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Non-standard errors
(2021)
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.
The equity trading landscape all over the world has changed dramatically in recent years. We have witnessed the advent of new trading venues and significant changes in the market shares of existing ones. We use an extensive panel dataset from the European equity markets to analyze the market shares of five categories of lit and dark trading mechanisms. Market design features, such as minimum tick size, immediacy and anonymity; market conditions, such as liquidity and volatility; and the informational environment have distinct implications for order routing decisions and trading venues' resulting market shares. Furthermore, these implications differ distinctly for small and large trades, probably because traders jointly optimize their trade size and venue choice. Our results both confirm and go beyond current theoretical predictions on trading in fragmented markets.
Advances in technology and several regulatory initiatives have led to the emergence of a competitive but fragmented equity trading landscape in the US and Europe. While these changes have brought about several benefits like reduced transaction costs, regulators and market participants have also raised concerns about the potential adverse effects associated with increased execution complexity and the impact on market quality of new types of venues like dark pools. In this article we review the theoretical and empirical literature examining the economic arguments and motivations underlying market fragmentation, as well as the resulting implications for investors' welfare. We start with the literature that views exchanges as natural monopolies due to presence of network externalities, and then examine studies which challenge this view by focusing on trader heterogeneity and other aspects of the microstructure of equity markets.
We show that "quasi-dark" trading venues, i.e., markets with somewhat non-transparent trading mechanisms, are important parts of modern equity market structure alongside lit markets and dark pools. Using the European MiFID II regulation as a quasi-natural experiment, we find that dark pool bans lead to (i) volume spill-overs into quasi-dark trading mechanisms including periodic auctions and order internalization systems; (ii) little volume returning to transparent public markets; and consequently, (iii) a negligible impact on market liquidity and short-term price efficiency. These results show that quasi-dark markets serve as close substitutes for dark pools and consequently mitigate the effectiveness of dark pool regulation. Our findings highlight the need for a broader approach to transparency regulation in modern markets that takes into consideration the many alternative forms of quasi-dark trading.
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.
A tale of one exchange and two order books : effects of fragmentation in the absence of competition
(2018)
Exchanges nowadays routinely operate multiple, almost identically structured limit order markets for the same security. We study the effects of such fragmentation on market performance using a dynamic model where agents trade strategically across two identically-organized limit order books. We show that fragmented markets, in equilibrium, offer higher welfare to intermediaries at the expense of investors with intrinsic trading motives, and lower liquidity than consolidated markets. Consistent with our theory, we document improvements in liquidity and lower profits for liquidity providers when Euronext, in 2009, consolidated its order ow for stocks traded across two country-specific and identically-organized order books into a single order book. Our results suggest that competition in market design, not fragmentation, drives previously documented improvements in market quality when new trading venues emerge; in the absence of such competition, market fragmentation is harmful.
This paper reconsiders the effect of investor sentiment on stock prices. Using survey-based sentiment indicators from Germany and the US we confirm previous findings of predictability at intermediate time horizons. The main contribution of our paper is that we also analyze the immediate price reaction to the publication of sentiment indicators. We find that the sign of the immediate price reaction is the same as that of the predictability at intermediate time horizons. This is consistent with sentiment being related to mispricing but is inconsistent with the alternative explanation that sentiment indicators provide information about future expected returns. JEL Classification: G12, G14 Keywords: Investor Sentiment , Event Study , Return Predictability
Recent empirical research suggests that measures of investor sentiment have predictive power for future stock returns at intermediate and long horizons. Given that sentiment indicators are widely published, smart investors should exploit the information conveyed by the indicator and thus trigger an immediate market response to the publication of the sentiment indicator. The present paper is the first to empirically analyze whether this immediate response can be identified in the data. We use survey-based sentiment indicators from two countries (Germany and the US). Consistent with previous research we find predictability at intermediate horizons. However, the predictability in the US largely disappears after 1994. Using event study methodology we find that the publication of sentiment indicators affects market returns. The sign of this immediate response is the same as the sign of the intermediate horizon predictability. This is consistent with sentiment being related to mispricing but is inconsistent with the sentiment indicator providing information about future expected returns.
JEL-Classification: G12, G14
Recent empirical research suggests that measures of investor sentiment have predictive power for future stock returns over the intermediate and long term. Given the widespread publication of sentiment indicators, smart investors should trade on the information conveyed by such indicators and thus trigger an immediate market response to their publication. The present paper is the first to empirically analyze whether an immediate response can be identified from the data. We use survey-based sentiment indicators from two countries (Germany and the US). Consistent with previous research we find there is predictability at intermediate time horizons. For the US, however, the predictability all but disappears after 1994. Using event study methodology we find that the publication of sentiment indicators affects market returns. The sign of the immediate response is the same as that of the predictability over the intermediate term. This finding is consistent with the idea that sentiment is related to mispricing, but is inconsistent with the idea that the sentiment indicator provides information about future expected returns.
Many equity markets combine continuous trading and call auctions. Oftentimes designated market makers (DMMs) supply additional liquidity. Whereas prior research has focused on their role in continuous trading, we provide a detailed analysis of their activity in call auctions. Using data from Germany’s Xetra system, we find that DMMs are most active when they can provide the greatest benefits to the market, i.e., in relatively illiquid stocks and at times of elevated volatility. Their trades stabilize prices and they trade profitably.