An Analysis of the Practice of Earnings Conference Calls: Evidence from Deposit Money Banks in Nigeria
Bibliographic record
Abstract
In the digital age, especially the advent of the Internet, companies now have complimentary media or outright substitute media for passing information to stakeholders, thus giving stakeholders better access to information and providing equity of access to information. Annual reports and other financial reports are now posted on companies' websites and are freely accessible. Spoken communications, hitherto made during live meetings, are now commonly done through earnings calls. The study sample consists of 25 deposit money banks in Nigeria. The banks' websites were visited to access conference calls records from the First Quarter of 2008 to the year-end of 2019. A dichotomous non-weighted scoring method was used to assess the availability of earnings conference calls, while a weighted scoring method was adopted for the types of earnings calls disclosed. The study found that most quoted deposit money banks have adopted earnings conference calls to interact with analysts and investors, while unquoted deposit money banks are left behind. The study also found that while conference calls are made either as audio-only or video, most banks only post the transcripts of such interaction. The study recommends that banks that have not adopted earnings conference calls should adopt this medium to convince stakeholders that the banks are not hiding anything bad. Furthermore, the original form of the conference calls should be made available on the banks' websites in addition to transcripts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".