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    Intermediate
    Finance
    10 min read

    Earnings Call Intelligence for Investment Research

    For investment firms analyzing 1000+ earnings calls quarterly. Extract sentiment, key metrics, and management signals. 85% faster research coverage.

    Who It's For

    Hedge funds, asset managers, and equity research teams who need to efficiently analyze earnings calls across large portfolios

    Problem Solved

    Analysts cannot listen to all relevant earnings calls in real-time. Critical management signals and sentiment shifts are missed, leading to delayed investment decisions

    Why Mixpeek

    85% faster research coverage, real-time alerts on sentiment shifts, and comparative analysis across quarters and competitors

    Overview

    Earnings calls contain crucial signals beyond the numbers. This use case shows how Mixpeek helps investment teams extract alpha from management commentary at scale.

    Challenges This Solves

    Call Volume

    1000+ earnings calls per quarter across covered companies

    Impact: Impossible for analysts to attend/listen to all relevant calls

    Real-Time Requirements

    Markets react within minutes of earnings commentary

    Impact: Delayed analysis means missed trading opportunities

    Qualitative Signals

    Management tone, hedging language, confidence shifts

    Impact: Subjective interpretation, inconsistent analysis

    Comparative Context

    Need to compare vs prior quarters and competitors

    Impact: Manual comparison is time-consuming and error-prone

    Implementation Steps

    Mixpeek transcribes and analyzes earnings call audio, extracting key metrics, sentiment by topic, management tone shifts, and comparisons to previous quarters

    1

    Ingest Earnings Calls

    Process earnings call audio in real-time

    import { Mixpeek } from 'mixpeek';
    const client = new Mixpeek({ apiKey: process.env.MIXPEEK_API_KEY });
    // Process earnings call as it happens
    async function processEarningsCall(callUrl: string, metadata: {
    ticker: string;
    quarter: string;
    year: number;
    company_name: string;
    }) {
    return client.extract({
    url: callUrl,
    extractors: [
    'speech-to-text', // Full transcription
    'speaker-diarization', // CEO vs CFO vs analyst
    'sentiment-analysis', // By speaker and topic
    'entity-extraction', // Metrics, guidance, products
    'topic-segmentation' // Segment by discussion topic
    ],
    settings: {
    languages: ['en'],
    speaker_labels: ['ceo', 'cfo', 'analyst', 'operator'],
    compare_to: `${metadata.ticker}-Q${parseInt(metadata.quarter)-1}-${metadata.year}`
    },
    metadata: metadata
    });
    }
    2

    Extract Key Signals

    Identify metrics, guidance, and sentiment

    // Extract structured data from call
    async function analyzeEarningsCall(callId: string) {
    const analysis = await client.retrieve({
    collection_id: 'earnings-calls',
    document_id: callId,
    return_fields: [
    'transcript',
    'sentiment_by_topic',
    'extracted_metrics',
    'guidance_statements',
    'management_tone'
    ]
    });
    return {
    // Quantitative extractions
    metrics: {
    revenue_mentioned: analysis.extracted_metrics.revenue,
    eps_mentioned: analysis.extracted_metrics.eps,
    guidance_changes: analysis.guidance_statements
    },
    // Qualitative signals
    sentiment: {
    overall: analysis.sentiment_by_topic.overall,
    by_topic: analysis.sentiment_by_topic.breakdown,
    ceo_confidence: analysis.management_tone.ceo,
    cfo_confidence: analysis.management_tone.cfo
    },
    // Notable language
    hedging_detected: analysis.hedging_language,
    unusual_phrases: analysis.unusual_vs_historical,
    // Q&A dynamics
    analyst_sentiment: analysis.analyst_questions_sentiment,
    management_deflections: analysis.deflection_count
    };
    }
    3

    Compare Across Time

    Track changes vs prior quarters

    // Compare to historical calls
    async function compareToHistory(ticker: string, currentQuarter: string) {
    const history = await client.retrieve({
    collection_id: 'earnings-calls',
    filters: {
    ticker: ticker,
    quarter: { $lt: currentQuarter }
    },
    sort: { quarter: 'desc' },
    limit: 4 // Last 4 quarters
    });
    const current = await client.retrieve({
    collection_id: 'earnings-calls',
    filters: { ticker: ticker, quarter: currentQuarter }
    });
    return {
    sentiment_trend: calculateTrend(history, current, 'sentiment'),
    tone_shift: detectToneShift(history[0], current),
    new_topics: findNewTopics(history, current),
    dropped_topics: findDroppedTopics(history, current),
    guidance_changes: compareGuidance(history[0], current)
    };
    }
    4

    Set Up Real-Time Alerts

    Get notified on significant signals

    // Configure alerts for significant events
    await client.alerts.create({
    collection_id: 'earnings-calls',
    name: 'Sentiment Shift Alert',
    conditions: [
    {
    field: 'sentiment.overall',
    operator: 'change_from_prior',
    threshold: 0.2 // 20% sentiment shift
    },
    {
    field: 'guidance_statements',
    operator: 'contains',
    values: ['lowered', 'reduced', 'decreased', 'challenging']
    },
    {
    field: 'management_tone.ceo',
    operator: 'below',
    threshold: 0.4 // Low confidence detected
    }
    ],
    destinations: ['slack', 'email'],
    priority: 'high'
    });

    Expected Outcomes

    85% faster analysis across portfolio companies

    Research Coverage

    Real-time alerts within 30 seconds of key statements

    Alert Latency

    40% more sentiment signals detected vs manual analysis

    Signal Detection

    3x more companies covered per analyst

    Analyst Productivity

    Instant comparison to 20+ quarters of history

    Historical Context

    Frequently Asked Questions

    Ready to Implement This Use Case?

    Our team can help you get started with Earnings Call Intelligence for Investment Research in your organization.