This week's screen surfaced a sharply divided tape, with Cognizant, Microsoft, and Garmin leading the upside while Lennox International and Vertiv absorbed the heaviest selling. The dispersion points to selective capital rotation rather than broad market conviction, with earnings reactions and company-specific repricing driving the strongest signals.
This edition of Signals Desk breaks down the five moves and then shows how to reproduce the screen using the FMP EOD Bulk API, including how to compare fixed-date snapshots, filter the market universe, and rank the week's largest gainers and decliners.
Key Takeaways
- Cognizant, Microsoft, and Garmin gained more than 20%, with each move supported by stronger operating evidence, improved guidance, or clearer demand validation.
- Lennox and Vertiv showed how sharply the market can react when reported results fall short of expectations already embedded in valuation.
- The FMP EOD Bulk API provides the price-movement screen, while earnings surprises, income statements, cash flow, and analyst data help explain what drove the repricing.
Five Names That Defined This Week's Tape
Cognizant Technology Solutions Corporation (CTSH)
Weekly Performance: +21.78%
Cognizant's 21.78% weekly gain reflected more than a routine earnings reaction. The company raised its annual adjusted profit forecast after reporting stronger performance in financial services and continued operating-margin improvement, even as enterprise clients remained selective about discretionary technology spending. That combination mattered because it shifted attention from the broader slowdown in consulting demand toward Cognizant's ability to protect profitability and capture spending tied to modernization and AI-related projects.
The signal is therefore less about a broad recovery in IT services and more about execution within an uneven demand environment. Readers should watch whether growth remains concentrated in financial services or begins to broaden across other verticals, and whether margin gains are supported by durable operating improvements rather than temporary cost controls. FMP's quarterly income statement and financial ratios datasets would help track revenue growth, operating margin, and earnings progression, while the analyst estimates endpoint would show whether expectations moved materially after the results.
Microsoft Corporation (MSFT)
Weekly Performance: +21.75%
Microsoft's 21.75% advance became one of the week's clearest signals that the market was still willing to reward large-scale AI investment when it was accompanied by visible cloud growth and cash generation. Azure revenue increased 43% in the fiscal fourth quarter, ahead of expectations, while Microsoft projected fiscal first-quarter sales of approximately $90.4 billion. The company also reported more than 30 million paid Copilot users and a cloud backlog of $678 billion, providing investors with measurable evidence that AI demand was translating into contracted business rather than remaining purely thematic.
Capital intensity remains central to the interpretation. Microsoft's quarterly capital expenditure rose more than 70% year over year to $41 billion, but management's spending framework and continued free-cash-flow generation helped ease some concern about the economics of AI infrastructure. The relevant question is not simply whether spending remains elevated, but whether Azure growth, backlog conversion, and Copilot adoption continue to justify that investment. FMP's cash flow statement, income statement, and analyst estimates data would provide a useful framework for comparing capital expenditure, free cash flow, cloud-supported revenue growth, and changes in consensus expectations.
Garmin Ltd. (GRMN)
Weekly Performance: +20.88%
Garmin rose 20.88% after reporting record second-quarter revenue of approximately $2.02 billion, an 11% year-over-year increase. Operating income climbed 30% to $616 million, while gross margin expanded to 62.4% and operating margin reached 30.4%. The company also raised its full-year guidance, giving the move a clear fundamental basis rather than leaving it dependent on market momentum alone.
The broader takeaway is Garmin's ability to produce growth and margin expansion despite operating across several discretionary product categories. Its mix of fitness, outdoor, aviation, marine, and automotive products provides more diversification than a conventional consumer-electronics company, but the sustainability of the quarter still depends on segment-level demand and product mix. Readers should monitor whether margin expansion remains broad-based and whether growth in premium wearables is supported by steady aviation and marine performance. FMP's income statement and segment-level financial data, where available, would help separate consolidated strength from the contribution of individual business lines, while historical earnings data would place the margin improvement in a longer-term context.
Lennox International Inc. (LII)
Weekly Performance: -23.15%
Lennox's 23.15% weekly decline followed a reduction in its full-year profit outlook and renewed evidence of pressure in residential HVAC demand. Home Comfort Solutions revenue fell 7% year over year, primarily because of lower volumes, while residential new construction remained a meaningful headwind. Although demand improved sequentially from the first quarter and favorable pricing and mix provided some support, the market reaction showed that investors were focused on the weaker volume environment and the revised earnings framework.
This move highlights the difference between pricing resilience and underlying demand strength. Lennox can preserve part of its profitability through mix, pricing, and operational execution, but those factors do not fully replace unit growth when housing activity and consumer confidence remain soft. The next useful signals are residential volume, distributor inventory, new-construction exposure, and the relationship between price realization and input costs. FMP's quarterly income statement, cash flow statement, and analyst estimates datasets would help illustrate whether lower revenue expectations are flowing through to margins, cash generation, and consensus earnings revisions.
Vertiv Holdings Co (VRT)
Weekly Performance: -16.80%
Vertiv declined 16.80% even though its second-quarter adjusted earnings grew strongly and management raised full-year guidance. The company reported 24% year-over-year sales growth and a 60% increase in adjusted diluted earnings per share to $1.52, while its updated 2026 outlook called for approximately $14 billion in net sales and adjusted diluted EPS of $6.65 to $6.75. The immediate pressure came from quarterly revenue falling short of expectations, a notable miss for a stock carrying substantial expectations tied to AI data-center investment.
The reaction illustrates how valuation and expectations can matter as much as reported growth. Vertiv's underlying business continued to benefit from demand for power and thermal-management infrastructure, but the selloff suggested that investors were applying a high threshold to near-term revenue delivery. The data suggests this is an area to monitor for differences between orders, backlog, recognized revenue, and execution capacity, rather than treating the weekly decline as a simple reversal in data-center demand. FMP's income statement, enterprise value and valuation multiples, and analyst estimates datasets would help compare operating growth with the expectations embedded in the share price, while earnings-history data would show whether revenue misses have become more frequent or remained isolated.
Reading the Broader Signal Behind the Weekly Moves
The week's five largest moves point to a market rewarding evidence rather than exposure. Cognizant, Microsoft, and Garmin each gained more than 20%, yet the common thread was not sector membership. Each supplied firmer operating results, improved guidance, or clearer validation of the spending narrative attached to the business. Lennox and Vertiv moved sharply lower because their reported fundamentals did not fully clear the expectations already embedded in their valuations. The split suggests earnings season was acting as a test of execution, with limited tolerance for gaps between a strong narrative and the latest numbers.
A weekly price screen shows where expectations changed, but not whether the reaction was proportionate. That is where the broader dataset available through FMP becomes analytically useful. EOD return data can be paired with the Earnings Surprises Bulk API to measure results against consensus, then compared with revenue, margins, and net income from the Income Statement Bulk API. Adding Cash Flow Statement data helps determine whether reported earnings were supported by operating cash generation or required unusually heavy capital deployment.
Expectations data completes the picture. Changes in analyst recommendations from the Upgrades Downgrades Consensus Bulk API can be viewed alongside Key Metrics TTM data, including free-cash-flow yield and valuation measures, to see whether the price reaction was followed by a broader reassessment of earnings quality. Across these five names, the signal was not a uniform sector rotation. It was a sharper distinction between companies validating the assumptions behind their valuations and those leaving parts of the investment case unresolved.
Building a Repeatable Weekly Movers Framework with FMP Data
A reliable weekly movers screen starts with bulk pricing data tied to fixed dates rather than pulling symbols one at a time. Using two end-of-day snapshots — one at the beginning of the observation window and one at the end — gives you enough information to calculate weekly performance across an entire market universe in a relatively clean workflow. Once the raw data is collected, the process becomes less about retrieval and more about refinement: narrowing the universe, removing lower-quality signals, and ranking the moves that actually matter.
That is where the FMP EOD Bulk API becomes useful operationally. Instead of stitching together hundreds of individual requests, the endpoint returns daily pricing data for all listed symbols in a single response for a chosen date. Before running the workflow, make sure your API key is active.
1. Pull Bulk EOD Prices for Both Anchor Dates
Begin by hitting the EOD Bulk endpoint twice — once for the close at the start of the window, once for the close at the end:
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https://financialmodelingprep.com/stable/eod-bulk?date=2024-10-22&apikey=YOUR_API_KEY |
Sample Response:
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[ { "symbol": "EGS745W1C011.CA", "date": "2024-10-22", "open": "2.67", "low": "2.7", "high": "2.9", "close": "2.93", "adjClose": "2.93", "volume": "920904" } ] |
The response includes standard OHLC pricing, adjusted close, and trading volume for every symbol available on the requested date. For pure weekly return calculations, the symbol and close fields are generally sufficient — or adjClose if you want performance adjusted for splits and dividends. The remaining fields become more useful later when introducing liquidity screens, minimum-price filters, or volatility checks.
2. Filter Down to Your Defined Universe
The raw dataset is intentionally broad. Running a movers screen directly against the entire feed usually produces noisy results dominated by illiquid names rather than meaningful institutional activity. A cleaner approach is to overlay a defined universe filter. In this example, the screen uses the S&P 500 constituent list:
For that filter, call the S&P 500 Index API endpoint:
https://financialmodelingprep.com/stable/sp500-constituent?apikey=YOUR_API_KEY
After pulling the constituent data, keep only the symbols that appear in both bulk EOD snapshots and the S&P 500 list. The same framework can easily be adapted elsewhere depending on the use case. A small-cap workflow might substitute the Russell 2000, a sector analyst could isolate semiconductor or energy names, and a global strategy desk could combine multiple regional indexes. The mechanics stay consistent — only the universe definition changes.
3. Join, Compute, and Rank
Once both filtered snapshots are prepared, the remaining work is mostly calculation and sorting. Join the datasets on symbol, then compute percentage performance across the window:
Weekly Performance % = (End Close − Start Close) / Start Close × 100
From there, rank the results separately by strongest gainers and largest decliners. Most workflows also apply additional cleanup filters before interpreting the output — minimum trading volume thresholds, exclusion of newly listed stocks, market-cap requirements, or other liquidity constraints intended to reduce statistical noise. The exact thresholds vary by strategy, but the objective is consistent: isolate moves that likely reflect meaningful positioning activity rather than unstable price behavior in thinly traded names.
At that stage, the screen becomes more than a leaderboard of weekly winners and losers. The combination of bulk pricing data, liquidity filtering, and universe control turns the output into a more structured view of where capital rotated during the week — highlighting names where both the magnitude of the move and the underlying trading quality justify deeper research attention.
From Daily Screening to Institutional Research Process
What begins as a useful desk-level screening process often becomes more valuable once it is standardized across a broader research organization. Weekly movers analysis is rarely just about identifying outperformers and underperformers in isolation; in institutional settings, the larger objective is building repeatable frameworks that multiple analysts, strategists, and portfolio teams can reference consistently across coverage areas.
That shift matters because fragmented workflows create interpretation drift. One analyst may screen using raw percentage moves, another may apply liquidity thresholds differently, while a third may exclude event-driven distortions altogether. Over time, inconsistent methodologies make cross-team comparisons less reliable and reduce confidence in how signals are being interpreted internally. Standardizing the workflow — from universe construction to filtering logic and ranking methodology — creates a cleaner foundation for collaborative research and internal decision-making.
This is where centralized data infrastructure becomes operationally important rather than merely convenient. Bulk APIs, standardized constituent datasets, and structured financial statement feeds allow research teams to work from the same underlying inputs instead of maintaining disconnected spreadsheets or manually assembled watchlists. Once workflows become shared internally, they are easier to audit, easier to reproduce historically, and easier to adapt across sectors, regions, or strategy groups without rebuilding the process from scratch each time.
In practice, that often leads to broader integration into internal dashboards, systematic monitoring tools, and recurring research pipelines. A desk tracking AI infrastructure momentum may layer movers data against earnings revisions and backlog growth, while another team focused on macro-sensitive sectors may connect the same framework to balance-sheet leverage or insider transaction activity. The underlying process stays consistent even as the analytical overlays evolve by mandate.
For firms moving beyond isolated analyst workflows, Financial Modeling Prep's Enterprise Plan becomes relevant less as a standalone product decision and more as infrastructure that supports shared research environments, larger-scale data pulls, governance controls, and cross-team consistency. At that point, the value is no longer simply speed of access — it is reducing workflow fragmentation while making internally distributed research easier to validate, compare, and operationalize across the organization.
What This Week's Tape Ultimately Revealed
This week's moves showed that the market was repricing execution quality, not simply rotating by sector. The FMP EOD Bulk API made that dispersion visible across the full screen, separating fundamentally supported gains from reactions shaped by demanding expectations.
If you found this useful, you might also like: Weekly Signals Desk | Five Notable Valuation Disconnects from the FMP API (July 20-24)
Disclosure: Signals Desk content is provided for informational and analytical purposes only and does not constitute investment advice or trade recommendations. The analysis reflects interpretation of market data and publicly disclosed or third-party information, including data accessed via Financial Modeling Prep APIs, at the time of publication. Signals discussed are probabilistic, can be wrong, and may change as market conditions and consensus data evolve. This content should be considered alongside broader research, individual objectives, and risk assessment.
