Equity Models
Earnings call and financial sentiment classification models available in the Models Service. See the Models Service overview for API usage.
Financial Phrase Bank
Financial Sentiment Classifier
Financial Phrase Sentiment
fpb_sentiment_labelClassifies the sentiment of financial sentences as positive, neutral, or negative from an investor perspective.
Labels
| Label | Description |
|---|---|
| Positive | The text conveys favorable financial developments-growth, profit, or a positive outlook. |
| Negative | The text conveys unfavorable financial developments-loss, decline, or a negative outlook. |
| Neutral | The text is factual or balanced with no clear positive or negative financial signal. |
Examples
"The company reported strong earnings growth and improved profit margins."
"The company released its quarterly report on Tuesday."
"The company warned of declining revenues due to weaker market demand."
Usage
curl -X POST \
-H "x-api-key: $ZQ_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model_id": "fpb_sentiment_label", "instances": [{"text": "Your text here"}]}' \
$ZQ_BASE_URL/v1/models/infer
Earnings Call Transcripts
Earnings Call Claim Projection Classifier
Outlook vs. Reported Numbers Discussion
ec_claim_projection_classificationShows whether a text discusses guidance or a forecast versus citing completed or audited-style results.
Labels
| Label | Description |
|---|---|
| IN_CLAIM | Forward-looking guidance, target, or projection-not purely historical reporting. |
| OUT_OF_CLAIM | Statement about realized results or established facts rather than a live forecast. |
Examples
"Management reaffirmed their full-year guidance of 15% revenue growth."
"Global semiconductor demand declined last quarter due to weaker consumer electronics sales worldwide."
Usage
curl -X POST \
-H "x-api-key: $ZQ_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model_id": "ec_claim_projection_classification", "instances": [{"text": "Your text here"}]}' \
$ZQ_BASE_URL/v1/models/infer
Earnings Call Competition Relevancy Classifier
Competitors and Market Position Discussion
ec_competition_relevancyHighlights rivalry, market share, and how the company stacks up against peers.
Labels
| Label | Description |
|---|---|
| IRRELEVANT | The text does not focus on competitive dynamics. |
| RELEVANT | The text discusses competition, peers, or market share. |
Examples
"We continue to see aggressive pricing from our main competitor in the enterprise segment."
"Our board approved a quarterly dividend of $0.50 per share."
Usage
curl -X POST \
-H "x-api-key: $ZQ_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model_id": "ec_competition_relevancy", "instances": [{"text": "Your text here"}]}' \
$ZQ_BASE_URL/v1/models/infer
Earnings Call Cost of Capital Relevancy Classifier
Funding Costs and Cost of Capital Discussion
ec_cost_of_capital_relevancyShows whether a text discusses how expensive it is for the company to fund itself-debt, equity, and hurdle rates.
Labels
| Label | Description |
|---|---|
| IRRELEVANT | The text does not focus on funding costs or cost of capital. |
| RELEVANT | The text discusses cost of capital, funding costs, or financing terms. |
Examples
"Rising interest rates have increased our weighted average cost of capital by 50 basis points."
"We opened three new retail locations in the Southeast region."
Usage
curl -X POST \
-H "x-api-key: $ZQ_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model_id": "ec_cost_of_capital_relevancy", "instances": [{"text": "Your text here"}]}' \
$ZQ_BASE_URL/v1/models/infer
Earnings Call Cyber Risk Relevancy Classifier
Cybersecurity Risk Discussion
ec_cyber_risk_relevancyShows whether a text discusses cyber threats, breaches, data security, or related risk.
Labels
| Label | Description |
|---|---|
| IRRELEVANT | The text does not focus on cybersecurity or data security. |
| RELEVANT | The text discusses cybersecurity, data protection, or cyber risk to the business. |
Examples
"Management discussed increased investment in cybersecurity following a recent ransomware attack targeting the company's IT systems."
"The board approved a dividend increase of two cents per share."
Usage
curl -X POST \
-H "x-api-key: $ZQ_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model_id": "ec_cyber_risk_relevancy", "instances": [{"text": "Your text here"}]}' \
$ZQ_BASE_URL/v1/models/infer
Earnings Call Forward Looking Classifier
Future Outlook vs. Past or Present Facts Discussion
ec_forward_looking_classificationShows whether management is talking about what is ahead or about what already happened or is already known.
Labels
| Label | Description |
|---|---|
| FORWARD_LOOKING | Focuses on the future-guidance, expectations, or plans. |
| NOT_FORWARD_LOOKING | Focuses on history, current facts, or non-forward-looking detail. |
Examples
"Our guidance suggests a 5% increase in capital expenditure for Q4."
"In the third quarter, we successfully integrated the acquired assets."
Usage
curl -X POST \
-H "x-api-key: $ZQ_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model_id": "ec_forward_looking_classification", "instances": [{"text": "Your text here"}]}' \
$ZQ_BASE_URL/v1/models/infer
Earnings Call Geopolitical Risk Relevancy Classifier
Geopolitics and Cross-Border Risk Discussion
ec_geopolitical_risk_relevancyShows whether a text discusses political tensions, conflicts, sanctions, or country risk that matter for operations.
Labels
| Label | Description |
|---|---|
| IRRELEVANT | The text does not focus on geopolitics. |
| RELEVANT | The text discusses geopolitical events, regions, or cross-border political risk. |
Examples
"Rising trade tensions in the region could impact our manufacturing costs."
"Our R&D team has filed three new patents for battery efficiency."
Usage
curl -X POST \
-H "x-api-key: $ZQ_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model_id": "ec_geopolitical_risk_relevancy", "instances": [{"text": "Your text here"}]}' \
$ZQ_BASE_URL/v1/models/infer
Earnings Call Growth Classifier
Growth vs. Contraction Talk Discussion
ec_growth_classificationSeparates discussion of expansion and tailwinds from slowdowns and headwinds-or topics that are not about growth at all.
Labels
| Label | Description |
|---|---|
| POSITIVE_GROWTH | Discusses growth, expansion, or improving trajectory for the business or sector. |
| NEGATIVE_GROWTH | Discusses decline, contraction, or obstacles to growth. |
| NO_GROWTH_RELATED_DISCUSSION | Not really about growth or shrinkage of the business or market. |
Examples
"We expect double-digit revenue expansion driven by our cloud segment next year."
"Macro headwinds have led to a contraction in our hardware sales volumes."
"There is no positive or negative outcome for us yet with regard to labour."
Usage
curl -X POST \
-H "x-api-key: $ZQ_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model_id": "ec_growth_classification", "instances": [{"text": "Your text here"}]}' \
$ZQ_BASE_URL/v1/models/infer
Earnings Call Labor Market Relevancy Classifier
Workforce and Hiring Discussion
ec_labor_market_relevancyShows whether a text discusses hiring, layoffs, wages, or labor availability as they affect the company.
Labels
| Label | Description |
|---|---|
| IRRELEVANT | The text does not focus on labor or the workforce. |
| RELEVANT | The text discusses employees, hiring, layoffs, wages, or labor conditions. |
Examples
"We've seen wage inflation of 8% in our manufacturing workforce this quarter."
"Our new mobile app reached 10 million downloads last month."
Usage
curl -X POST \
-H "x-api-key: $ZQ_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model_id": "ec_labor_market_relevancy", "instances": [{"text": "Your text here"}]}' \
$ZQ_BASE_URL/v1/models/infer
Earnings Call Material Impact Classifier
Material Impact Classification for Investors
ec_material_impact_classificationIndicates whether a passage signals material positive impact, material negative impact, or no material impact-i.e. whether developments appear economically meaningful for shareholders.
Labels
| Label | Description |
|---|---|
| POSITIVE_IMPACT | Material positive impact: commentary or facts that suggest a meaningful favorable effect on the business or investment case. |
| NEGATIVE_IMPACT | Material negative impact: commentary or facts that suggest a meaningful adverse effect on the business or investment case. |
| NO_IMPACT | No material impact implied: balanced, routine, or factual wording without a clear economically meaningful upside or downside signal. |
Examples
"The new tax legislation will significantly improve our after-tax cash flow."
"Supply chain disruptions had a material adverse effect on our quarterly margins."
"The minor rebranding of our logo will not affect our financial guidance."
Usage
curl -X POST \
-H "x-api-key: $ZQ_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model_id": "ec_material_impact_classification", "instances": [{"text": "Your text here"}]}' \
$ZQ_BASE_URL/v1/models/infer
Earnings Call Price Relevancy Classifier
Prices, Costs, and Margins Discussion
ec_price_relevancyShows whether a text discusses input costs, selling prices, revenue, margins, or similar pricing economics.
Labels
| Label | Description |
|---|---|
| IRRELEVANT | The text does not focus on pricing or cost economics. |
| RELEVANT | The text discusses prices, costs, margins, or related economics. |
Examples
"We implemented a 7% price increase across our product portfolio in January."
"Our sustainability initiatives reduced carbon emissions by 20%."
Usage
curl -X POST \
-H "x-api-key: $ZQ_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model_id": "ec_price_relevancy", "instances": [{"text": "Your text here"}]}' \
$ZQ_BASE_URL/v1/models/infer
Earnings Call Regulatory Risk Relevancy Classifier
Regulation and Legal Risk Discussion
ec_regulatory_risk_relevancyShows whether a text discusses rules, compliance, regulators, or litigation that could affect the business.
Labels
| Label | Description |
|---|---|
| IRRELEVANT | The text does not focus on regulatory or legal matters. |
| RELEVANT | The text discusses regulation, compliance, policy, or legal proceedings. |
Examples
"Pending FDA approval could delay our product launch by six months."
"We completed the migration of our data centers to the cloud."
Usage
curl -X POST \
-H "x-api-key: $ZQ_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model_id": "ec_regulatory_risk_relevancy", "instances": [{"text": "Your text here"}]}' \
$ZQ_BASE_URL/v1/models/infer
Earnings Call ESG Relevancy & Positive Signal Classifier
ESG Tone Discussion
ec_esg_alpha_sentimentClassify whether ESG-related content sounds positive, negative, neutral-or whether the sentence is not really about ESG.
Labels
| Label | Description |
|---|---|
| ESG_POSITIVE | ESG angle reads clearly favorable for the company or stakeholders. |
| NOT_ESG_RELATED | Not mainly about environmental, social, or governance topics. |
Examples
"Our renewable energy investments are generating both environmental benefits and cost savings."
"We are facing regulatory scrutiny over our carbon emissions disclosures, which could result in significant fines."
"We published our annual sustainability report in line with GRI standards this quarter."
"Revenue from our subscription business grew 25% year-over-year."
Usage
curl -X POST \
-H "x-api-key: $ZQ_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model_id": "ec_esg_alpha_sentiment", "instances": [{"text": "Your text here"}]}' \
$ZQ_BASE_URL/v1/models/infer
Earnings Call Uncertainty Clarity Classifier
Clarity and Confidence of Wording Classification
ec_uncertainty_complexity_classificationCaptures whether management sounds hedgy, uses heavy jargon, speaks plainly with conviction-or is just boilerplate.
Labels
| Label | Description |
|---|---|
| HIGH_UNCERTAINTY | Heavy hedging or non-committal language about outcomes. |
| CLEAR_AND_CERTAIN | Direct, simple language that sounds confident. |
| NOT_RELEVANT | Routine or procedural text without real business substance for this lens. |
Examples
"Given the volatile macro environment, we're unable to provide full-year guidance at this time."
"The multi-jurisdictional regulatory arbitrage across our vertically integrated enterprise necessitates a holistic synergistic realignment of our cross-functional value-chain imperatives."
"We're confident in achieving our stated margin expansion targets this year."
"The company held its annual shareholder meeting on March 15."
Usage
curl -X POST \
-H "x-api-key: $ZQ_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model_id": "ec_uncertainty_complexity_classification", "instances": [{"text": "Your text here"}]}' \
$ZQ_BASE_URL/v1/models/infer
Earnings Call M&A Strategy Classifier
M&A and Deals Discussion
ec_merger_acquisition_intentCategorizes talk about buying companies, selling businesses, or life after a deal-versus no deal-related content.
Labels
| Label | Description |
|---|---|
| ACQUISITION_INTENT | Buying companies, scouting targets, or growing through acquisitions. |
| DIVESTITURE_INTENT | Selling businesses, spin-offs, or exiting assets. |
| POST_MERGER_INTEGRATION | After the deal-integration, synergies, combining teams, or post-close execution. |
| NO_MA_DISCUSSION | No meaningful M&A, divestiture, or integration topic in this sentence. |
Examples
"We're actively evaluating strategic acquisition targets in the fintech space."
"We plan to divest our non-core logistics business by the end of this fiscal year."
"We've achieved $200 million in synergies from the Acme acquisition ahead of schedule."
"Our employee retention rate improved to 92% this year."
Usage
curl -X POST \
-H "x-api-key: $ZQ_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model_id": "ec_merger_acquisition_intent", "instances": [{"text": "Your text here"}]}' \
$ZQ_BASE_URL/v1/models/infer
Earnings Call Capital Allocation Classifier
How Cash is Used Classification
ec_capital_allocation_priorityShows whether a text discusses how the company intends to use its cash-dividends, debt, or reinvestment.
Labels
| Label | Description |
|---|---|
| SHAREHOLDER_RETURNS | Cash returned via dividends, buybacks, or similar programs. |
| DEBT_REPAYMENT | Cash used to reduce debt or strengthen the balance sheet. |
| REINVESTMENT_CAPEX | Cash earmarked for capex, R&D, or organic growth projects. |
| NOT_ALLOCATION_RELATED | Does not clearly state a priority for how cash will be used. |
Examples
"We plan to return $2 billion to shareholders through buybacks and dividends this year."
"Our primary focus is reducing leverage to investment-grade levels over the next 18 months."
"We're allocating $500 million to expand our semiconductor fabrication capacity."
"Customer satisfaction scores improved by 12 points year-over-year."
Usage
curl -X POST \
-H "x-api-key: $ZQ_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model_id": "ec_capital_allocation_priority", "instances": [{"text": "Your text here"}]}' \
$ZQ_BASE_URL/v1/models/infer
Earnings Call Macro Sensitivity Classifier
Classification of Macro Factor Matters Discussion
ec_macro_economic_sensitivityClassify whether the sentence ties results or outlook to inflation, interest rates, currencies-or none of these.
Labels
| Label | Description |
|---|---|
| INFLATIONARY_PRESSURE | Links the story to economy-wide inflation or deflation, not just one product's price. |
| INTEREST_RATE_SENSITIVITY | Links the story to interest rates or central-bank policy (not currency moves alone). |
| CURRENCY_FX_IMPACT | Links the story to exchange rates or currency moves. |
| NO_MACRO_MENTION | No clear macro driver-mostly company-specific or operational. |
Examples
"Prices are increasing and there are early signs of inflation."
"Higher rates have significantly impacted demand for our mortgage products."
"The strong dollar reduced our international revenue by $50 million this quarter."
"We launched our new mobile banking app with biometric authentication."
Usage
curl -X POST \
-H "x-api-key: $ZQ_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model_id": "ec_macro_economic_sensitivity", "instances": [{"text": "Your text here"}]}' \
$ZQ_BASE_URL/v1/models/infer
Earnings Call Product Pipeline Classifier
New Products and R&D Progress Discussion
ec_product_innovation_pipelineSeparates launch announcements and R&D updates from unrelated product discussion.
Labels
| Label | Description |
|---|---|
| NEW_LAUNCH | Highlights a new product or service going to market. |
| RD_PROGRESS | Update on research, development stages, or the innovation pipeline. |
| NOT_PRODUCT_RELATED | Not mainly about launches, R&D milestones, or product roadmap. |
Examples
"We're launching our next-generation electric vehicle platform in Q3."
"Our Phase 3 clinical trials are progressing well with promising efficacy data."
"The company reported strong revenue growth during the quarter driven by improved operational efficiency."
Usage
curl -X POST \
-H "x-api-key: $ZQ_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model_id": "ec_product_innovation_pipeline", "instances": [{"text": "Your text here"}]}' \
$ZQ_BASE_URL/v1/models/infer
Earnings Call Q&A Deception & Evasion Classifier
Straight Answer vs. Sidestep Discussion
ec_qa_deception_evasion_detectionFor each analyst question and management reply, indicates whether management answered directly or seemed to dodge or hedge.
Labels
| Label | Description |
|---|---|
| EVASIVE_RESPONSE | The answer feels evasive-vague, off-topic, or avoiding a clear commitment when specifics were asked for. |
| DIRECT_RESPONSE | The answer directly addresses the question with specifics or a clear management position. |
Examples
"QUESTION: Can you give us a specific revenue target for Q3? ANSWER: We remain focused on executing our long-term strategy and are confident in the direction of the business as we navigate the current environment."
"QUESTION: What is your expected gross margin for next quarter? ANSWER: We expect gross margin to be in the range of 58% to 60%, driven by lower input costs and improved manufacturing efficiency."
Usage
curl -X POST \
-H "x-api-key: $ZQ_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model_id": "ec_qa_deception_evasion_detection", "instances": [{"text": "Your text here"}]}' \
$ZQ_BASE_URL/v1/models/infer
Earnings Call Q&A Analyst vs. Management Tone Classifier
Analyst vs. Management Tone Discussion
ec_qa_manager_analyst_divergenceCompares how the analyst's question and management's answer line up-purely factual, aligned in mood, or pulling in different directions.
Labels
| Label | Description |
|---|---|
| FACTUAL_EXCHANGE | Mostly mechanics, numbers, or definitions-little debate over outlook or sentiment. |
| STANCE_DIVERGENCE | Question and answer take clearly different tones on risk, optimism, or outlook. |
| TONE_CONVERGENCE | Question and answer share a similar stance-both upbeat, both cautious, or both neutral. |
Examples
"QUESTION: Can you clarify how you're accounting for the deferred revenue from the enterprise contracts? ANSWER: Sure-those are being recognised ratably over the contract term, typically 24 to 36 months, in line with our standard revenue recognition policy."
"QUESTION: Your margins have declined for three consecutive quarters and costs are rising sharply-why should investors believe this isn't the start of a longer-term profitability issue? ANSWER: We completely disagree with that characterization-this is a short-term investment phase, and we are extremely confident margins will expand meaningfully over the next two quarters."
"QUESTION: The new product cycle looks like a meaningful tailwind—how are customers responding so far?; ANSWER: Early feedback has been very positive; pipeline activity is ahead of plan and we're optimistic about adoption through 2026."
Usage
curl -X POST \
-H "x-api-key: $ZQ_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model_id": "ec_qa_manager_analyst_divergence", "instances": [{"text": "Your text here"}]}' \
$ZQ_BASE_URL/v1/models/infer
Support
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