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

Abstract

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

The Ambivalent Role of High-Frequency Trading in Turbulent Market Periods

“…In this sense, HFTs play a stabilizing role in such periods as they tend to balance both sides of the market. This is consistent with other studies (Brogaard et al, 2014;Zhang, 2017)…”

Asymmetric news responses of high‐frequency and non‐high‐frequency traders

“…The decrease in cumulative net trading indicates that traders reduce their trading positions within 10 min after news arrivals to realize trading profits. Similar behavior has been observed on a higher‐frequency level by Zhang ().…”

“…(), Hautsch et al. (), and Zhang (). Although this can be either due to better market monitoring abilities or smaller order and trade sizes, Hautsch et al.…”

“…() predict that stocks with higher news informativeness can be more liquid despite the attraction of informed HFTs. Other studies on HFT and price discovery show positive effects of HFTs on price discovery and efficiency in general (see Brogaard, Hendershott, & Riordan, ; Chaboud, Chiquoine, Hjalmarsson, & Vega, ; Zhang, , among others) and positive effects around macroeconomic news (cf. Jiang, Lo, & Valente, ; Scholtus, Van Dijk, & Frijns, ).…”

Is Trading Fast Dangerous?

“… See Hendershott and Riordan (2013) orZhang (2017) for evidence that HFTs use future prices as a source of information.7 This scenario is predicted by models of informed trading, such asGlosten (1994); see van Kervel (2015) for evidence.…”