System Surveillance

Abstract: In recent years, trade activity in stock markets has increased substantially. This is mainly attributed to the development of powerful computers and intranets connecting traders to markets across the globe. The trades have to be carried out almost instantaneously and the systems in place that handle trades are burdened with millions of transactions a day, several thousand a minute. With increasing transactions the time to execute a single trade increases, and this can be seen as an impact on the performance. There is a need to model the performance of these systems and provide forecasts to give a heads up on when a system is expected to be overwhelmed by transactions. This was done in this study, in cooperation with Cinnober Financial Technologies, a firm which provides trading solutions to stock markets. To ensure that the models developed weren‟t biased, the dataset was cleansed, i.e. operational and other transactions were removed, and only valid trade transactions remained. For this purpose, a descriptive analysis of time series along with change point detection and LOESS regression were used. State space model with Kalman Filtering was further used to develop a time varying coefficient model for the performance, and this model was applied to make forecasts. Wavelets were also used to produce forecasts, and besides this high pass filters were used to identify low performance regions. The State space model performed very well to capture the overall trend in performance and produced reliable forecasts. This can be ascribed to the property of Kalman Filter to handle noisy data well. Wavelets on the other hand didn‟t produce reliable forecasts but were more efficient in detecting regions of low performance.