News-based trading strategies
By Stefan Feuerriegel, Helmut Prendinger. 2016
82 citations recorded by the source corpus.
The marvel of markets lies in the fact that dispersed information is instantaneously processed and used to adjust the price of goods, services and assets. Financial markets are particularly efficient when it comes to processing information; such information is typically embedded in textual news that is then interpreted by investors. Quite recently, researchers have started to automatically determine news sentiment in order to explain stock price movements. Interestingly, this so-called news sentiment works fairly well in explaining stock returns. In this paper, we design trading strategies that utilize textual news in order to obtain profits on the basis of novel information entering the market. We thus propose approaches for automated decision-making based on supervised and reinforcement learning. Altogether, we demonstrate how news-based data can be incorporated into an investment system. Financial disclosures are the main source for the decision-making in finance.Sentiment analysis of financial disclosures can provide decision support.We design and compare different strategies for news trading.These can outperform our benchmarks in terms of profits but at the cost of risk.Especially viable approaches are supervised and reinforcement learning.
Subfields: Economics and Econometrics, Finance, Management Science and Operations Research
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Citations in the corpus
- Decision support from financial disclosures with deep neural networks and transfer learning (2017, background): “The text source can come in various forms such as, e. g., headlines of news stories [19], the content of newspaper articles [20, 11, 21, 22, 23] or company-specific disclosures [24, 6].”