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The influence of conference calls' semantic characteristics on the company market performance: Text analysis

Elena Fyodorova (), Ruslan Sayakhov (), Igor Demin () and Dmitriy Afanasyev
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Elena Fyodorova: HSE University, Moscow, Russia
Ruslan Sayakhov: HSE University, Moscow, Russia
Igor Demin: Financial University under the Government of the Russian Federation, Moscow, Russia

Russian Journal of Economics, 2019, vol. 5, issue 3, 297-320

Abstract: The purpose of the study is to find out the influence of semantic (emotional coloring, length and complexity of the text) and thematic features (environmental, corporate-social and legal context) of conference calls with market analysts and investors on future company performance (CAR) and analysts' recommendation for the share. The empirical framework of the research includes annual conference calls of public companies on the Moscow (MOEX) and London Stock Exchanges (LSE) from 2015 to 2019. The research methodology is based on the semantic analysis of the call text by using the linguistic dictionaries NRC 2010 and Corporate Social Responsibility 2016. The results of the study illustrate the significant impact of textual features of the conference call (the general tone of the call, the tone of management and the negative tone of analysts, the length and complexity of the text) on the abnormal stock returns (for 3, 14, 30 days). This relation is consistent for companies of both stock exchanges, but diverges in terms of the influence of the thematic characteristics of the call that can be explained by the mandatory disclosure of this information by European public companies (ESG Reports), as opposed to voluntary publication by Russian companies. The results can be applied both by the management of public companies in order to improve companies' attractiveness (perception and transparency) and its market value in the short and medium-term period, and by investors to manage effectively the portfolio by predicting the future dynamics of the company's share price after a conference call based on semantic tone and thematic features.

Keywords: conference call; semantic analysis; thematic analysis; non-financial information; neural network MLP; linguistic glossaries (search for similar items in EconPapers)
JEL-codes: G32 G41 (search for similar items in EconPapers)
Date: 2019
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DOI: 10.32609/j.ruje.5.47422

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