events come to be seen as ever more normal (for example,
extremely rapid crashes), until a disastrous failure occurs.
4 Financial stability and
computer-based trading
4.1 Introduction
As described in Chapter 11 a broad interpretation of computer-based trading (CBT) is used in this report. A useful taxonomy of CBT identifies four characteristics which can be used to classify CBT systems. First, CBT systems can trade on an agency basis (i.e. attempting to get the best possible execution of trades on behalf of clients) or a proprietary basis (i.e. trading using one’s own capital). Second, CBT systems may adopt liquidity-consuming (aggressive) or liquidity-supplying (passive) trading styles. Third, they may be classified as engaging in either uninformed or informed trading and fourth, a CBT algorithm generates the trading strategy or only implements a decision taken by another market participant.
Much of the current public debate is concerned with the class of aggressive predatory algorithms, especially those that operate at high speed and with high frequency. Because most financial institutions’ use of CBT cannot be neatly assigned to only one of the above four categories, it is more fruitful to think about CBT systems, the algorithms they employ directly and the frequency at which they trade, rather than to think about the behaviour of a named financial or trading corporation, such as a specific investment bank or fund management company. For much the same reasons, the focus of the discussion in this chapter is not on any one asset class (such as equities, foreign exchange (FX), commodities or government bonds), but rather on the forces that seem likely to shape future issues of stability arising from CBT. This chapter summarises the intuition behind some of the more economically plausible risk factors of CBT: these ‘risk drivers’ can best be viewed as forming the logical basis of possible future scenarios concerning the stability of the financial markets. There is no agreed definition of ‘systemic stability’ and ‘systemic risk’, and the reader is referred to DR292 for a discussion and a survey on empirical systemic risk measures.
4.2 How has computer-based trading affected financial stability in the past?
The raison d’être for financial markets is to aggregate myriad individual decisions and to facilitate an efficient allocation of resources in both primary and secondary markets3 by enabling a timely and reliable reaping of mutual gains from trade, as well as allowing investors to diversify their holdings. As with many other aspects of modern life, innovations in technology and in finance allow the repetitive and numerically intensive tasks to be increasingly automated and delegated to computers. Automation, and the resulting gains in efficiency and time, can lead to benefits but can lead also to private and social costs. The focus of this chapter is solely on possible repercussions of CBT (including high frequency trading (HFT) in particular) on financial stability, especially the risks of instability. This should certainly not be construed as meaning that CBT is socially detrimental or bears only downside risks and costs. It is hoped that by better understanding the risk-drivers of CBT on financial stability, the creators, users and regulators of CBT systems may be able to manage the risks and allow the benefits of CBT to emerge while reducing the social costs.
The conclusions of this chapter apply to any given market structure, but they are especially relevant to the continuous double auctions of the electronic limit order book that run on traders’ screens in most of the major financial markets worldwide. The reason is that even if daily volume is large, the second- by-second volume may not be. Even in a huge market such as the FX market, a sufficiently large order
1 See Box 1.1 in Chapter 1, and also DR5 (Annex D refers).
2 DR29 (Annex D refers).
3 When a company issues equities (shares) to raise capital, this is the primary market in action. When the shares are then
can temporarily sway prices, depending on how many other orders are in the market (the ‘depth’ of the market) at the time and how quickly the book is replenished (the ‘resilience’ of the market). Price volatility is a fundamental measure useful in characterising financial stability, since wildly volatile prices are a possible indicator of instabilities in the market and may discourage liquidity provision4. In DR1, Linton notes that fundamental volatility has decreased in the UK equities market since the turmoil of 2008/2009, and liquidity and trading volume have slowly returned5. If HFT contributes to volatility, Linton argues, it might be expected that the ratio of intraday volatility to overnight volatility would have increased as HFT became more commonplace, but they find no evidence to support that hypothesis. They note that the frequency of large intraday price moves was high during the crisis period, but the frequency has declined to more normal levels since the end of 2009. However, Boehmer et al. (2012)6 in a study spanning 39 exchanges in 36 countries have found that higher volatility and CBT activity move together over the period 2001 to 2009, though causality is not yet clear and the economic magnitudes appear to be small.
4 Stability can, of course, differ from volatility by placing significantly greater weight on large, infrequent price changes, especially
if the latter do not appear to be fundamental.
5 See Figure 4.1 for a time series of realised volatility computed as (high-low)/low; the implied volatility index VFTSE follows a
similar pattern.