Abstract. In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in 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 (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants.

Abstract. The reliability of trade classification algorithms that identify the liquidity demander in financial markets transaction data has been questioned due to an increase in the frequency of quote changes. Hence, this paper proposes a new method. While established algorithms rely on an ad hoc assignment of trades to quotes, the proposed full-information (FI) algorithm actively searches for the quote that matches a trade. The FI algorithm outperforms the existing ones, particularly at low timestamp precision: For data timestamped at seconds misclassification is reduced by half compared to the popular Lee-Ready algorithm. These improvements also carry over into empirical applications such as the estimation of transaction costs. The recently proposed interpolation method and bulk volume classification algorithm do not offer improvements.

Abstract. Since 2018, EU and UK financial markets regulators have been in receipt of data on transactions in debt instruments, such as corporate bonds, reported under the Markets in Financial Instrument Regulation. The data gives regulators a more detailed and broader view of trading in these instruments than previously. Reports submitted under this framework, however, come with a number of unique challenges that require careful consideration. Among those challenges are that reports are not submitted in a completely standardised way, that prices and quantities can be reported in different units, and that reports may be submitted by both counterparties of a transaction. This paper describes an approach for handling these issues for transaction reports on corporate bonds, with the aim of helping to enhance the data quality and supporting robust research into this market.

Abstract. We find that clients with stronger past trading relationships with a dealer receive consistently better prices in corporate bond trading. The top 1% of relationship clients enjoy transaction costs that are 51% lower than those of the median client—an effect which was particularly beneficial when transaction costs spiked during the COVID-19 turmoil. We find clients' liquidity provision to be a key motive why dealers grant relationship discounts: clients to whom balance-sheet constrained dealers can turn as a source of liquidity are rewarded with relationship discounts. Another important motive for dealers to give discounts to relationship clients is because these clients generate the bulk of dealers' profits. Finally, we find no evidence that extraction of information from clients' order flow is related to relationship discounts.

Abstract. Here are three reasons. (a) This paper proves that the popular investor-level herding measure is a biased estimator of herding. Monte Carlo simulations demonstrate that the measure underestimates herding by 20% to 100% of the estimation target. (b) The bias varies with the number of traders active in an asset such that regression type analyses using LSV to understand the causes and consequences of herding are likely to yield inconsistent estimates if controls are not carefully chosen. (c) The measure should be understood purely as a test on binomial overdispersion. However, alternative tests have superior size and power properties.