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Clarifying Conference Calls

Companies and Markets

By Dr. Wang Chun Wei  |  December 21, 2022

What if we could find investment opportunities based on how people say things, as much as what they do say?

Over the years, quantitative signals have evolved both in insight and in the dataset used to capture that insight. The evolution has seen the extraction of tradeable information from price-based data (e.g., momentum) to accounting-based data (e.g., earnings yield) and more recently to text-based data (e.g., sentiment using natural language processing). One such text-based data set is conference call transcripts from quarterly company results meetings.

In his research paper Conference Call Clarity, Wei uses FactSet’s Document Distributor – XML Transcripts DataFeed to examine how English conference call transcripts can be employed to construct simple and intuitive alpha signals. He also uncovers simple themes in conference calls that may be used as systematic signals for alpha models.

Muddying the Waters

Language and thought are inherently interrelated concepts. Per the Sapir-Whorf hypothesis, twentieth-century linguists have often debated whether it is even possible to have one without the other. While we are not here to discuss how linguistic categories influence thought, we do believe the way language is used, and how sentences are strung together, provides valuable insights into not only the content, but the intentions and thoughts of the writer (or speaker). It is often said that clear thinking translates to clear writing or, per author Shane Parrish, “Clear writing gives poor thinking nowhere to hide.”

But what if one indeed wanted to hide poor thinking (or poor financial performance for that matter)? The one would go out of one’s way to employ unnecessarily complicated language. Language that is intended to obfuscate and confuse the analyst—something called “weasel words.”

In other words, markers of linguistic obfuscation may point to insincerity by company management. This is the motivation of our research.

There has been a significant body of academic research examining this type of behavior. The results are not surprising.

In essence, higher linguistic complexity in annual reports is associated with lower quality of information disclosure, subsequently resulting in greater divergence of opinions, greater volatility, and poorer performance in the long run.

The bottom line: managers use linguistic complexity to muddy the waters.

Conference Calls

Following the works of Li in 2008, there have been many papers in the academic literature that examine the use of language or linguistic markers in financial reports and communiques. Most focus on the text content of company annual reports. However, annual reports are polished and reviewed by teams of lawyers, incorporating a lot of legal jargon and boilerplate text. For instance, Brown and Tucker’s “Large-Sample Evidence on Firms' Year-over-Year MD&A Modifications” (2011) shows that the year-on-year changes in the informational content of the manager discussion and analysis section of the 10-K has been declining. They blame this on the increasingly “boilerplate nature” of the 10-K report.

Our focus will be on quarterly earnings conference call transcripts. Company managers and invited sell- and buy-side analysts dial into the call, which is recorded and later (or perhaps immediately) transcribed to words and published by third-party data providers like FactSet. The conference calls have a two-part structure: a prepared management discussion followed by a Q&A with the analysts present.

Conference call transcripts are relatively more fluid than U.S. 10-Q and 10-K reports, which incorporate a lot of legal jargon and boilerplate text. Bloomfield’s “Discussion of annual report readability, current earnings, and earnings persistence” (2008) commented that conference calls, being less scripted, better allow us to examine information content.

Furthermore, we find conference calls interesting for analysis as they are games of information asymmetry:

  • Senior management has information that the analysts do not
  • The former is trying to paint the information in the best possible light, while the latter is trying to pry as much information from the managers as possible to reduce the asymmetry
  • In other words, conference calls are a platform for observing managers' voluntary disclosure behavior. The interaction between the two sides during the call may provide useful insights that may not have been fully reflected in the traded price


As with any voluntary disclosure, managers face an ethical dilemma. This is shown in Evans et al.’s “Honesty in Managerial Reporting” (2001) and Liu et al.’s “Managers’ Unethical Fraudulent Financial Reporting: The Effect of Control Strength and Control Framing” (2014) research. On one hand they are bound by laws of continuous disclosure. On the other hand, when the information is negative, they tend to delay release. This is known as “bad news hoarding.”

Discretionary disclosures such as conference calls provide opportunities for managers to engage in opportunistic behavior. Managers are likely to exploit the information asymmetries between themselves (insiders) and analysts (outsiders).

Thus, managers who use complex or verbose language in their earnings calls may be trying to obfuscate information from prying analysts. Therefore, linguistic complexity in conference calls is likely to provide hints on future stock price performance.


This article is an excerpt of “Conference Call Clarity” (Wei, 2022, 1-4). View the research paper for more information including data characteristics, related research, measures for linguistic complexity, and more.

This blog post has been written by a third-party contributor and does not necessarily reflect the opinion of FactSet. The information contained in this blog post is not legal, tax, or investment advice. FactSet does not endorse or recommend any investments and assumes no liability for any consequence relating directly or indirectly to any action or inaction taken based on the information contained in this article.


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Dr. Wang Chun Wei, CFA

Quantitative Portfolio Manager

Dr. Wang Chun Wei is a Quantitative Portfolio Manager at Realindex Investments. He has 11 years of experience in financial services, and prior to joining Realindex, has worked as a Quantitative Analyst at Macquarie Group and Regal Funds Management and as a Senior Investment Analyst at Australian Super. He has also worked as a lecturer in finance at the University of Queensland Business School. Dr. Wei earned a Bachelor of Commerce (Honours) in Actuarial Studies from the University of Melbourne, a PhD in Finance from the University of Sydney, and a Master in Computer Science from the University of Illinois Urbana-Champaign. He is a CFA charter holder since 2017.


The information contained in this article is not investment advice. FactSet does not endorse or recommend any investments and assumes no liability for any consequence relating directly or indirectly to any action or inaction taken based on the information contained in this article.