Financial Markets Monitoring and Surveillance: A Quote Stuffing Case Study

Abstract

Financial markets are very important components of the world economy whose structure has changed substantially over the last thirty years primarily due to the use of information and communication technologies. Market monitoring and surveillance is an important process to the financial markets in order to ensure that market rules and policies are met as well as to detect any act or attempt of fraudulent manipulation. Recently, a new kind of trading has emerged, High Frequency Trading, which allows traders to place and execute orders within milliseconds via computerized programs. It is still unclear whether existing financial market systems are capable of carrying out effective monitoring and detecting inconsistencies in trades at such high speeds. A strategy that has been made possible in high frequency trading is when very large orders are submitted and withdrawn within milliseconds in an apparent attempt to flood and confuse the market and the competitors, known as “quote stuffing.” This paper presents a high frequency trading analysis of a particular trading scenario and discusses how quote stuffing can affect the function of trading systems. In order to do so, a financial market simulator is developed to model high frequency trading systems and it is used to analyze and demonstrate how quote stuffing can increase the gap of best bid and ask prices between financial markets, contrary to the evidence that high frequency trading has helped to align prices across financial markets. This has some very important implications as traders could profit by artificially creating latencies in trading data feeds and taking advantage of the induced price differences between financial markets. The paper identifies a number of indicators and visualization techniques that demonstrate how efficient control mechanisms could be implemented by a financial market in order to safeguard the behavior of its trading system under a quote stuffing scenario. Moreover, suggestions are put forward on how a real-time engine could be used to detect potential high frequency trading manipulative behaviors.