How to Screen for Earnings Winners Using KPI Analysis

With 88% of early S&P 500 reporters beating EPS estimates through 17 April, Q1 2026 is shaping up as a strong earnings quarter, but the real edge lies in knowing where to look before results drop. Morgan Stanley’s equity strategy team has released a screening methodology designed to identify companies likely to deliver positive surprises on specific key performance indicators, a framework that moves beyond simple EPS beats toward more granular operational metrics. This analysis breaks down how institutional investors approach earnings season positioning, examines the current macro backdrop, and applies Morgan Stanley’s methodology to specific stocks reporting in the coming weeks.

Why early beat rates signal opportunity, not complacency

The headline statistics confirm underlying strength. Of the S&P 500 companies that reported through 17 April, 88% exceeded EPS estimates, well above the 78% five-year average and the 76% ten-year average. Revenue beat rates followed the same trajectory: 84% of reporters surpassed forecasts, compared to historical averages of 70% (five-year) and 67% (ten-year). The aggregate surprise margins reinforce the theme, with EPS results landing 10.8% above expectations and revenue coming in 2.0% higher than consensus.

FactSet’s S&P 500 earnings season analysis through 17 April 2026 confirms the aggregate beat rates cited above, with their independent data showing 88% of early reporters exceeding EPS estimates and 84% surpassing revenue forecasts, figures that align with the strong performance observed across major financial data providers.

Blended year-over-year earnings growth sits at 13.2%, whilst revenue growth clocks in at 9.9%. These figures suggest corporate fundamentals are holding up despite geopolitical noise, including the Strait of Hormuz disruption and Federal Reserve rate-cut delays.

Strong aggregate beat rates support a constructive market backdrop, yet understanding how equity markets absorbed the recent geopolitical shock helps investors assess whether current valuations reflect durable fundamentals or transient relief.

But only 10% of S&P 500 companies had reported as of 17 April. The aggregate beat rates are strong, yet they mask sector and quality dispersion. Morgan Stanley’s Michael Wilson characterises the current environment as early cycle, a contrarian view given the market’s age. He argues that positive operating leverage is fuelling earnings recovery, and that price-to-earnings leadership will widen across the market during 2026. The implication: aggregate strength creates a constructive backdrop, but the alpha lies in identifying which specific companies will surprise on the metrics that matter most.

Wilson’s early cycle thesis assumes that positive operating leverage will drive P/E expansion, yet the recent sentiment divergence from market action suggests that institutional positioning may be more cautious than headline index performance implies.

Strong aggregate beat rates support a constructive market backdrop. However, with only 10% of companies reported, dispersion will likely increase as more sectors weigh in. Investors who position ahead of high-conviction names can capture alpha before the broader narrative crystallises.

How Morgan Stanley screens for earnings season winners

Morgan Stanley’s methodology applies a two-layer filter. The first layer is straightforward: the stock must carry an overweight rating from the firm’s equity analysts. This filter ensures the starting universe comprises names where the research team sees fundamental value.

The second layer is where the approach sharpens. Sector analysts select the most pertinent performance metric for each stock, rather than applying a blanket EPS screen. For a wealth management firm, the watched metric might be net new assets. For a cloud monitoring company, it could be revenue growth. For a sports betting operator, adjusted EBITDA may be the lever that signals operating inflection. The methodology was published the week of 21 April 2026.

This is a more surgical approach than simply betting on EPS beats. Most retail investors screen for EPS surprises alone, which are often priced in by the time results land. Morgan Stanley’s framework targets the specific operational lever most likely to surprise, whether that is revenue, adjusted EBITDA, net new assets, or another metric. Understanding this logic helps investors think beyond consensus estimates.

The numbered steps:

1. Filter for stocks rated overweight by Morgan Stanley equity analysts
2. Identify the company-specific KPI most likely to drive a positive surprise, as determined by the relevant sector analyst

The early cycle thesis driving the framework

Michael Wilson’s early cycle characterisation underpins the screening logic. He considers this view contrarian, given how long the current expansion has run. His thesis rests on positive operating leverage: companies are converting revenue growth into disproportionate earnings growth as they scale beyond break-even thresholds.

Wilson projects that price-to-earnings leadership will widen across the market during 2026. In practical terms, this means the gap between winners and laggards will expand as investors reward companies delivering on the right operational metrics. The Morgan Stanley screen is designed to identify those names before the market reprices them.

Understanding KPI based earnings analysis

A key performance indicator is a specific, measurable metric that captures the health of a company’s core business operations. Different sectors track different KPIs because the levers that drive value vary by business model. Wealth management firms watch net new assets because client acquisition compounds over time. Growth companies in the cloud infrastructure space focus on revenue because scale determines unit economics. Sports betting operators track adjusted EBITDA because it isolates operating profitability from promotional spend.

Choosing the right KPI for a specific company matters more than tracking generic EPS. EPS is an accounting output. A KPI is an operational input. Morgan Stanley’s Q1 2026 results illustrate this distinction.

The SEC’s updated Compliance and Disclosure Interpretations for non-GAAP financial measures under Regulation G and Item 10(e) of Regulation S-K establish the regulatory framework for how companies can present adjusted EBITDA, net new assets, and other KPIs in earnings releases, ensuring that the metrics Morgan Stanley’s analysts track meet disclosure standards.

The firm reported $20.6 billion in revenue, up 16% year-over-year, and $3.43 EPS, beating the $3.00 estimate. Return on tangible common equity (ROTCE) hit 27.1%, up from 23.0% a year earlier. These are strong headline numbers, but the Wealth Management segment’s KPIs reveal the durability of the results.

Wealth Management net new assets totalled $118 billion in Q1 2026, compared to $93.8 billion in Q1 2025. Fee-based asset flows reached $54 billion. These metrics confirm the firm’s wealth management moat is deepening. Institutional Securities revenue came in at $10.7 billion, up 19% year-over-year, driven by heightened market volatility and strong client demand for advisory services.

Morgan Stanley’s EPS beat tells you they had a good quarter. The net new asset figure tells you their wealth management moat is deepening. Investors who track the right KPI see the durability of results, not just the headline. This is the analytical skill the Morgan Stanley screening methodology operationalises.

Two stocks Morgan Stanley expects to surprise

Morgan Stanley’s screen identified Datadog and DraftKings as two event-driven setups where the watched KPI could catalyse re-rating. Both have experienced meaningful drawdowns, creating a setup where a positive surprise on the monitored metric could shift sentiment.

Datadog (NASDAQ: DDOG) reports Q1 results on 7 May 2026 before market open, with a conference call scheduled for 8:00 AM EDT. The stock is down 15% over the prior six months. Morgan Stanley’s sector analysts are watching revenue as the key metric. The thesis: AI adoption increases data volumes and IT complexity, positioning Datadog as a beneficiary. Guggenheim characterised the company as a key AI beneficiary, noting that enterprises deploying AI models generate exponentially more telemetry data, which Datadog’s observability platform is designed to capture.

The risk is that competitive pressures from hyperscale cloud providers (AWS, Azure, Google Cloud) offering native monitoring tools may cap Datadog’s pricing power. The six-month drawdown reflects this concern. A revenue beat, particularly if driven by AI workload growth, would validate the bull case.

“Datadog is positioned as a key beneficiary of AI adoption, given the increase in data volumes and IT complexity that AI workloads generate.” (Guggenheim analysis)

DraftKings (NASDAQ: DKNG) reports Q1 results on 30 April 2026. The stock is down 29% over the prior 12 months. Morgan Stanley is focused on adjusted EBITDA as the key metric. The firm expects DraftKings to beat Q1 adjusted EBITDA despite issuing full-year guidance of $700 million to $900 million, which came in below consensus of $980.7 million (FactSet). The company launched a prediction markets platform in late 2025, diversifying beyond sports betting.

The bear case centres on competitive intensity and regulatory overhang. Kalshi and other prediction market entrants are fragmenting user attention, whilst state-level tax increases in key markets (New York, Illinois) compress margins. The 12-month drawdown prices in these headwinds. An adjusted EBITDA beat would signal that the company’s promotional efficiency and user retention are stronger than the market assumes.

For investors evaluating Datadog’s observability platform within the broader competitive landscape, our dedicated guide to AI’s impact on SaaS business models examines how hyperscale cloud providers are deploying native monitoring tools and how this structural shift affects pricing power for third-party observability vendors.

Both stocks represent event-driven opportunities, not buy-and-hold recommendations. The watched KPI in each case is the pivot point. A beat could catalyse re-rating. A miss would confirm the bear case. The risk-reward profile is binary.

Applying the framework to your own watchlist

The Morgan Stanley methodology is not proprietary magic. It is a disciplined process: identify quality names, determine the metric that matters, and position before the surprise. Individual investors can replicate this by doing pre-earnings homework rather than reacting to headlines.

A practical three-step checklist:

1. Identify the most relevant KPI for the stock. This requires understanding the business model. For a SaaS company, annual recurring revenue (ARR) or net retention rate may matter more than EPS. For a biotech, clinical trial enrolment or regulatory milestone progress outweighs quarterly revenue.
2. Check recent analyst commentary for consensus expectations on that KPI. Consensus EPS estimates are widely tracked. Consensus KPI expectations are less visible but often disclosed in sell-side research notes or company guidance.
3. Position before results land, not after. The alpha in earnings season belongs to those who anticipate the surprise, not those who chase the headline.

Upcoming earnings dates can be verified via company investor relations pages or SEC filings. As of 17 April, the data cutoff for aggregate S&P 500 beat rates, more companies will report through May. Monitor these sources for the most current timing.

What the data does not yet tell us

Real-time data from 18 April to 21 April 2026 is unavailable in current market databases. The statistics cited above reflect the latest verifiable information through 17 April 2026. Actual progress may have evolved in the interim.

Updated beat rates, sector-specific surprises, and revised growth estimates will emerge as reporting progresses. Available search results lack coverage from Bloomberg, CNBC, Reuters, WSJ, FactSet, or Refinitiv for this period, as well as views from other strategists at firms like Goldman Sachs or JPMorgan. Investors should monitor these sources, along with company filings, for the most current figures as the reporting window advances.

Conclusion

Q1 2026 early beat rates are running above historical averages, supporting a constructive backdrop for equity positioning. Morgan Stanley’s KPI-based screening methodology offers a more surgical approach than EPS-focused analysis, targeting the specific operational levers most likely to surprise. Datadog and DraftKings represent two event-driven setups where the watched metric could catalyse re-rating, though both carry execution risk given competitive and regulatory pressures.

As earnings season progresses, the dispersion between winners and laggards will widen. Investors who identify the right KPI before results land position themselves to capture alpha rather than chase headlines. The aggregate strength provides a tailwind, but the edge belongs to those who know what operational metric matters most for each name on their watchlist.

Review your own watchlist through the KPI lens before the next round of reports. The edge in earnings season belongs to those who know what to watch before the numbers drop.

For readers wanting to understand the structural drivers behind the early cycle thesis and positive operating leverage assumptions, our full explainer on AI capital expenditure and equity market support examines how $600 billion in infrastructure spending creates revenue visibility for specific subsectors and why this capex cycle differs from prior technology buildouts.

This article is for informational purposes only and should not be considered financial advice. Investors should conduct their own research and consult with financial professionals before making investment decisions.