Abstract
Sentiment analysis is a content-analytic investigative framework for researchers, traders and the general public involved in financial markets. This analysis is based on carefully sourced and elaborately constructed proxies for market sentiment and has emerged as a basis for analysing movements in stock prices and the associated traded volume. This approach is particularly helpful just before and after the onset of market volatility. We use an autoregressive framework for predicting the overall changes in stock prices by using investor sentiment together with lagged variables of prices and trading volumes. The case study we use is a small market index (Danish Stock Exchange Index, OMXC 20, together with prevailing sentiment in Denmark, to evaluate the impact of sentiment on OMXC 20. Furthermore, we introduce a rather novel and quantitative sentiment proxy, that is the use of the index of a larger market (US S&P 500), to see how the smaller market reacts to changes in the larger market. The use of larger market index is justified on economic/financial grounds in that globalisation has introduced a degree of interdependence, and allow us to explore global influences as a proxy for sentiment. We look at the robustness of our prediction. (Local) Negative sentiment (as articulated in Danish newspapers over a 7 year period (2007–2013), does have an impact on the local markets, but the global market (S&P 500) has an even greater impact.
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Notes
- 1.
OMX Copenhagen 20 (OMXC 20) is an equity market index for the Copenhagen Stock Exchange, a part of the NASDAQ OMX Group traded on NASDAQ stock exchange.
- 2.
Danish Business Digest is a daily abstracting service in English language, which covers Denmark and provides corporate, industry and economic news.
- 3.
Esmerk Denmark News provides English-language summaries on key business issues abstracted from local language sources (including 96 different news agencies: Børsen, Jyllands-Posten and Politiken etc.).
- 4.
A token is an individual occurrence of a linguistic unit in speech or writing.
- 5.
A sentiment analysis system developed at Trinity College Dublin.
- 6.
\(1^{st}\) difference of conditional variances of GARCH(1,1) models.
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Zhao, Z., Ahmad, K. (2015). Qualitative and Quantitative Sentiment Proxies: Interaction Between Markets. In: Jackowski, K., Burduk, R., Walkowiak, K., Wozniak, M., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2015. IDEAL 2015. Lecture Notes in Computer Science(), vol 9375. Springer, Cham. https://doi.org/10.1007/978-3-319-24834-9_54
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DOI: https://doi.org/10.1007/978-3-319-24834-9_54
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