Text Mining for Sentiment Analysis in Bond Portfolio Construction

The Efficient Market Hypothesis stipulates that financial instruments within a market perfectly reflect all public information. As the predominant method by which information is disseminated, it is considered that information contained within news influences the value of financial instruments. Thus, if the Efficient Market Hypothesis holds, then understanding the implications of such information is vital in informing portfolio construction. Sentiment analysis is the attempt of a machine to quantify the opinion of natural language. Therefore, this paper visits the Efficient Market Hypothesis by using a novel approach of analysing the relationship between financial news sentiment and corporate bonds in the time frame between 2015 and 2021. Several methods of sentiment analysis are employed to compare the efficacy of traditional lexicon-based knowledge extraction methods and the novel Transformer model approach. Furthermore, the short- and long-term impact of news is evaluated by comparing lagging and decaying signals. The outputs yielded from the sentiment analysis are used as a parameter in portfolio construction ant their returns are examined in light of Sharpe’s Ratio to find success in demonstrating the continued viability of the Efficient Market Hypothesis, and the strong relationship between this theory and modern methods of financial sentiment analysis. Ultimately, this paper yields literature leading results with a Sharpe Ratio of 2.1.