Literature review

Methodology

Data analysis and results

Conclusion

References

Abstract

This study examines the predictability of real-time news data on investors’ buying behaviour in the futures market, using supervised sentiment analysis. Market sentiment or traders’ buying behaviour is captured at the bid-ask stage of price formation using the net buying pressure (NBP). Any significant change in NBP patterns defines an “interesting market event”. Real-time news headlines are automatically labelled using interesting market events, assuming a lag between the market information and its impact on buying behaviour. News was found to have an impact on the market buying behaviour of the S&P NIFTY index futures with an optimal lag of 5 minutes. Manual labelling of the news data validated this empirical finding.

Literature review

Prior studies have established the predictability of the impact of news on market sentiment in the spot market context. Supervised sentiment analysis is a text classification task where the impact of textual market information on financial markets is learnt by using labelled training data. The creation of the training data involves labelling the news instances according to their impact on the markets. The news instances can be labelled manually according to the news discourse (Davis et al., 2006; Bozic & Seese, 2011) or automatically based on the corresponding market trends (Mittermayer & Knolmayer, 2006). Labelling news instances manually is a precise but labour-intensive task; hence, this method is not suitable for high-frequency news analytics. Automatically aligning news instances with the corresponding market trends makes for one of the most challenging tasks in sentiment analysis. Yoo et al. (2005), Mittermayer & Knolmayer (2006), Nikfarjam et al. (2010), and Nassirtoussi et al. (2014) reviewed studies that have used the supervised sentiment analysis approach. A brief review of some of the major concerns that our study deals with, follows. News-trend alignment is one of the most important parts of a supervised sentiment analysis model for testing and contextualising market efficiency. The accuracy of the newstrend alignment procedure constrains the efficacy of the supervised sentiment analysis.