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Learning with Sentiment

Combining machine learning with news sentiment for stock market trading

Machine Learning methods are often used to forecast and trade financial markets, including sentiment data can significantly enhance the performance of Machine Learning algorithms.

In recent years, we have seen an unprecedented explosion of interest in applying artificial intelligence and machine learning to a variety of quantitative finance problems, ranging from derivatives pricing and risk management to market forecasting and algo trading. In fact, Artificial Intelligence and Machine Learning are now seen as the greatest enablers of competitive advantage in the finance sector. 

In this paper, Svetlana Borovkova, Probability & Partners, Margot Dijkstra, VU Amsterdam, and Rossy Nguyen, VU Amsterdam use Refinitiv News Analytics, Refinitiv's natural language processing engine in two applications of machine learning with sentiment: 

  • Intraday forecasting of a major stock index – EURO STOXX 50. 
  • Daily trading of the 100 most liquid S&P 500 stocks. 

Both applications use state-of-the-art Machine Learning techniques – LSTM neural net and NEAT genetic algorithm – in combination with news sentiment.

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