How to enable automated trading engines to cope with news-related liquidity shocks? Extracting signals from unstructured data

• We propose a text-mining system that forecasts liquidity after news releases.
• We show how to incorporate financial domain knowledge within knowledge discovery.
• The proposed system is capable to extract signals from unstructured data.
• A novel simulation-based evaluation shows superiority to a benchmark approach.

Financial markets are characterised by high levels of complexity and non-linearity. Information systems have often been applied to support investors by forecasting price changes in securities markets. In addition to the asset price, liquidity represents another financial variable that has a high relevance for investors because it constitutes a main determinant of total transaction costs. Previous research has shown that the level of liquidity is affected by the publication of corporate disclosures. To derive an optimal order execution strategy that minimises the transaction costs, investors as well as automated trading engines must be able to anticipate changes in the available market liquidity. However, there is no research on how to forecast the impact of corporate disclosures on market liquidity. Therefore, we propose an IT artefact that allows automated trading engines to appropriately react to news-related liquidity shocks. The system indicates whether the publication of a regulatory corporate disclosure will be followed by a positive liquidity shock, i.e., lower transaction costs compared to historical levels. Utilising text mining techniques, the content of the corporate disclosures is analysed to generate a trading signal. Furthermore, the trading signal is evaluated within a simulation-based use case that considers English and German corporate disclosures and is shown to be of economic value.