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Against the backdrop of COVID-19, we study how the interactions of mutual funds and dealers introduce fragility to the municipal bond market and induce lasting market impacts. During the crisis, trading surges, whereas dealers' liquidity provision plunges for mutual-fund-held bonds, leading to greater price depressions in these bonds. Importantly, the crisis reshapes the market's perceptions of mutual fund fragility risks, with the aftermath-yield spreads widening significantly more for bonds with greater mutual fund exposures. Such postcrisis pricing effects reflect dealers' continued reluctance to provide liquidity for mutual fund–held bonds, and they are stronger for bonds whose mutual fund holders are more susceptible to investor runs. This paper was accepted by Victoria Ivashina, finance. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.4912. [ABSTRACT FROM AUTHOR]

Execution traders know that market impact greatly depends on whether their orders lean with or against the market. We introduce the OEH model, which incorporates this fact when determining the optimal trading horizon for an order, an input required by many sophisticated execution strategies. This model exploits the trader's private information about her trade's side and size, and how it will shift the prevailing order flow. From a theoretical perspective, OEH explains why market participants may rationally 'dump' their orders in an increasingly illiquid market. We argue that trade side and order imbalance are key variables needed for modeling market impact functions, and their dismissal may be the reason behind the apparent disagreement in the literature regarding the functional form of the market impact function. We show that in terms of its information ratio OEH performs better than participation rate schemes and VWAP strategies. Our backtests suggest that OEH contributes substantial 'execution alpha' for a wide variety of futures contracts. An implementation of OEH is provided in Python language. [ABSTRACT FROM AUTHOR]

Abstract: Andersen and Bondarenko's paper “VPIN and the Flash Crash” is essentially a comment on our 2011 Journal of Portfolio Management paper using our measure of order toxicity, VPIN. Andersen and Bondarenko dispute our empirical findings and argue that VPIN essentially does not work. This is incorrect, and is refuted by results in AB and by independent research. Far from “replicating” our results, AB attack a methodology we do not advocate, an analysis we never performed, and conclusions we did not draw. Our note here makes clear why microstructure features play an important role in understanding price dynamics. [Copyright &y& Elsevier]

We propose a dynamic econometric microstructure model of trading and we investigate how the dynamics of trades and trade composition interact with the evolution of market liquidity, market depth, and order flow. We estimate a bivariate generalized autoregressive intensity process for the arrival rates of informed and uninformed trades for 16 actively traded stocks over 15 years of transaction data. Our results show that both informed and uninformed trades are highly persistent, but that the uninformed arrival forecasts respond negatively to past forecasts of the informed intensity. Our estimation generates daily conditional arrival rates of informed and uninformed trades, which we use to construct forecasts of the probability of information-based trade (PIN). These forecasts are used in turn to forecast market liquidity as measured by bid-ask spreads and the price impact of orders. We observe that PINs vary across assets and over time, and most importantly that they are correlated across assets. Our analysis shows that one principal component explains much of the daily variation in PINs and that this systemic liquidity factor may be important for asset pricing. We also find that PINs tend to rise before earnings announcement days and decline afterwards. [ABSTRACT FROM AUTHOR]