Algorithmic decision-making plays an important role in financial markets. Current tools in trading focus on popular companies which are discussed in thousands of news items. However, it remains unclear whether methodologies from the field of data analytics relying on large samples can also be applied to small datasets of less popular companies or whether these methodologies lead to the discovery of meaningless patterns resulting in economic losses. We analyze whether the impact of media sentiment on financial markets is influenced by two levels of investor attention and whether this impacts algorithmic decision-making. We find that the influence differs substantially between news and companies with high and low investor attention. We apply a trading simulation to outline the practical consequences of these interrelations for decision support systems. Our results are of high importance for financial market participants, especially for algorithmic traders that consider sentiment for investment decision support.
Information Systems Frontiers, Vol. 23, No. 2, pp. 477-494,
2021
