Job Market Paper
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.
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.
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.
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 more elastic and less distortive than suggested.
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
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.
"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 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.