{"edition":"2026-09-29","organization":"SGG Research","legal_entity":"DREAVERR Digital Solutions LLP","description":"SGG Research develops alternative data factors for US equity research. Its work studies earnings-call participation, interaction networks and quantitative patterns, delivered through structured datasets, documented studies and an authenticated API.","boilerplate":"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.","facts":[["Organization","SGG Research"],["Research focus","Alternative data factors for US equities, derived from structured earnings-call observations"],["ECPND","Earnings Call Participation Network and Dynamics: structured participation history across companies and dates"],["ECQDI","Earnings Call Quantitative Dynamics and Intensity: quantitative patterns in external analyst contributions"],["Delivery","Authenticated API access with timestamped, versioned score records; hourly checks and updates on change"],["Evaluation","Public whitepapers and a free research package with two years of daily historical scores, ending eight weeks before the package date"],["Purpose","Data analytics and independent research; not financial or investment advice"]],"reuse":{"title":"Press material reuse permission","credit":"Source: SGG Research","paragraphs":["SGG Research grants a free, worldwide, non-exclusive permission to copy, publish, distribute, quote, translate and adapt the original press texts and designated network images on this page and in the SGG Research press kit. This includes newspapers, magazines, websites, newsletters, broadcasts, educational publications and commercial editorial publications. No separate approval is needed for these uses.","Automated crawling, indexing, text and data mining, and use of these materials in AI-generated answers, summaries and knowledge-retrieval systems are expressly permitted under the same conditions. Each release is readable as public HTML; the full press texts are also available in plain text, structured JSON and the download. No account is required.","Credit SGG Research and link to this press page wherever the medium supports links. Identify substantive edits or translations. Images may be resized and cropped. Retain enough context to describe the network accurately, including the snapshot date where relevant. When reproducing performance figures, retain the horizon, comparison basis and historical-research qualification.","Do not imply that SGG Research, a named company or a participant endorses a publication, product or investment. Do not alter numbers, labels or connections in a way that misrepresents the research. This permission does not grant ownership of trademarks, endorsement rights or rights belonging to third parties.","This permission is limited to the designated press material. It does not license transcript text, underlying source databases, score CSVs, research-package datasets, paid API responses, credentials or the proprietary factor methodology for redistribution. Those materials remain subject to their separate terms. This specific permission governs the permitted press reuse where the general service terms would otherwise restrict it.","The material is provided as published for information and editorial use, without a guarantee of accuracy, completeness or future outcomes. It is research material, not financial or investment advice. Independently verify claims relevant to your publication and consult the linked papers for the full study context. Nothing in this permission limits rights or remedies that cannot lawfully be excluded."]},"primary_release_slug":"earnings-call-factors-for-us-equity-research","releases":[{"slug":"earnings-call-factors-for-us-equity-research","kind":"launch","date":"2026-09-29","published_at":"2026-09-29","title":"SGG Research launches ECPND early access for US equity research","standfirst":"The earnings-call participation factor is now available through an authenticated API, alongside a free historical research package and a documented comparison with a Qlib / LightGBM baseline.","paragraphs":["SGG Research has opened early access to Earnings Call Participation Network and Dynamics (ECPND), an alternative data factor for US equity research. The service delivers timestamped, versioned scores through an authenticated API, allowing quantitative researchers and investment teams to evaluate participation information within their own models and portfolio processes.","The launch connects three parts of the research programme: a daily factor dataset, a public study of its historical contribution and a delivery service for ongoing use. Researchers can begin with a free sample of two years of daily historical scores, ending eight weeks before the package date, before deciding whether to subscribe to current data."],"sections":[{"heading":"A different observation from the earnings call","paragraphs":["Earnings calls record both what a company says and which external professionals choose to engage with it. Analysts bring sector knowledge, company coverage and research priorities to those discussions. Their participation can reflect work and expectations formed before the call. Recurring participants also connect companies, creating a history of attention that extends beyond a single quarterly event.","ECPND turns that participation history into a bounded research score. The underlying network evolves as further eligible observations become available, so a company can have a current score even on a day when it has no new call. The score is designed to complement an existing equity-research process; it is not a probability of a price increase or a predicted return."]},{"heading":"A published comparison with the baseline held constant","paragraphs":["The ECPND whitepaper evaluates weekly US equity portfolios formed from the same Qlib / LightGBM predictions, with and without the participation factor. Capital, execution rules and trading costs are shared. The study covers 245 weekly starting dates from January 2022 to September 2026, four fixed model seeds, and both buy & hold and weekly rebalancing. Each reported holding period includes only completed formations.","For buy & hold, the primary comparison records mean paired return differences of +17 basis points over three months, +54 over six months and +107 over twelve months, after the stated cost of 10 basis points per executed trade. The six-month 95% block-bootstrap interval is +13 to +99 basis points; the three- and twelve-month intervals include zero. Weekly portfolios overlap, and these are SGG Research's historical calculations under the disclosed assumptions, not an independently audited result or a live investment track record.","The paper sets out the baseline, timing convention, portfolio rules and sensitivity checks so readers can assess what the comparison does and does not establish. The free research package includes historical scores, study results, cohort comparisons and a worked selection example. Its two-year score sample supports evaluation over those supplied dates; it does not reproduce the entire study window by itself."]},{"heading":"Ongoing data for existing research workflows","paragraphs":["The API provides a current score set with observation and publication information and version identifiers. Hourly checks look for relevant changes and publish updated data when required. The checking frequency describes data maintenance; it does not prescribe an hourly trading strategy. Customers can retrieve the dataset on a schedule suited to their own decision process.","ECPND early access is priced at USD 1,850 per month, billed quarterly at USD 5,550 plus applicable taxes. Coverage, permitted use and subscription terms are set out on the product and terms pages. The service supplies factor data and analytics for internal research, with API documentation and integration support."]},{"heading":"Continuing the research beyond the historical study","paragraphs":["SGG Research is also recording prospective ECPND portfolio decisions and subsequent outcomes through its forward-testing process. That record is at an early stage and is kept separate from the historical results. Further work examines selection strength, portfolio exposures and implementation assumptions, alongside the separate ECQDI text factor and research into combining the two inputs.","The aim of early access is to make a documented research input available for testing in other models and workflows. The published history provides a reason to investigate the factor; the customer's own universe, costs and portfolio design determine how it should be evaluated."]}],"links":[{"label":"ECPND whitepaper","href":"/whitepaper/ecpnd"},{"label":"Research package and early access","href":"/#validate-pricing"},{"label":"API documentation","href":"/api-docs"}],"url":"/press/earnings-call-factors-for-us-equity-research","download":"sgg-research_earnings-call-factors-for-us-equity-research.txt"},{"slug":"combined-factors-whitepaper","kind":"whitepaper","date":"2026-09-29","published_at":"2026-09-29","title":"New study examines ECPND and ECQDI as complementary research inputs","standfirst":"The combined-factor paper separates a useful question from an easy assumption: different rankings can broaden the information set, but do they improve the same portfolio together?","paragraphs":["SGG Research has published a study of Earnings Call Participation Network and Dynamics (ECPND) alongside Earnings Call Quantitative Dynamics and Intensity (ECQDI). First published on 29 September 2026, the note examines how participation history and quantitative patterns in external analyst contributions interact within the same US equity-selection process.","Across 245 weekly formations, the factors have a mean stock-ranking correlation of +0.076. Their lowest-fifth groups overlap on 4.34% of candidates with both scores. These observations show that the two inputs produce different rankings in the studied panel; they do not establish statistical independence or guarantee a better combined portfolio.","The original equal-weight rank combination adds 39 basis points to mean six-month buy & hold returns over the Qlib / LightGBM baseline, compared with 54 basis points for ECPND alone. A wider comparison examines 60 rules and 52 matched ECPND controls, including different weights and selection strengths. Some configurations are promising, but reliable incremental performance over a suitably matched ECPND-only rule remains to be demonstrated.","The paper presents correlation and overlap diagnostics alongside paired portfolio results, continuous accounts and sensitivity checks. It retains unfavourable outcomes and explains why a stronger selection rule can account for part of an apparent improvement. The results are historical, long-only research after the stated costs. The note is publicly readable and intended to support independent evaluation of whether the factors complement each other in a researcher's own models."],"sections":[],"links":[{"label":"Read the combined-factor whitepaper","href":"/whitepaper/combination"},{"label":"ECPND whitepaper","href":"/whitepaper/ecpnd"},{"label":"ECQDI whitepaper","href":"/whitepaper/ecqdi"}],"paper_first_published_at":"2026-09-29","paper_updated_at":"2026-09-29","url":"/press/combined-factors-whitepaper","download":"sgg-research_combined-factors-whitepaper.txt"},{"slug":"ecqdi-whitepaper","kind":"whitepaper","date":"2026-07-29","published_at":"2026-09-29","title":"SGG Research publishes ECQDI study on quantitative patterns in earnings calls","standfirst":"The first ECQDI paper introduces a deterministic text factor focused on quantitative patterns in external analyst contributions, providing a second perspective on earnings-call research.","paragraphs":["SGG Research's first ECQDI whitepaper introduces Earnings Call Quantitative Dynamics and Intensity as a separate research input for US equities. The project examines the quantitative character of external analysts' contributions to earnings calls, with earlier calls from the same sector providing context for the measurement.","The rationale is that questions can reflect how analysts test assumptions, interpret reported performance and examine the scale of a company's activities. Those observations differ from the participation history captured by ECPND. They may help a researcher describe an earnings-call discussion in another way, but a plausible interpretation alone does not establish investment usefulness.","The paper frames that usefulness as a portfolio question: what changes when a fixed ECQDI application is added to an otherwise unchanged stock-ranking process? Consistent timing, a common candidate universe and shared costs are essential to that comparison. Different selection strengths and portfolio policies may produce different outcomes, making the application rule part of the research rather than an incidental choice.","The paper gives researchers a framework for examining quantitative discussion alongside other equity inputs, including participation history. It keeps the measurement separate from the investment process in which it may be used. ECQDI is a research factor to be evaluated in the user's own models and workflows, not a recommendation to buy or sell securities."],"sections":[],"links":[{"label":"Read the ECQDI whitepaper","href":"/whitepaper/ecqdi"},{"label":"Get the research package","href":"/#validate-pricing"}],"paper_first_published_at":"2026-07-29","paper_updated_at":"2026-09-29","url":"/press/ecqdi-whitepaper","download":"sgg-research_ecqdi-whitepaper.txt"},{"slug":"ecpnd-whitepaper","kind":"whitepaper","date":"2026-03-29","published_at":"2026-09-29","title":"ECPND whitepaper documents the participation factor and its portfolio evidence","standfirst":"The first ECPND paper introduces earnings-call participation history as a structured research input and sets out how to examine its contribution to a price-based equity model.","paragraphs":["The first publication of SGG Research's ECPND whitepaper marks a step from the research question to a documented framework for evaluation. Earnings Call Participation Network and Dynamics examines who participates in earnings calls, how recurring participants connect companies and whether that history offers information useful to US equity research.","The paper explains the rationale for studying professional participation and the role of a supplied daily score. A company's latest eligible call is part of a wider history; the value for a decision date must be interpreted with the information eligible at that time. A missing score remains distinct from a low score, and a high score is not a forecast of a particular return.","The evaluation question is whether the factor adds value to an existing stock-selection process. Paired portfolios provide a way to ask that question while holding the underlying model forecasts, capital, execution assumptions and costs constant. Researchers can examine the score's application without needing access to the proprietary calculation.","The openly readable paper provides a common reference for researchers assessing participation data within their own models and workflows. Its purpose is to make the research question and portfolio application understandable, while distinguishing a supplied factor from an investment recommendation. ECPND is a data analytics and research product, not financial or investment advice."],"sections":[],"links":[{"label":"Read the ECPND whitepaper","href":"/whitepaper/ecpnd"},{"label":"Get the research package","href":"/#validate-pricing"}],"paper_first_published_at":"2026-03-29","paper_updated_at":"2026-09-29","url":"/press/ecpnd-whitepaper","download":"sgg-research_ecpnd-whitepaper.txt"},{"slug":"ecqdi-research-begins","kind":"research_milestone","date":"2026-03","published_at":"2026-09-29","title":"SGG Research begins ECQDI research into quantitative patterns in earnings calls","standfirst":"A second research programme examines the quantitative character of external analyst contributions, extending the focus from participation history to a different observable within the same conversation.","paragraphs":["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."],"sections":[{"heading":"Why quantitative discussion is worth studying","paragraphs":["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."]},{"heading":"A measurement that can be examined and repeated","paragraphs":["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."]},{"heading":"Testing contribution within an existing model","paragraphs":["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."]},{"heading":"A separate line of research with a possible joint application","paragraphs":["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."]}],"links":[{"label":"Subsequent ECQDI whitepaper","href":"/whitepaper/ecqdi"},{"label":"Subsequent combined-factor study","href":"/whitepaper/combination"},{"label":"ECPND research background","href":"/whitepaper/ecpnd"}],"url":"/press/ecqdi-research-begins","download":"sgg-research_ecqdi-research-begins.txt"},{"slug":"ecpnd-research-begins","kind":"research_milestone","date":"2025-03","published_at":"2026-09-29","title":"SGG Research begins research into earnings-call participation networks","standfirst":"The ECPND programme starts from a question beyond the transcript text: can the changing pattern of professional participation add information to US equity research?","paragraphs":["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."],"sections":[{"heading":"Following connections across companies and time","paragraphs":["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."]},{"heading":"From an observation to a usable research factor","paragraphs":["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."]},{"heading":"Asking whether the extra information helps","paragraphs":["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."]},{"heading":"Building a record that other researchers can question","paragraphs":["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."]}],"links":[{"label":"Subsequent ECPND whitepaper","href":"/whitepaper/ecpnd"},{"label":"ECPND early access announcement","href":"/press/earnings-call-factors-for-us-equity-research"}],"url":"/press/ecpnd-research-begins","download":"sgg-research_ecpnd-research-begins.txt"}],"whitepapers":[{"title":"ECPND - Earnings call participation network and dynamics as an alternative data factor for US equities","href":"/whitepaper/ecpnd"},{"title":"ECQDI - Earnings call quantitative dynamics and intensity as an alternative data factor for US equities","href":"/whitepaper/ecqdi"},{"title":"ECPND and ECQDI as complementary research inputs for US equities","href":"/whitepaper/combination"}],"origin":"https://sgg-development-development.up.railway.app","release":{"slug":"earnings-call-factors-for-us-equity-research","kind":"launch","date":"2026-09-29","published_at":"2026-09-29","title":"SGG Research launches ECPND early access for US equity research","standfirst":"The earnings-call participation factor is now available through an authenticated API, alongside a free historical research package and a documented comparison with a Qlib / LightGBM baseline.","paragraphs":["SGG Research has opened early access to Earnings Call Participation Network and Dynamics (ECPND), an alternative data factor for US equity research. The service delivers timestamped, versioned scores through an authenticated API, allowing quantitative researchers and investment teams to evaluate participation information within their own models and portfolio processes.","The launch connects three parts of the research programme: a daily factor dataset, a public study of its historical contribution and a delivery service for ongoing use. Researchers can begin with a free sample of two years of daily historical scores, ending eight weeks before the package date, before deciding whether to subscribe to current data."],"sections":[{"heading":"A different observation from the earnings call","paragraphs":["Earnings calls record both what a company says and which external professionals choose to engage with it. Analysts bring sector knowledge, company coverage and research priorities to those discussions. Their participation can reflect work and expectations formed before the call. Recurring participants also connect companies, creating a history of attention that extends beyond a single quarterly event.","ECPND turns that participation history into a bounded research score. The underlying network evolves as further eligible observations become available, so a company can have a current score even on a day when it has no new call. The score is designed to complement an existing equity-research process; it is not a probability of a price increase or a predicted return."]},{"heading":"A published comparison with the baseline held constant","paragraphs":["The ECPND whitepaper evaluates weekly US equity portfolios formed from the same Qlib / LightGBM predictions, with and without the participation factor. Capital, execution rules and trading costs are shared. The study covers 245 weekly starting dates from January 2022 to September 2026, four fixed model seeds, and both buy & hold and weekly rebalancing. Each reported holding period includes only completed formations.","For buy & hold, the primary comparison records mean paired return differences of +17 basis points over three months, +54 over six months and +107 over twelve months, after the stated cost of 10 basis points per executed trade. The six-month 95% block-bootstrap interval is +13 to +99 basis points; the three- and twelve-month intervals include zero. Weekly portfolios overlap, and these are SGG Research's historical calculations under the disclosed assumptions, not an independently audited result or a live investment track record.","The paper sets out the baseline, timing convention, portfolio rules and sensitivity checks so readers can assess what the comparison does and does not establish. The free research package includes historical scores, study results, cohort comparisons and a worked selection example. Its two-year score sample supports evaluation over those supplied dates; it does not reproduce the entire study window by itself."]},{"heading":"Ongoing data for existing research workflows","paragraphs":["The API provides a current score set with observation and publication information and version identifiers. Hourly checks look for relevant changes and publish updated data when required. The checking frequency describes data maintenance; it does not prescribe an hourly trading strategy. Customers can retrieve the dataset on a schedule suited to their own decision process.","ECPND early access is priced at USD 1,850 per month, billed quarterly at USD 5,550 plus applicable taxes. Coverage, permitted use and subscription terms are set out on the product and terms pages. The service supplies factor data and analytics for internal research, with API documentation and integration support."]},{"heading":"Continuing the research beyond the historical study","paragraphs":["SGG Research is also recording prospective ECPND portfolio decisions and subsequent outcomes through its forward-testing process. That record is at an early stage and is kept separate from the historical results. Further work examines selection strength, portfolio exposures and implementation assumptions, alongside the separate ECQDI text factor and research into combining the two inputs.","The aim of early access is to make a documented research input available for testing in other models and workflows. The published history provides a reason to investigate the factor; the customer's own universe, costs and portfolio design determine how it should be evaluated."]}],"links":[{"label":"ECPND whitepaper","href":"/whitepaper/ecpnd"},{"label":"Research package and early access","href":"/#validate-pricing"},{"label":"API documentation","href":"/api-docs"}],"url":"/press/earnings-call-factors-for-us-equity-research","download":"sgg-research_earnings-call-factors-for-us-equity-research.txt"},"study":{"recomputed":"2026-09-21","first_formation":"2022-01-10","last_formation":"2026-09-14","prices_through":"2026-09-18","formation_weeks":245,"seeds":[19,41,73,101],"baseline":"Qlib / LightGBM","policy":"Buy & hold","cost_bp_per_trade":10,"comparisons":[{"months":3,"mean_difference_bp":17,"interval_95_bp":[-13,47],"completed_formations":232},{"months":6,"mean_difference_bp":54,"interval_95_bp":[13,99],"completed_formations":219},{"months":12,"mean_difference_bp":107,"interval_95_bp":[-5,218],"completed_formations":193}],"context":"Mean paired return differences after the stated 10 bp trading costs, averaged across completed weekly formations and four fixed model seeds. Weekly holdings overlap. The six-month 95% block-bootstrap interval is above zero; the three- and twelve-month intervals include zero. These are SGG Research's historical calculations, not independently audited results, annualized alpha or live performance. See the ECPND whitepaper for timing, universe, selection and execution assumptions."},"network":{"snapshot_at":"2026-09-29T07:24:26.720Z","requested_from":"2026-06-29","requested_to":"2026-09-29","first_call":"2026-08-10","last_call":"2026-09-28","calls":900,"companies":894,"participants":1617,"links":3469,"window_limit":900,"source_sha256":"6f742e908ffe5f21d7e6c79eb99f594366d243625ecb258aacdd1b9c48624d91","context":"Frozen export of the landing-page network snapshot. The display is capped at the 900 most recent calls within the requested three-month window; it is not a count of all US calls or the complete factor research database. The two views retain the same stored node positions. Node proximity is a layout property, not a return prediction, and participation does not imply endorsement."},"images":[{"id":"overview","title":"Earnings-call participation network - overview","caption":"Overview of the stored participation network. Squares identify companies; circles identify named external participants; links record participation in a call. Node positions are identical in both views.","focus":null,"width":3200,"height":2000,"png":"sgg-research_network-overview.png","svg":"sgg-research_network-overview.svg","preview":"sgg-research_network-overview-preview.webp","bytes":{"png":1522545,"svg":920481}},{"id":"detail","title":"Earnings-call participation network - detail","caption":"Detail centred on DE. Squares identify companies; circles identify named external participants; links record participation in a call. Node positions are identical in both views.","focus":"DE","width":3200,"height":2000,"png":"sgg-research_network-detail.png","svg":"sgg-research_network-detail.svg","preview":"sgg-research_network-detail-preview.webp","bytes":{"png":1514145,"svg":1193914}}],"links":{"press":"/press","release":"/press/earnings-call-factors-for-us-equity-research","releases":["/press/earnings-call-factors-for-us-equity-research","/press/combined-factors-whitepaper","/press/ecqdi-whitepaper","/press/ecpnd-whitepaper","/press/ecqdi-research-begins","/press/ecpnd-research-begins"],"license":"/press#reuse","whitepaper_ecpnd":"/whitepaper/ecpnd","whitepaper_ecqdi":"/whitepaper/ecqdi","whitepaper_combination":"/whitepaper/combination"},"asset_version":"c89549ab0e43"}