Speculatively Accelerated Capital

High-Frequency-Trading

Is high frequency trading good or bad? A reasonable answer must differentiate. Various strategies can be classified as high frequency; each needs to be considered separately before issuing a general verdict.

First, one should distinguish passive and active high frequency strategies. Passive strategies engage in non-designated market making by submitting resting orders. Profits come from earning the bid-ask spread and liquidity rebates offered by exchanges. Active strategies involve the submission of marketable orders. Their profit often directly translates into somebody else’s loss. Consequently, they have raised more (and eloquent) suspicion (including FLASH BOYS by Michael Lewis). Active strategies typically exploit short-term predictability of asset prices. This is particularly evident in order anticipation strategies, which

ascertain the existence of large buyers or sellers in the marketplace and then trade ahead of these buyers or sellers in anticipation that their large orders will move market prices (Securities and Exchange Commission, 2014, p. 8).

Hirschey demonstrates that high frequency traders indeed anticipate large orders with the help of complex algorithms. Large orders are submitted by institutional investors for various reasons. New information (or misinformation) on the fundamental asset value is one of them. Others include inventory management, margin calls, or the activation of stop-loss limits.

Even in the absence of order anticipation strategies, large orders are subject to execution shortfall, i.e. the liquidation value falls short of the mark-to-market value. Execution shortfall is explained in the literature as a consequence of information asymmetry (Glosten and Milgrom) and risk aversion among market makers (Ho and Stoll).

Institutional investors seek to achieve optimal execution (i.e. minimize execution shortfall and trading costs) with the help of execution algorithms. These algorithms, e.g. the popular VWAP (volume weighted average price), are typically based on the observation that price impact depends on the relative volume of an order: Price impact is lower when markets are busy. When high frequency traders detect such an execution algorithm, they obtain information on future trades and can earn significant profits with an order anticipation strategy.

That such order anticipation strategies have been described as aggressive, predatory  and “algo-sniffing” (MacKenzie) suggests that the Securities and Exchange Commission is not alone in suspecting that they “may present serious problems in today’s market structure”. But which problems exactly? There is little doubt that order anticipation strategies increase the execution shortfall of large orders. This is bad news for institutional investors. But, to put it bluntly, “the money isn’t gone, it’s just somewhere else”. The important question is whether order anticipation strategies decrease market quality.

Papers on the relationship between high frequency trading and market quality have identified two issues where the influence of high frequency trading remains inconclusive:

• How do high frequency traders influence market efficiency under normal market conditions?

An important determinant of market efficiency is volatility. Zhang and Riordan finds that high frequency traders increase volatility, Hasbrouck and Saar finds the opposite. Benos and Sagade point out that intraday volatility is “good” when it is the result of price discovery, but “excessive” noise otherwise. They study high frequency trading in four British stocks, finding that high frequency traders participate in 27% of all trading volume and that active high frequency traders in particular “can significantly amplify both price discovery and noise”, but “have higher ratios of information-to-noise contribution than all other traders”.

• Do high frequency traders increase the risk of financial breakdowns? Bershova and Rakhlin echo concerns that liquidity provided by (passive) high frequency traders could be

fictitious; although such liquidity is plentiful during ‘normal’ market conditions, it disappears at the first sign of trouble

and that high frequency trading

has increasingly shifted market liquidity toward a smaller subset of the investable universe […]. Ultimately, this […] contributes to higher short-term correlations across the entire market.

Thus, high frequency trading may be beneficial most of the time, but dangerous when markets are under pressure. The sociologist Donald MacKenzie agrees, arguing that high frequency trading leaves no time to react appropriately when something goes wrong. This became apparent during the 2010 Flash Crash. When high frequency traders trade ahead of large orders in their model of price impact, they cause price overshooting. This can lead to a domino effect by activating stop-loss limits of other traders, resulting in new large orders that cause even greater price overshooting, etc. Empirically, however, the frequency of market breakdowns was significantly lower during 2007-2013 than during 1993-2006, when high frequency trading was less prevalent.

Even with high-quality data, empirical studies cannot fully entangle different strategies employed by high frequency traders, but what is required instead is an integration of high frequency trading into a mathematical model of optimal execution. It features transient price impact, heterogeneous transaction costs and strategic interaction between an arbitrary number of traders. High frequency traders may decrease the price deviation caused by a large order, and thus reduce the risk of domino effects in the wake of large institutional trades….

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