In March 2025, SGG Research began the research programme that became Earnings Call Participation Network and Dynamics (ECPND). Its starting point was that an earnings call leaves a structured record of professional participation as well as a record of management's statements. The programme asks whether that participation history can provide a useful additional input for US equity models.
The distinction matters because participation is a choice made by people with research responsibilities and limited time. External analysts often follow companies or sectors over multiple reporting periods. Their presence and questions may reflect expertise, expectations and investment considerations developed before a call. These characteristics make participation worth studying, while also requiring care about what attendance can actually tell a researcher.
Following connections across companies and time
A single participant list describes one event. Repeated observations create a richer record: an analyst returns to a company, appears across several issuers or changes the pattern of participation over time. Connecting those observations forms a network of companies and named external participants. Its evolution offers a way to examine how professional attention is distributed and how that distribution changes.
The research focuses on named external speakers recorded in structured call transcripts. Management representatives are a different part of the conversation, while people who only listen may not appear in the record at all. The resulting network is therefore an observable part of the earnings-call process, rather than a census of everyone paying attention to a company.
From an observation to a usable research factor
A dataset becomes useful to a quantitative researcher when the same definition can be applied consistently across stocks and dates. The programme seeks to turn eligible participation history into a structured factor that can be joined to an equity panel, with a clear distinction between an available value and missing evidence. That requires consistent participant identities, an explicit observation date and a disciplined treatment of historical information.
Timing is central to the question. A portfolio decision must use the information eligible at that decision, not a later transcript or a stock outcome that had not yet occurred. Daily factor records also need to distinguish the age of a company's latest call from changes in the surrounding participation history. A day without a new call need not be a day without relevant information.
Asking whether the extra information helps
The investment-research question is incremental: does this observation contribute something useful when added to an existing model? Comparing a factor portfolio with an unrelated strategy would make that difficult to judge. A more informative test holds the underlying stock-ranking model and portfolio assumptions constant, then measures what changes when participation information is allowed to influence selection.
A credible evaluation also needs to examine alternatives. Company size, sector, analyst coverage and existing price patterns can affect both participation and returns. Costs, portfolio turnover and missing data may alter the practical result. The programme's purpose is to test the factor against such considerations, rather than assume that a plausible account of professional attention establishes predictive value.
Building a record that other researchers can question
The intended output is a research input with documented use, not an instruction to trade a particular stock. A published study and supplied score history can allow other teams to test whether the observation is useful within their own universes and workflows. Keeping the proprietary calculation separate from the disclosed portfolio application makes it possible to explain the test without distributing the score engine.
The programme connects a measurable observation with a question that other researchers can examine: whether participation history improves decisions made with an existing equity model. Documenting the input, its timing and its portfolio application gives that question a consistent basis for further work.
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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