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Order-to-Trade Ratios and Market Liquidity

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TLDR
In this article, the impact of high order-to-trade ratio (OTR) penalty on the Italian stock market has been investigated and the authors find that the penalty is associated with a collapse in the quoted depth of stocks that make up the bulk of trading in Italian equities and an increase in price impacts of trading across the treated stocks.
Abstract
We study the impact on market liquidity of the introduction of a penalty for high order-to-trade ratios (OTRs), implemented by the Italian stock exchange to curtail high-frequency quote submission. We find that the fee is associated with a collapse in the quoted depth of the stocks that make up the bulk of trading in Italian equities and an increase in price impacts of trading across the treated stocks. Spreads do not change, however. Stocks from a pan-European control sample show no such liquidity changes. Thus, the Italian OTR fee had the effect of making Italian stocks markets more shallow and less resilient. Large stocks are more severely affected than midcaps. We also find evidence of a limited decrease in turnover. Consolidated liquidity, constructed by aggregating across all electronic trading venues for these stocks, decreases just like that on the main exchange. Thus, liquidity was not simply diverted from the main exchange, it was reduced in aggregate.

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Citations
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Journal ArticleDOI

High-Frequency Trading and Price Discovery

Clifford S. Ang
- 01 Apr 2015 - 
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References
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Journal ArticleDOI

Estimating Standard Errors in Finance Panel Data Sets: Comparing Approaches

TL;DR: In this paper, the authors examine the different methods used in the literature and explain when the different standard errors yield the same (and correct) standard errors and when they diverge.
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Simple Formulas for Standard Errors that Cluster by Both Firm and Time

TL;DR: In this article, it is shown that it is easy to calculate standard errors that are robust to simultaneous correlation across both firms and time, and that any statistical package with a clustering command can be used to easily calculate these standard errors.
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TL;DR: Based on within-stock variation, it is found that algorithmic trading and liquidity are positively related and quoted and effective spreads narrow under autoquote and adverse selection declines, indicating that algorithms do causally improve liquidity.
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Robust Inference with Multi-Way Clustering

TL;DR: This article proposed a new variance estimator for OLS as well as for nonlinear estimators such as logit, probit and GMM, that provcides cluster-robust inference when there is two-way or multi-way clustering that is non-nested.
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