How Do HFs Figure Out Delta In Earnings

I'm a bit confused how analysts at C/P72 are able to figure out (with certainty) whether a company beats/misses earnings/revenues on a QoQ basis

 How are they able to precisely develop models that use reliable projections, and actually remain grounded, without overcomplicating the key parameters? Where are they sourcing these values and information from (is it just internally from the data teams?)

8 Comments
 

Based on the most helpful WSO content, hedge funds like C/P72 often rely on a combination of advanced modeling techniques, alternative data sources, and deep industry insights to predict earnings deltas with precision. Here's how they typically approach it:

  1. Alternative Data Sources:
    Analysts often use third-party or alternative data sets to break down revenue growth into components like market growth, market share gains, and pricing. This granular approach allows them to identify trends that might not be immediately visible in standard sell-side models. For example, they might analyze credit card transaction data, web traffic, or even satellite imagery to gauge company performance.

  2. Equity Research and Consensus Estimates:
    Equity research reports are a key resource. These reports often provide high-level projections for revenue, EBITDA, and other metrics. Analysts compare these projections across multiple sources to identify discrepancies or trends. They also check the deltas between individual analysts and the consensus, paying attention to the number of analysts contributing to each period, as this can skew projections.

  3. Management Guidance:
    When available, management guidance is a critical input. It provides insights into future prospects, sales volumes, cost structures, and capital expenditures. However, in cases where management guidance is unavailable, analysts lean heavily on equity research and alternative data.

  4. Simplified Models with Key Parameters:
    Instead of overcomplicating models, analysts focus on key drivers of revenue and earnings. For instance, they might prioritize understanding pricing power, market share dynamics, and cost-saving initiatives over diving into every line item of the cash flow statement. This approach keeps the models grounded and actionable.

  5. Historical Data and Trends:
    Analysts track historical performance and trends to identify patterns or anomalies. For example, they might analyze how a company has historically performed relative to guidance or consensus estimates.

  6. Internal Data Teams:
    Many hedge funds have dedicated data teams that aggregate and process vast amounts of information. These teams provide analysts with clean, actionable data, which is then used to refine projections.

By combining these methods, hedge funds develop reliable projections that remain grounded while avoiding unnecessary complexity. This approach allows them to anticipate earnings beats or misses with a higher degree of confidence.

Sources: Sources and Uses: How to Find and Use Information in IBD (EBITDA example), Q&A: Equity Analyst at a Sovereign Wealth/Pension Fund, DCF Modeling Course ~ Pre-training text.pdf, Sources and Uses: How to Find and Use Information in IBD (EBITDA example)

I'm an AI bot trained on the most helpful WSO content across 17+ years.
 

The answer is that they don’t do any of these things “reliably” via a model. People are looking at option volumes/strikes, alt data, earnings of suppliers/vendors/competitors and assessing what may happen within some range of outcomes. Earnings is often more qualitative than quantitative (e.g., you can beat consensus but have soft commentary on the call and the stock will fall). At large shops, you’ll also have tremendous access to the c-suite and can try to pick up on how things may be going by analyzing behavior/tone etc (funds have hired CIA interrogators and linguists to judge a mgmt team’s candor).

 

More positioning around narrative going into the print vs actual #s beating

 

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