پول بسیار سریع: معاملات با فرکانس بالا در NASDAQ

This paper provides evidence regarding high-frequency trader (HFT) trading performance, trading costs, and effects on market efficiency using a sample of NASDAQ trades and quotes that directly identifies HFT participation. I find that HFTs engage in successful intra-day market timing, spreads are wider when HFTs provide liquidity and tighter when HFTs take liquidity, and prices incorporate information from order flow and market-wide returns more efficiently on days when HFT participation is high.

High-frequency trading has become a pervasive feature of the equity markets in a relatively short period of time. Estimates of high-frequency trading activity levels vary, but are large. Consistent with this notion, an identified group of high-frequency traders (HFTs) participates in 68.3% of the of dollar trading volume in the sample I study in this paper. The developments in market structure (such as decimalization, REG NMS, and automated electronic limit order books) that have created the circumstances for HFTs to flourish are relatively recent. Our understanding of the impact of high-frequency trading on market quality is in its infancy, partly due to its sudden emergence and, until very recently, the lack of high-quality data. There are widely differing views among market participants, regulators, and the financial media on whether HFTs are beneficial, neutral, or detrimental. The disagreements regarding their impact on market quality partly stem from a lack of consensus on the nature of their trading practices. A common view is that HFTs have taken over the market-making function. Under this scenario, they generally benefit the market by increasing competition to provide liquidity, but there are still concerns that they lack the affirmative obligations that bound traditional market-makers and could cause disruptions by exiting the market at their discretion. HFTs are also thought to engage in high-frequency arbitrage, which may have the beneficial effect of making prices more efficient. The alternate perspective is that the liquidity they provide is unreliable, and is outweighed by disruptive practices they are alleged to employ such as order spoofing, predatory trading, herding, or overloading market infrastructure with excessive messages. I provide evidence on these issues by examining HFT trading and market quality impacts in a sample of NASDAQ trades and quotes that identifies HFT participation. This is the same dataset used in Brogaard (2012) and Brogaard, Hendershott, and Riordan (forthcoming) [BHR (2013) hereafter], but I primarily focus on different research questions and where there is overlap, different empirical strategies are employed that yield additional insights. The first question I address is what are the sources of HFT profitability? I investigate their market timing performance. This is important because it helps characterize their strategies to give insights into their motives for trading, which likely impacts market quality, and also provides evidence on intraday return predictability. My second research question is what trading costs do HFTs face when executing their strategies? This provides additional insights into the sources of their profitability, as well as their decisions on when to supply and demand liquidity. Examining the permanent price impacts of HFT trades also tests theoretical predictions that they impose high adverse selection costs on other traders when demanding liquidity and avoid being adversely selected when providing liquidity. Finally, what impact do HFTs have on market quality? I address this question from the perspective of market efficiency. If HFTs act primarily as liquidity providers or arbitrageurs, we might expect their activity to make prices more efficient, while some of the disruptive strategies they are thought to employ could have the opposite effect. My main findings are as follows. HFT trading performance as measured in a Volume-Weighted Average Price (VWAP) analysis reflects successful market timing, and this performance is surprisingly strong at longer horizons than might be expected. Trading costs are unconditionally very low in this sample, but spreads are wider on trades where HFTs provide liquidity and tighter on trades where HFTs take liquidity, suggesting that HFTs provide liquidity when it is scarce and consume liquidity when plentiful. I investigate theories that HFTs impose higher adverse selection costs on slower traders and face less adverse selection themselves, and find mixed results that are only significant for specific subsamples and trade types. Prices are more efficient on days when HFTs are more active in a given stock, in the sense that it takes less time for stock prices to incorporate information from order flow and market index returns. This result is driven by HFT liquidity-demanding trades. These findings should be interpreted with caution. As discussed in more detail below, the sample does not identify the activity of all HFTs, and contains only NASDAQ continuous trading activity in the sample stocks. The sample stocks are traded in multiple venues, and are presumably traded by the sample HFTs in other venues. Also, the NASDAQ exchange is organized as an electronic limit order book with price and time priority, partial pre-trade transparency,1 post-trade transparency, anonymity, and a maker-taker fee model. It is not clear that any conclusions drawn in this sample will necessarily generalize to markets that are organized differently. These concerns are somewhat mitigated by the facts that the sample contains an economically large amount of trading activity, both in absolute terms and as a share of volume in the sample firms, and the identified HFT firms account for a large share of the observed volume. In addition, although I fail to find evidence of any detrimental effects of HFTs, I can only observe their collective activity and my analysis focuses on their trading and effects aggregated over a variety of market conditions. It is possible that individual HFTs follow disruptive strategies that are hidden by this level of aggregation, or that HFTs collectively have negative impacts in certain market conditions. Nevertheless, this paper should advance our understanding of HFT trading behavior and market quality impacts. The rest of this paper is organized as follows. Section 2 reviews the relevant literature. Section 3 describes the data. Section 4 analyzes HFT trading performance. Section 5 studies trading costs and how they vary with HFT participation. Section 6 presents price efficiency tests. Section 7 concludes.

In this paper, I analyze HFT trading performance, trading costs, and effects on market efficiency using a sample of NASDAQ trades and quotes with HFT participation explicitly identified. HFTs seem to possess intraday market timing ability, and this result is not driven solely by very short-term signals or trading at fleeting prices. The magnitude of their market timing performance suggests that there is economically significant predictability in intraday prices. Trading costs are low in this market, but spreads are wider on trades where HFTs provide liquidity and tighter on trades where HFTs take liquidity. This suggests that HFTs provide liquidity when it is scarce and consume liquidity when it is plentiful. Prices incorporate information from order flow and market-wide returns more efficiently on days when HFT participation is high. This effect is driven by HFT demand-side participation, implying that HFTs improve price efficiency when demanding liquidity. This new evidence can potentially provide guidance to theoretical researchers seeking to model HFT behavior and market quality impacts. For example, the relatively low spreads earned on their liquidity providing trades, their market timing performance, and the large share of their trades that demand liquidity together suggest that one may not want to model HFTs as uniformly following market-making strategies. The HFT intraday market timing results suggest that models where HFTs solely profit from very short-term activities such as trading at fleetingly available prices may be incomplete. It is worth reiterating that my data are limited to NASDAQ continuous trading and my focus is on the collective trading and market quality impacts of the sample HFTs aggregated over a variety of market conditions. These issues and other limitations of this study are discussed in more detail above. Conclusions drawn in this setting may not generalize to other environments, and continued study of these issues is clearly warranted. In particular, HFT trading strategies and impacts on market quality in extreme market conditions are important topics for future research.