The Future of Computer Trading in the Financial Markets: Working Paper

Economic research thus far provides no direct evidence that high frequency computer based trading has increased volatility. However, in specific circumstances, a key type of mechanism can lead to significant instability in financial markets with computer based trading (CBT): self-reinforcing feedback loops (the effect of a small change looping back on itself and triggering a bigger change, which again loops back and so on) within well-intentioned management and control processes can amplify internal risks and lead to undesired interactions and outcomes. The feedback loops can involve risk-management systems, and can be driven by changes in market volume or volatility, by market news, and by delays in distributing reference data. A second cause of instability is social: a process known as normalisation of deviance, where unexpected and risky events come to be seen as ever more normal (e.g. extremely rapid crashes), until a disaster occurs. Overall, liquidity has improved, transaction costs are lower, and market efficiency has not been harmed by computerised trading in regular market conditions. The nature of market making has changed, shifting from designated providers to opportunistic traders. High frequency traders now provide the bulk of liquidity, but their use of limited capital combined with ultra-fast speed creates the potential for periodic illiquidity. Computer –driven portfolio rebalancing and deterministic algorithms create predictability in order flows. This allows greater market efficiency, but also new forms of market manipulation. Technological advances in extracting news will generate more demand for high frequency trading, while increased participation in this will limit its profitability. Ongoing advances in the sophistication of ’robot‘ automated trading technology, and reductions in the cost of that technology, are set to continue for the foreseeable future. Today’s markets involve human traders interacting with large numbers of robot trading systems, yet there is very little scientific understanding of how such markets can behave. For time-critical aspects of automated trading, readily customisable, special-purpose silicon chips offer major increases in speed; where time is less of an issue, remotely-accessed ’cloud‘ computing services, offer even greater reductions in cost. Future trading robots will be able to adapt and learn with little human involvement in their design. Far fewer human traders will be needed in the major financial markets of the future.