In March 2026, SGG Research began work on the programme now known as Earnings Call Quantitative Dynamics and Intensity (ECQDI). The project asks whether the quantitative character of external analysts' contributions to an earnings call contains information that can help an equity researcher distinguish between companies.
The programme complements an existing research effort into participation networks. ECPND examines who takes part and how participation history evolves across companies. ECQDI turns attention to the contributions themselves: the use of quantitative content within the discussion. Keeping the two questions separate makes it possible to evaluate whether they describe distinct information rather than two versions of the same factor.
Why quantitative discussion is worth studying
An earnings call gives analysts an opportunity to test their understanding of a company directly with management. Questions may connect reported performance with expectations, assumptions or operational constraints. A request to clarify the scale of a change can have a different character from a broad request for management's outlook. Those differences make the structure of quantitative discussion a potential research input.
The hypothesis does not assume that more numerical language is always better. The relevance of a quantitative contribution depends on its context, and industries differ in what they routinely discuss. Research therefore needs to distinguish a recurring company or sector convention from variation that could be useful across investment decisions.
A measurement that can be examined and repeated
The ECQDI approach is a deterministic text measurement. Given the same structured input and definition, the calculation should produce the same result. External analyst contributions are separated from management's statements, and the measurement is interpreted against earlier calls from the same sector. This provides a consistent basis for comparisons without requiring a reader to adopt a discretionary interpretation of each transcript.
Reproducibility also depends on timing. An older call can provide context only if its information was available before the observation being evaluated. A historical test needs to preserve that ordering and separate the measurement of the call from the subsequent stock return used to assess it. Source coverage, missing observations and revisions belong in the evaluation rather than being treated as incidental details.
Testing contribution within an existing model
The central portfolio question is whether adding ECQDI changes an existing stock-selection process in a useful way. A paired design starts with a common universe and model ranking, then compares the selection with and without the additional factor. Shared prices, allocation rules and costs help distinguish the effect of the factor's application from a change in the surrounding strategy.
Different portfolio policies can produce different answers. An observation that helps select a buy & hold portfolio need not improve a frequently rebalanced account. Selection strength, turnover and the treatment of stocks without a score are therefore part of the research question. The programme is intended to document those differences, including settings in which the factor does not help.
A separate line of research with a possible joint application
ECQDI also creates a way to study whether two observations from the same event complement each other. Participation history and quantitative discussion may lead to different stock rankings. That difference is a starting point for combined-factor research, not evidence on its own that a blended portfolio will perform better. Any combination needs a comparison with each factor individually and with a matched selection rule.
The programme broadens SGG Research's work on earnings calls by treating quantitative discussion as an observation in its own right. Its usefulness as an individual factor and as a complement to participation history requires separate comparisons, with the same attention to timing, coverage and portfolio assumptions.
About SGG Research
SGG Research develops alternative data factors for US equity research. Its work examines who participates in earnings calls, how connections evolve across companies, and the quantitative content of external analyst contributions. SGG Research delivers versioned factor scores, historical datasets and documented studies for independent evaluation in customers' own models and workflows. Its products are data analytics and research services, not financial advice.
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