Da Huang

How does passive investing affect active management? A simple model shows and the empirical results demonstrate that as passive investment rises, investors identify the skill of active managers faster, leading unskilled managers to exit the active mutual fund industry. Because unskilled active managers increase noise in stock prices, greater passive investing improves market efficiency as unskilled managers exit. These findings reconcile increasing closet indexing and fund homogenization, which could imply a lack of skill, with the literature that documents that skill exists in the active mutual fund industry.

This paper shows that exchange-traded funds (ETFs) "sample" their indexes, systematically underweighting or omitting illiquid index stocks. As a result, arbitrage activity between the ETF and its index has heterogeneous effects on underlying asset markets. Using an instrumental variables approach, we find that the trading activity of ETFs reduces liquidity and price efficiency and increases volatility and co-movement for liquid stocks, but has no effect on illiquid stocks. Our results demonstrate that the effects of passive investing on asset markets depend on how passive funds replicate their target index.

We show that property tax limitations reduce the inherent pro-cyclicality of property taxes and expose households to greater risk of housing distress. We develop a novel measure of tax policy risk using an Arrow-Debreu framework to price property tax regimes' consumption smoothing features and obtain simulated measures of risk that capture all of the key characteristics of states' property tax systems. Using parcel-level data for all U.S. residential properties and a state-border discontinuity design, we show that a one standard deviation increase in tax policy risk increases the probability of mortgage distress by 0.24 percentage points.

This paper examines how cybersecurity risk in crypto securities affects asset returns. Hackers steal cryptocurrencies by exploiting bugs in the code. We develop a novel measure of ex ante cybersecurity risk by counting bug reports from GitHub, which houses the source code that produces crypto assets. A high cybersecurity risk predicts a lower return: a one standard-deviation increase in cybersecurity risk is associated with a 17 basis point decline in daily return. We interpret cybersecurity as a shock to the production technology of crypto assets. The return predictability from our measure is unique because it does not rely on the assumption of crypto assets' fundamental value, but rather stems from the canonical investment-based asset pricing theory.

Empirical asset pricing literature views illiquid assets as a "bug" because they add noise and bias to analyses of return predictability. In this paper, we show that illiquidity is a feature rather than a bug for option return predictability. Previously well-documented trading strategies with high Sharpe ratio become unprofitable once we purge illiquid options from the analyses. Illiquidity is the source of return predictability. Our results are robust to varies microstructure bias corrections, suggesting thinly-traded and heavily-traded options are fundamentally different. Our results have important implications for future empirical research in option markets.

We develop a method of pricing tax risk using a Arrow-Debreu model combined with a detailed tax model. To demonstrate this method, we build a tax simulation model for property taxes in the United States that captures key features that differ across states and change the risk associated with the property tax. In this context, we conceptualize property tax payments as the payouts of a complex asset that differ across states of the world. We then simply price these payouts using an Arrow-Debreu pricing model. Pricing tax risk in this way has several important applications and is easily implemented.

Does high-frequency trade increase or decrease volatility in financial markets during crises? We introduce a novel intraday volatility measure for ETFs, and find that during the Covid-19 crisis period, the withdrawal of high-frequency trade from large stock ETFs increases intraday ETF volatility net of the fundamental shock from Covid itself by over 30%. The speed of arbitrage activities slows down during the Covid-19 period as high-frequency traders reduce the intensity of their trading. While high frequency traders may serve as de facto market makers during normal times, they withdraw from the market during a crisis, precisely when they are needed most.

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in sample estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard errors. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants.