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For example, arbitrage trading requires orders to be identified and executed quickly, as arbitrage opportunities are short-lived in the market. Moreover, trading based on the identification of trends and statistical metrics can be performed faster and identified more precisely by a computer. This growth has been facilitated by technological developments, such as increased computing power, reduced storage costs and the implementation of artificial intelligence and machine learning techniques.

In the banking sector, cost considerations, competitiveness and profitability are key incentives to trade using algorithms.

Four Big Risks of Algorithmic High-Frequency Trading

In recent years, a number of major ALGO trading failures have resulted in substantial losses, fines and reputational damage for credit institutions and investment firms. These risks, and the rapid expansion of ALGO trading, have led to increased attention to the applicable supervisory and regulatory framework.

Similar requirements were also introduced for trading venues. The requirements cover, among other things:. These include minimum obligations for institutions pursuing market-making strategies based on algorithms to provide liquidity to the trading venue where they operate and limitations on the ratio of unexecuted orders to transactions.

In parallel, the mandate of the ECB, in its role as single prudential supervisor for significant credit institutions in the euro area, consists of preserving the safety and soundness of these institutions. For that purpose, the ECB is required to enforce the Capital Requirements Regulation CRR , which imposes capital requirements on all institutions performing trading activities.

These requirements apply to all trading forms, including ALGO trading, and are mainly based on the market risk or volatility of the trading portfolios. Moreover, ALGO trading incidents could also be linked to the prudential definition of operational risk in the CRR because they are often linked to failures of systems and processes. In practice, MiFID requirements also play an important role in reducing operational risks.

Despite the many advantages of ALGO trading, individual system failures resulting in significant losses have highlighted the need for institutions to develop specific risk management capabilities and for regulators and supervisors to monitor the risks that it poses. The new MiFID requirements enforced by national market supervisors will reduce the potential operational risk embedded in this type of trading. Prudential supervisors, for their part, will remain vigilant as to the risk management implications and governance of ALGO trading. The platform reduced latency from six seconds to two milliseconds and made HFQ possible on the TSE for the first time.

The quote-to-trade ratio more than doubled after the launch of Arrowhead.

Algorithmic trading

A number of researchers have investigated the impact of HFQ on market quality measures such as liquidity and cost of trading, but there has been less focus on how HFQ affects systemic risk. Although HFQ can increase volatility, it is not clear whether HFQ affects the severity of losses from the type of episodic illiquidity observed during the Flash Crash of May in the U. In our research, we examine stressful market conditions when systemic risks are most relevant.

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How did the reduced latency of Arrowhead affect systematic trading risks such as shock-propagation risk, quote-stuffing risk, Limit Order Book LOB attrition risk, and tail risk? Our analysis goes beyond the traditional measures of market quality. To quantify the true state of the LOB we compute measures such as the LOB slope and the cost of immediacy COI , which tend to be more stable than National Best Bid and Offer NBBO spreads, and also are more relevant for liquidity demanders with order sizes larger than volume supplied by the best quotes.

With our advanced measures, we also study the distinct effects of low latency on high-frequency quoting versus high-frequency trading.

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Whereas the systemic risks associated with high-frequency trading result from aggressive demand for liquidity, the systemic risks of high-frequency quoting emanate from the cancellation or absence of quotes from liquidity suppliers. The novel findings relate to the risks of HFQ. We show that HFQ made possible by Arrowhead amplifies systemic risk by increasing shock-propagation risk, quote-stuffing risk, LOB attrition risk, and tail risk.

Algorithmic trading: trends and existing regulation

The incidence of extraordinary market-wide volatility in large groups of stocks, such as occurred during the Flash Crash on May 6, , in the U. Regulatory responses to systemically risky events such as flash crashes include a single stock circuit breaker or limits on the movement up or down of a single stock, but they do not explicitly focus on measures of systemic or correlated risks. We find that Arrowhead increases the exposure to systemic risk even more during tail-risk events, which can potentially lead to a highly destabilizing market situation.

One implication of our finding is that low-latency markets may benefit from safety features such as kill switches, circuit breakers, and rigorous software testing, which prevent the proliferation of risks from one stock to another and to the trading system at large.

These effects include increased trading speed, increased volume and number of trades, and increased LOB liquidity.