Evidentiality and Disagreement in Earnings Conference Calls: Preliminary Empirical Findings

Abstract

In financial communication, earnings conference calls represent a remarkable resource to understand the impact of argumentation on the decision-making process of the investing community. In this paper, we present some preliminary findings from a corpus-based study of earnings conference calls held by listed companies; specifically, we look at the distribution of evidentials in question and answer turns and their correlation with disagreement expressions. Our empirical results suggest that evidentials are argumentative indicators and characterize the argumentative roles of executives and analysts.

Keywords

• Evidentiality
• Disagreement
• Financial Communication

-

Financial communication is an ideal setting for the medium-to-large-scale analysis and evaluation of arguments and the observation of their impact on the decision-making process of investors. Our focus is on earnings conference calls (ECCs); held shortly after the release of a listed company’s quarterly results, an ECC generally begins with a corporate presentation by a company’s chief executives followed by a round of questions posed to the executives by the financial analysts (the Q&A session). As shown in [ 1 ], the most e↵ective part of an ECC is the argumentation in the Q&A session. Earlier argumentation modeling e↵orts targeted at ECCs have focused on formalizing an argumentatively relevant dialogue protocol validated on a small manually annotated corpus of ECCs [ 2 ]. Here we proceed with a multi-pronged research strategy, which combines a coarse rule-based automatic annotation dialogue moves in a large corpus of ECCs with the dictionary-based study of potential indicators of argumentation. These shallow methods are directly comparable with the kind of sentiment analysis used in finance research [ 3 ].

Data and Methods

Our study of evidentials as potential indicators for the characterization of argumentative moves in ECCs is based on two corpora: a small, manually annotated one, containing 46 conference call transcripts (508,787 words) and a relatively large one, containing 1,134 call transcripts (with 3,797,907 words in the corporate presentations 1,605,855 words in the questions, and 4,229,270 words in the replies).

The large corpus was segmented based on the deterministic structure of the call transcripts. Since the presentation and Q&A sessions are always labelled and the participants are always listed along with their roles, the call dynamics are predictable, with analysts asking questions and corporate players providing answers. Fully unsupervised coarse-grained dialog act labelling was performed with a Finite State Machine (all operator segments were ignored).

Taking inspiration from the results of corpus studies of evidential lexemes as argumentative indicators [ 5 ], we built a 208 item dictionary of words and n-grams meant to capture evidentiality as the encoding of the source of information and epistemic status of the propositional content of an utterance. The dictionary was built starting from corpus studies of evidentiality in English (especially [ 4 ]) and progressively manually refined through the study of corpus concordances. The dictionary items are categorized according to the types of meanings of evidentials represented in Figure 1.

A disagreement dictionary (158 n-grams) was also created. It includes adversative and concessive connectives, lexical expressions of disagreement, negations and hedges. The dictionary, which partially draws on previous argument mining approaches to disagreement [ 6 ], and contains a wide array of adversative and concessive connectives, negations, expressions that explicitly indicate disagreement, and hedges or mitigating devices to introduce disagreement (e.g. to be honest). 3 3.1

Preliminary Empirical Findings

Distribution of Evidentials in the calls Form

Subtype P sign From data 5963 869 4623 11455 1.57 0.54 1.09 prove From data 5944 859 3072 9875 1.57 0.54 0.73 guess Conjecture 164 6630 1982 8776 0.04 4.13 0.47 should Conjecture 1492 2775 2269 6536 0.39 1.73 0.54 obviously Conjecture 240 1653 4139 6032 0.06 1.03 0.98 probably N.A. 168 537 3476 4181 0.04 0.33 0.82 show From data 1621 344 1421 3386 0.43 0.21 0.34 proved From data 1999 150 581 2730 0.53 0.09 0.14 seem From data 101 1390 525 2016 0.03 0.87 0.12 clearly From data 325 275 1182 1782 0.09 0.17 0.28 assume Conjecture 629 582 445 1656 0.17 0.36 0.11 proving From data 891 146 443 1480 0.23 0.09 0.10 looks From data 112 821 506 1439 0.03 0.51 0.12 seems From data 54 987 332 1373 0.01 0.62 0.08 could be From data 159 367 738 1264 0.04 0.23 0.17 and disagreement are highly correlated (⇢ = 0.69). The correlation between disagreement and evidencial is also substantial if we consider the questions (⇢ = 0.73) and the answers (⇢ = 0.68) separately. 4

Conclusion and Future Work

Our ECC corpus shows a marked di↵erence in the distribution of evidentials in Q&A turns, matching the di↵erent epistemic position and argumentative role of executives and financial analysts. We also observe a substantial correlation between evidentiality and disagreement. Contrary to our initial expectation, the correlation is not exclusively driven by the questions posed by analysts, but also by the answers provided by the executives. The results suggest that disagreement and evidentiality dictionaries can function as sub-components of a dictionarybased indicator of argumentativity.

Our preliminary findings encourage us to pursue further investigations based on the combination of automatic segmentation of discourse units and dictionarybased methods.

References

1. Palmieri , R. , Rocci , A. , and Kudrautsava , N.: Argumentation in earnings conference calls. Corporate standpoints and analysts' challenges . Studies in Communication Sciences , 15 , ( 2015 ).

2. Budzynska , K. , Rocci , A. , and Yaskorska , O. : Financial Dialogue Games : A Protocol for Earnings Conference Calls . In S. Parsons et al. (Eds.), Computational Models of Argument (pp. 19 - 30 ). IOS Press, ( 2014 ).

3. Loughran , T. and Mcdonald , B. : Textual Analysis in Accounting and Finance : A Survey . ( 2016 ).

4. Bednarek , M. : Epistemological positioning and evidentiality in English news discourse: A text-driven approach . Text and Talk , 26 ( 6 ), pp. 635 - 660 , ( 2006 )

5. Musi , E. , and Rocci , A. : Obviously Epistentials are Argumentative Indicators : Evidence from an Italian and English Corpus of Newspaper Articles . In Proceedings of the Workshop on the Foundations of the Language of Argument at COMMA ( 2016 )

6. Budzynska , M. Janier , J. Kang , B. Konat , C. Reed , P. Saint-Dizier , and O. Yaskorska: Automatically identifying transitions between locutions in dialogue , in Proceedings of 1st European Conference on Argumentation: Argumentation and Reasoned Action ( 2015 )