High frequency trading (HFT) activity has increased over recent years. In a recent paper, Rene Carmona and Kevin Webster claimed that about 70 per cent of all the transactions in the US equity market were carried out using this tool in 2010, although it is now thought that the percentage in recent years has declined somewhat to around 50-60 per cent. Nevertheless, given the importance of the phenomenon, the attention of regulators has heightened also in light of the potential negative impact that such trading might have on financial markets.
Regulators have sought to deal with HFT by introducing new rules in, for example, MIFID II in Europe or the FINRA rule on algorithm trading in the US. The regulatory approach to high frequency trading has nonetheless been different across markets and regions also because of the absence of a clear-cut definition and universally recognised measures to identify it.
The purpose of this article is to critically assess the regulatory innovations that have been introduced in the European legislation through the MIFID II, highlighting the strengths and the weaknesses of the current framework. To that end, we first present the different market strategies that could be implemented by investors using high frequency trading algorithms. Then we will focus on the potential threats to financial market stability brought by high frequency trading techniques. Lastly we will present the most recent European regulatory response.
High frequency trading can be defined as an algorithmic trading technique that implies that large volumes of orders are placed automatically at very high speeds. High frequency trading strategies can be divided in four main categories: passive market-making strategy, arbitrage strategy, structural strategy and directional strategy.
- In the passive market-making strategy, traders, who act as market makers, provide liquidity to the markets by submitting non-marketable resting orders; such activity entails for traders to continuously update their bid-ask spreads to reflect the market situation. In this case the main sources of profit are the gains arising from the bid-ask spread and the fees for liquidity provisions services.
- The arbitrage strategy seeks to exploit pricing disparities for the same products traded in different markets. For example a security that is traded in multiple stock exchanges and is trading at different prices.
- The structural strategy attempts to exploit vulnerabilities of the market or of its participants. For example, traders that have better access to information sources can profit by trading in venues that are offering executions at stale prices.
- Finally in the directional strategy, market participants can take long or short positions in anticipation of a price move up or down. One type of directional strategy, for example, is “news trading”, which implies a fast response to the release of new public information in order to generate profits.
Some economists argue that HFT provides potential advantages because of increasing financial market efficiency: i) increased liquidity in financial markets (Henershott, Jones and Menkveld), ii) facilitation of larger trades (Hasbrouk and Saar), ii) improved pricing efficiency Brogaard and Garriott), iv) tighter bid-ask spreads (Hagstromer and Nordén) and v) lower transaction costs (Chlistalla).
At the same time, HFT creates new risks. A major source of risk is the increased speed of shock transmission across different markets, and thus increased level of systemic risk. One example of faster shock transmission is the flash crash. A flash crash is defined as a rapid decline in the price of a security, typically caused by automated trading tools. Even if there is no widespread consensus on the causes of recent flash crash events, most economists believe that a clear example of a flash event triggered by the use of HFT techniques is the shock experienced on 6 May 2010, when there was a sudden fall in the S&P 500 stock index caused by an automated execution algorithm. The index lost almost 9 per cent of its value within a few minutes, only to recover a large part of the loss a short time afterwards. Another example is the Knight Capital case: due to a trading error, the largest trader in U.S. equities lost $460 million in August 2012 and created a major stock market disruption. Also in August 2012, the shares of Peet’s Coffee and Tea rose close to 5 per cent just after the markets opened.
Another important criticism of HFT is that, although it may increase liquidity in normal times, in crisis times liquidity evaporates completely as the algorithms are turned off. And arguably liquidity is important to have in a crisis. This criticism has been proved in a research paper by Kirilenko et al. analysing the case of the Flash Crash of 2010. The authors show that, unlike traditional market makers, high frequency traders did not alter their inventories when faced with the liquidity imbalances.
The new European regulation on the matter, i.e. MIFID II, tackles high frequency trading as a subset of algorithm trading. The first important change that it introduced is that investors, who do not fall within a specific exemption regime, must be authorised by the financial market authorities. Furthermore, investors that use high frequency trading strategies need to store time-sequenced records of their algorithmic trading systems and trading algorithms for at least five years. This new provision, in particular, should curtail a widespread market abuse practice that was used by specialised investors who were sending out orders and withdrawing them immediately after.
Finally, the new regulatory package (in particular a consultation paper issued by the European Securities and Markets Authority – ESMA), lays-out a clearer set of parameters to ensure consistency in the identification of high-frequency trading practices. Through the principles stated in this document, firms using these strategies are identified following two options: i) quantitative thresholds such as the message rates, or ii) the comparison between the median order lifetime and the median lifetime of all the orders in the trading venue; in particular, if the former is lower, then there is evidence of use of high frequency trading practices.
In essence, the new regulatory package increases the transparency requirements for investors implementing automated trading practices. This innovation constitutes an important step forward in the regulation of such practices that up until the introduction of the MIFID II normative were still in a grey area. Yet, there is still some room for improvement. More specifically, the greater transparency requirements alone cannot increase the financial market resilience to a shock caused by misuse of high frequency trading practices. In such a case it would be advisable to sharpen the tools of the supervisory authorities to prevent the occurrence of these situations. In particular, supervisors might strengthen the oversight of financial markets by applying the same tools used in high frequency trading. Through the use of these tools, in fact, supervisors would be better able to detect suspicious trading activities leading to market manipulation practices, among others: i) insider trading, ii) front running, iii) “painting the tape” strategies, iv) fictitious orders, v) wash trading and vi) trader collusion.
- This blog post gives the views of its author(s), not the position of the ECB, Banca d’Italia, LSE Business Review or the London School of Economics.
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Benedict Weller is a principal supervisor in the Single Supervisory Mechanism at the European Central Bank. He was previously principal economist in risk management at the ECB between 2012 – 2016, Senior Expert in TARGET2-Securities between 2009-2011, and prior to that Economist in ECB’s Monetary Policy Operations from 2001-2008, focusing on liquidity and collateral management. Between 1998-2000, he was Editor of Central Banking Journal in London. He has a BA and MA in Economics from Jesus College, Cambridge University.
Michelangelo Bruno is an on-site inspector in the financial supervision department at Banca d’Italia. Previously, he was supervision analyst in the ECB’s Single Supervisory Mechanism between 2015 – 2017 and research and teaching assistant at Bocconi University’s finance department between 2013 – 2014. He holds a MSc in accounting, financial management and control from Bocconi University and a Master’s in political economy from LSE.