Order anticipation around predictable trades

Abstract

I study the presence of order anticipation strategies by examining predictable patterns in large order trades. I construct three simple signals based on child‐order execution patterns and find empirical evidence that stronger signals are correlated with higher execution costs. I use the SEC's (Securities and Exchange Commission's) ban on unfiltered access and increase in noise trading as shocks to order anticipatory activities of algorithmic traders and find that the price impact of predictability is smaller when order anticipation becomes difficult. The empirical findings are mostly consistent with the back‐running theory that predicts delayed price impact as strategic traders learn about large orders gradually.

Broker Colocation and the Execution Costs of Customer and Proprietary Orders

“…Insert Figure 2 about here Altogether, the figure and the statistical tests imply that there is more periodicity in the order submission of agency versus proprietary colocated orders. The presence of such predictable patterns in order submissions in calendar time, which has also been observed in earlier studies (Brugler, 2015;Saglam, 2020), might contribute to poor execution performance, as such patterns can be anticipated by HFTs engaging in back-running behavior. For instance, Saglam (2020) finds that the use of time-and volume-based execution algorithms leads to predictable patterns in order flow, which are correlated with higher execution costs.…”

“…We furthermore find a higher degree of periodicity in colocated agency as compared to proprietary orders, which is consistent with a more prevalent use of timerbased algorithms for agency trades. Brugler (2015) and Saglam (2020) also provide evidence of calendar-time periodicity in trading activity, which in Saglam (2020) is associated with poor execution quality. Finally, the individual child order executions of proprietary orders experience better execution quality when measured in terms of effective spreads and price impacts in comparison to observationally similar agency orders.…”

“…Van Kervel and Menkveld (2019) find that, in the Swedish equity market, HFTs provide liquidity early-on during institutional trades but later turn to trade in the same direction. Saglam (2020) observes that execution algorithms such as the Volume-Weighted Average Price (VWAP) can lead to predictable patterns in order flow that can be picked up by HFTs. Putniņš and Barbara (2016) find that, in the Australian equity market, the effects of HFT firms on institutional transaction costs differ in the cross-section of HFT firms, with some affecting them positively and others negatively.…”

Institutional Order Handling and Broker-Affiliated Trading Venues

“…Hendershott, Jones and Menkveld (2011), Brogaard, Hendershott and Riordan (2014), Brogaard, Hagstromer, Norden and Riordan (2015) and Boehmer, Li, and Saar (2018) find that HFTs are associated with improved market quality and price efficiency. However, a recent literature links the market impact of institutional orders to HFT "back-runners" who detect order flow "footprints" and trade ahead or alongside the institutional investor (see Yang and Zhu (2017) for theory and Kirilenko, Kyle, Samadi and Tuzun (2017), Van Kervel and Menkveld (2018), Saglam (2018), and Korajczyk and Murphy (2018) for empirical evidence). In this context, sub-optimal routing by a conflicted broker to an affiliated ATS could over expose the order and increase the price impact of institutional trades.…”

Periodicity of trading activity in foreign exchange markets

“…Similarly, the observed periodicity may stem from proprietary algorithms attributable to astute traders (e.g., hedge funds) for several reasons. First, smart algorithms can harness the order anticipation strategy that exploits the predictability of order flows from agency counterparts (Sağlam, 2020). Second, given the liquidity demand from agency algorithms surrounding a round‐time mark, sophisticated traders are incentivized to compete with designated dealers by implementing a passive market‐making strategy (Kirilenko & Lo, 2013).…”