News-based supervised sentiment analysis for prediction of futures buying behaviour

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. Keywords: Sentiment analysis, Real-time news, Trade direction, Vector space model, Support vector machines, Prediction, Futures market, Net buying pressure