Abstract
There are numerous studies suggesting that published news stories have an important effect on the direction of the stock market, its volatility, the volume of trades, and the value of individual stocks mentioned in the news. There is even some published research suggesting that automated sentiment analysis of news documents, quarterly reports, blogs and/or Twitter data can be productively used as part of a trading strategy. This paper presents just such a family of trading strategies, and then uses this application to re-examine some of the tacit assumptions behind how sentiment analyzers are generally evaluated, in spite of the contexts of their application. This discrepancy comes at a cost.
Cite
CITATION STYLE
Kazemian, S., Zhao, S., & Penn, G. (2014). Evaluating Sentiment Analysis Evaluation: A Case Study in Securities Trading. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (pp. 119–127). Association for Computational Linguistics (ACL). https://doi.org/10.3115/v1/w14-2620
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