Affiliations: [a] Dipartimento di Informatica, University of Bari Aldo Moro, Bari, Italy
| [b] Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, 9747 AG Groningen, The Netherlands
| [c] Department of Computer Science, University of Liverpool, Liverpool L69 3BX, UK
| [d] Department of Communication and Media, University of Liverpool, Liverpool L69 3BX, UK
Correspondence:
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Corresponding author: Andrea Pazienza, Dipartimento di Informatica, University of Bari Aldo Moro, Via E. Orabona, 4, Bari, Italy. E-mail: andrea.pazienza@uniba.it.
Abstract: Financial analysts constitute an important element of financial decision-making in stock exchanges throughout the world. By leveraging on argumentative reasoning, we develop a method to predict financial analysts’ recommendations in earnings conference calls (ECCs), an important type of financial communication. We elaborate an analysis to select those reliable arguments in the Questions & Answers (Q&A) part of ECCs that analysts evaluate to estimate their recommendation. The observation date of stock recommendation update may variate during the next quarter: it can be either the day after the ECC or it can take weeks. Our objective is to anticipate analysts’ recommendations by predicting their judgment with the help of abstract argumentation. In this paper, we devise our approach to the analysis of ECCs, by designing a general processing framework which combines natural language processing along with abstract argumentation evaluation techniques to produce a final scoring function, representing the analysts’ prediction about the company’s trend. Then, we evaluate the performance of our approach by specifying a strategy to predict analysts recommendations starting from the evaluation of the argumentation graph properly instantiated from an ECC transcript. We also provide the experimental setting in which we perform the predictions of recommendations as a machine learning classification task. The method is shown to outperform approaches based only on sentiment analysis.
Keywords: Argumentation, natural language processing, sentiment analysis, machine learning
