Dark trading and price discovery

Comerton‐Forde, Carole; Putniņš, Tālis J.

doi:10.1016/j.jfineco.2015.06.013

Cited by 162 publications

(64 citation statements)

References 62 publications

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“…This result is consistent with findings by Hatheway et al (2016) and Comerton-Forde and Putniņš (2015) who show that orders executing in dark trading venues are predominantly uninformed. As a consequence, adverse selection risk and bid-ask spreads in lit markets increase when the level of dark trading is high.…”

Section: Related Literaturesupporting

confidence: 93%

“…However, while the findings in Conrad et al (2003), Hatheway et al (2016), Comerton-Forde and Putniņš (2015) and Garvey et al (2016) support the predictions the model makes for low levels of adverse selection, the cross-sectional results in Ready (2014) are consistent with the model's implications for high levels of adverse selection. The latter statement also applies to the results reported in Nimalendran and Ray (2014) who present evidence that informed investors split their trades across dark and lit venues.…”

Section: Related Literaturesupporting

confidence: 51%

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Spoilt for Choice: Order Routing Decisions in Fragmented Equity Markets

et al. 2016

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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.

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Section: Related Literaturesupporting

confidence: 93%

Section: Related Literaturesupporting

confidence: 51%

Spoilt for Choice: Order Routing Decisions in Fragmented Equity Markets

et al. 2016

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“…For cap tier 1–200, we use the average bid‐ask spreads for the stocks in the S&P/ASX 200 over the month of December 2013. The estimated bid‐ask spread for cap tier 201–500 is based on our estimate for 1–200 and the estimate for 1–500 obtained from Comerton‐Forde and Putnins (). The total transaction cost is the two‐way turnover for the cap tier multiplied by the sum of the assumed commission (20 bp) and half of the bid‐ask spread – we assume that trade requires on average to cross half of the spread.…”

Section: Resultsmentioning

confidence: 99%

“… The latter figure is based on the Comerton‐Forde and Putnins (), who observed the average bid‐ask spread for the All Ordinaries over the period February 2008 to October 2011 was 129 bp. …”

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confidence: 99%

The Piotroski F‐score: evidence from Australia

Hyde¹

2016

Accounting & Finance

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A market-neutral strategy that is long [short] stocks with a high [low] Piotroski F-score generates an index-weighted 0.8 percent pm on S&P/ASX 200 stocks and 1.4 percent pm on smaller stocks. Equal-weighted returns are higher and in all cases returns are statistically significant. However, the Carhart model alphas are not statistically significant except in the case of equal-weighted small cap portfolios. For such portfolios, however, most of the alpha comes from the short side and most institutional investors would find them uninvestable due to capacity constraints. A range of tests indicate that analyst neglect does not explain the F-score premium.

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“…Information efficiency involves different dimensions, one of which is the timeliness of price adjustment. In this section, I analyze the degree of price discovery measured by a measure of price delay, as applied by Hou and Moskowitz (2005), Comerton-Forde andPutniņš (2015), andCarrion (2013).…”

Section: Robustness Checksmentioning

confidence: 99%

Need for speed: Hard information processing in a high‐frequency world

Zhang

2017

Journal of Futures Markets

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I study the role of high-frequency traders (HFTs) and non-high-frequency traders (nHFTs) in transmitting hard price information from the futures market to the stock market using an index arbitrage strategy. Using intraday transaction data with HFT identification, I find that HFTs process hard information faster and trade on it more aggressively than nHFTs. In terms of liquidity supply, HFTs are better at avoiding adverse selection than nHFTs. Consequently, HFTs enhance the linkage between the futures and stock markets, and significantly contribute to information efficiency in the stock market by reducing the delay between the stock and the futures markets. J E L C L A S S I F I C A T I O N G10, G14 | INTRODUCTIONIn recent years, the volume and complexity of information accessible to participants in financial markets has grown to exceed human information processing capacity. As a result, computer algorithms are now used to process large amounts of information more quickly. One specific group of computer trading algorithms is that used by high-frequency traders (HFTs). HFTs distinguish themselves from other groups of traders through their use of high speed trading and information processing, their high trading volume, as well as their sophisticated algorithms.1 A major concern of regulatory authorities, such as the U.S.Securities and Exchange Commission (SEC) and the U.S. Commodity Futures Trading Commission (CFTC), is the influence of HFTs on market quality and price discovery (cf. the call for comments of the SEC, 2010). HFTs contribute to price discovery through the application of different information processing strategies. Common information processing strategies of HFTs include arbitrage trading strategies. Index arbitrage focuses on mispricings between an index (such as the S&P 500) and its components. It can thus be categorized as "hard" quantitative information processing. To address the regulatory concerns relating to price discovery, I analyze the role of HFTs and non-high-frequency traders (nHFTs) in interpreting "hard" futures price information for index arbitrage strategies, and study the implications for information efficiency. This paper provides an empirical test of hard information processing strategies used by HFTs, specifically index arbitrage strategies between the E-mini futures and the U.S. stock markets.The results show that HFTs use their competitive advantage to react to hard quantitative information shocks faster and more strongly than nHFTs. Specifically, they trade in the direction of hard information shocks within the first few seconds and quickly start selling off their trading position in order to realize their trading profits. This trading behavior translates into HFTs increasing wileyonlinelibrary.com/journal/fut

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Dark trading and price discovery

Cited by 162 publications

References 62 publications

Spoilt for Choice: Order Routing Decisions in Fragmented Equity Markets

Spoilt for Choice: Order Routing Decisions in Fragmented Equity Markets

The Piotroski F‐score: evidence from Australia

Need for speed: Hard information processing in a high‐frequency world

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