High-frequency traders and price informativeness during earnings announcements
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High-frequency traders and price informativeness during earnings announcements Nilabhra Bhattacharya 1 & Bidisha Chakrabarty 2
& Xu
(Frank) Wang 2
# Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract High frequency traders (HFTs) account for a significant fraction of the total market volume. Prompted by concerns that HFTs reap unfair advantages over other traders by using super-fast trading technologies, some regulatory proposals aim to curb HFTs’ ultra-low-latency activities. However, research suggests that HFTs also play beneficial roles in financial markets, including liquidity provision as voluntary market makers. Currently, little is known about their role in incorporating firm-specific fundamental information into prices. Employing a novel dataset that identifies trades by HFTs and non-HFTs, we find that earnings response coefficients are larger and abnormal price impact of trades are lower when HFTs trade more following earnings announcements, suggesting that HFTs facilitate efficient assimilation of earnings news. HFTs also enhance the forecasting capabilities of financial analysts. Furthermore, HFT participation increases return synchronicity around earnings announcements when multiple firms in the same industry announce earnings on the same day. The evidence suggests that HFTs help incorporate relevant industry information, and this effect arises from HFTs’ liquidity supplying function. We address the endogenous preference of HFTs for large and liquid stocks by including multiple controls for firm size and liquidity, implementing abnormal or change specification for the price impact tests, and performing pre-treatment placebo tests for all of our analyses. Keywords High frequency trading . Earnings announcements . Earnings response
coefficient . Price impact of trades . Analyst forecast JEL classification D53 . G12 . G14 . M41
* Bidisha Chakrabarty [email protected] Extended author information available on the last page of the article
N. Bhattacharya et al.
1 Introduction The last decade and a half have witnessed the emergence of a new class of traders, who use sophisticated machine-deployed algorithms, ultra-fast connections, and dedicated servers colocated with those of the stock exchanges, all with the intent to exploit even the smallest of trading opportunities. These traders, dubbed high frequency traders (HFTs), account for a significant portion of the market trading volume (Brogaard et al. 2014). Although early work focused mainly on HFTs’ efforts to minimize latency (Hasbrouck and Saar 2013), recent research documents that these traders play various other roles as well. For example, they act as market makers, supplying liquidity to institutional traders (Malinova and Park 2015), affect competition among trading venues by performing statistical arbitrage (Boehmer et al. 2018), foster an arms race in speed (Budish et al. 2015), and yet provide positive externalities to non-HFTs in the form of improved effective and realized spreads (Brogaard and Garriott 2
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