Predicting Break-Points in Trading Strategies with Twitter

Abstract

The importance of being able to identify changepoints in financial time series has been stressed by many authors, both for econometric forecasting and for enhancing the performance of trading strategies. Strategies which work well in one type of context may lose money under different circumstances so it is crucial for traders to be able to identify break-points as they occur. Our basic hypothesis is that these breakpoints are usually linked to factors outside the markets such as breaking news or the changes in the socio-political context. The buzz on the internet especially on social networks like Twitter provides an advance indicator of these changes. In this paper we propose a novel approach for identifying micro-breakpoints based on Twitter. In order to measure the impact on trading strategies, of knowing when changes occur, we used a simple genetic algorithm using Forex data as the reference benchmark. It makes a decision every two minutes whether to hold US dollars or euro. We compare its performance to a hybrid algorithm which stops trading and goes through a relearning phase after each Twitter alert. In tests over a 5 month period, the hybrid algorithm performed significantly better than the benchmark algorithm. One unexpected result was the discovery of a wave-like relationship between the time to react to each alert and the performance of the algorithmic trader. We call this the Twitter wave. As our results have only been validated over a 5 month period, longer tests are clearly required but the preliminary results are very promising. If confirmed they open up new perspectives for identifying micro-breakpoints in real-time, for high-frequency trading.