project website

Revise and Resubmit, Review of Asset Pricing Studies

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 that invests in the top backward-looking double-sort strategy generates equal-weighted (value-weighted) monthly average returns of 4% (2.7%) at an annualized Sharpe ratio of 2 (1.38), on par with state-of-the-art anomaly-based machine learning strategies.

The staggered start of HFT across markets - This graph shows HFT start dates across the markets in our sample.

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.

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.

Illustration of the identification strategy and findings for the case of cash dividends - Demand pressure effects differ from news effects in terms of timing; news change asset prices at announcement, while demand pressure changes asset prices at payment, when financial institutions receive cash and reinvest. I examine returns of the firm itself, peer stocks, and connected stocks around announcement and payment dates at the daily frequency. Dividend firms experience abnormal returns after both the announcement and the payment, as is well-known. Next, I find no evidence for spillovers on industry peers. Finally, connected stocks show payment date price pressure effects but no announcement date news effects.

This paper uses the reinvestment of corporate payouts by financial institutions as a nonfundamental shock to asset prices to estimate the slope of the demand curve for stocks and the real effects of stock returns on corporate financing and investment. Exploiting the separation of announcement and payment at the daily frequency, I find dividends in particular generate payment date price pressure but no announcement date news spillover effects, suggesting that dividend-induced trading is plausibly exogenous to fundamentals. Using dividend-induced trading as a natural experiment for stock returns, I estimate an asset demand elasticity of 1.25 and 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.

Work in Progress

Excess Returns of Portfolios Sorted on Select Home Characteristics - This figure shows percent average excess price appreciation of quintile portfolios sorted on continuous, micro-level property characteristics. The characteristics are sorted with respect to the return of the high portfolio. The sample covers 1996 to 2020.

"Risk and Return in the Residential Real Estate Market," with Mathias Büchner and Bryan Kelly

We examine the cross-section of property-level real estate returns using proprietary data from Zillow containing the universe of US housing transactions from 1996 to 2020. We find many property characteristics predict returns, even after accounting for multiple hypothesis testing. Several signals mirror well-known anomalies in the stock market, such as value, size, momentum, long-term return reversal, and profitability. In addition, there are real estate-specific predictors: home age, the return realized by the last owner, local income per capita, housing supply growth, property taxes, and variables related to idiosyncratic risks. Further, using machine learning techniques, we document that real estate returns are highly predictable. Out-of-sample, houses in the top decile of predicted returns outperform homes in the bottom decile by around 10% annually, even holding location, time, and size constant to account for investors' constraints. Finally, observable factor models fail to price real estate assets. However, instrumented principle component analysis (IPCA) explains this puzzle: a model with two latent risk factors prices the cross-section of housing returns.

Map of Total Climate Risk Aggregated to the County-Level - This figure shows percent expected annual losses due to risks that increase with climate change and directly affect real estate: wild fires, hurricanes, strong wind, coastal flooding, and riverine flooding.

"Real Estate Investing, Climate Risk, and Inequality"

Whose real estate investments are most exposed to climate risk? How do real estate investors respond to climate change news? And how do housing prices react to climate change news? I measure climate risk exposure as the expected annual loss to buildings using census-tract level climate risk data from FEMA. Further, I combine proprietary data from Zillow containing the universe of US real estate transactions from 1996 to 2020 with HMDA data which allows me to observe demographic information of buyers and sellers. I find the top of the wealth distribution is most exposed to climate risk. However, in response to climate change news, the wealthy disproportionately sell exposed homes. These sales are absorbed by firms. Lastly, prices of exposed homes decline after climate change news.

We Track Investors on the Bitcoin Blockchain Using Known Addresses and Clustering Algorithms.

"Cryptocurrencies, Return Chasing, and Return Predictability," with Stefan Nagel and Zachry Wang

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