One revised takeover offer, one regulatory decision and one strong earnings report account for three of this week's five largest moves. Synaptics led the S&P 500 and MidCap 400 after onsemi switched its offer to all cash, Carnival rallied on record results, and Fair Isaac lost close to a quarter of its value after the housing regulator opened mortgage pricing to a competing score. Viavi rounded out the gainers on a run with no single trigger, while AppLovin extended a steady slide to a new 52-week low.
This edition walks through what drove each of the five and then shows how the FMP EOD Bulk API can rebuild the screen from two fixed closing snapshots, including how to catch corporate actions that distort raw weekly returns.
Key Takeaways
- Synaptics' gain reflects a change in deal structure rather than a change in its business: shareholders now hold a fixed cash value instead of exposure to the acquirer's share price.
- Fair Isaac's decline is the clearest example in the screen of a regulatory decision reaching directly into a company's most profitable revenue line.
- Viavi's advance came without a company-specific catalyst and coincided with gains across optical networking peers, which points to sector positioning rather than new information.
- A spin-off produced the largest raw decline in the universe this week, a reminder that weekly return screens need a corporate-action filter before the rankings are read.
Five Moves That Shaped This Week's Tape
Synaptics Incorporated (SYNA)
Weekly Performance: +17.89%
Synaptics posted the week's largest gain, most of it on Friday, after onsemi revised its pending acquisition from an all-stock transaction to an all-cash offer at $123 per share. The headline deal value is lower than the original stock-based figure, but the structure matters more for Synaptics holders. Under the earlier terms, their payout moved with onsemi's share price; under the new terms, it is fixed. onsemi's own shares also rose, as the change removes dilution for its holders and, by the company's account, makes the deal accretive on closing.
With the stock now trading close to the offer, the analytical focus shifts from Synaptics' operating results to deal mechanics. The transaction still requires Synaptics shareholder approval and is expected to close in mid-2027, which leaves a long window for regulatory review. FMP's Search Mergers & Acquisitions API holds the transaction record, and comparing the share price with the $123 offer over the coming months would show how much completion risk the market is assigning to a long timeline.
Viavi Solutions Inc. (VIAV)
Weekly Performance: +15.78%
Viavi's gain extended a winning streak that has stretched across roughly two and a half weeks, with the largest moves coming on Thursday and Friday. Unlike the other two gainers, there was no single announcement behind it. The stock has been treated increasingly as part of the AI infrastructure trade since its addition to the S&P MidCap 400 earlier this year, and revenue growth has been strong, although operating margins remain well below those of larger technology peers.
The context is the clearest signal. Lumentum, Coherent and Fabrinet, all tied to optical networking for data centers, posted double-digit gains in the same week, which suggests capital was moving into the theme broadly rather than reacting to Viavi-specific news. FMP's Industry Performance Snapshot API makes it possible to compare Viavi's move against its industry on the same dates. If the stock continues to track its peers closely, its price is likely to stay sensitive to sentiment around AI spending as a whole, rather than to its own reported results.
Carnival Corporation & plc (CCL)
Weekly Performance: +15.78%
Carnival matched Viavi's gain to within a hundredth of a percentage point, with most of the move coming on Tuesday after its fiscal third-quarter report. The company delivered record revenue and yields, beat its own guidance, raised its full-year adjusted EPS outlook and said bookings for 2027 remain at record levels. Higher fuel costs were the main offset. The stock had fallen sharply over the preceding weeks on concern about pricing and capacity in the Caribbean, so the report addressed the specific worries that had been weighing on the group.
The reaction spread beyond Carnival. Royal Caribbean also gained substantially over the week, in part because the print eased concerns about sector pricing that analysts had flagged. The question now is whether 2027 pricing holds as more capacity arrives. FMP's Earnings Transcript API provides management's commentary on booking curves, onboard spending and fuel hedging from call to call, which tends to reveal shifts in demand before they appear in reported yields.
Fair Isaac Corporation (FICO)
Weekly Performance: -23.39%
Fair Isaac suffered the week's largest economic decline after the Federal Housing Finance Agency announced at the start of the week that Fannie Mae and Freddie Mac will adopt a unified pricing model for mortgages that incorporates VantageScore, the credit score jointly developed by the three major credit bureaus. The shares fell by roughly a quarter on Tuesday and did not recover through the end of the week. The decision builds on earlier steps by the regulator to open mortgage underwriting to alternative scores, but this is the first to tie it directly to how loans are priced.
The scores business, and mortgage origination in particular, has been the main source of Fair Isaac's margin expansion in recent years, driven largely by pricing. A credible competing score in pricing models directly challenges that leverage. FMP's Revenue Product Segmentation API separates the Scores segment from the Software business, and following mortgage-related scores revenue over the next several quarters will show how quickly, if at all, lenders change their behavior once the new framework takes effect.
AppLovin Corporation (APP)
Weekly Performance: -13.69%
AppLovin's decline was steady rather than sudden, extending a losing streak that pushed the shares to a new 52-week low midweek. The most visible development was the company's lawsuit against Unity, which alleges that a competing ad quality product improperly collected data to feed models used in mobile ad auctions. Unity's public response framed the suit as an incumbent reacting to rising competition. The stock now trades well below its longer-term moving averages after a sharp run in prior years.
The litigation itself is unlikely to resolve quickly, so its importance lies in what it implies about competitive intensity in mobile advertising, which has been the foundation of AppLovin's high margins. FMP's Price Target Summary API shows how many analyst targets were published recently and at what average level, which helps determine whether coverage is lowering expectations alongside the share price or treating the decline as sentiment-driven. A widening gap between the two would itself be informative.
What Separates a Repricing From a Re-rating This Week
The five moves sort into two groups. Synaptics and Fair Isaac were repriced by external decisions: an acquirer changed its offer, and a regulator changed the rules for the most important market for Fair Isaac's product. Carnival, Viavi and AppLovin moved on information about their own trajectory or their sector's, whether through a strong report, thematic positioning or litigation that hints at competitive pressure. The first group's price changes are largely settled by the event; the second group's will be tested by subsequent results.
The screen also produced a useful caution. Corteva showed a weekly decline of more than 80%, larger than any genuine move in the universe, because it completed the spin-off of its seed business, now trading as Vylor, on Thursday. The price reset reflects shareholders receiving a separate stock, not a loss of value. Without a corporate-action check, it would have topped the decliners list.
That kind of check is easy to build into the workflow using the broader FMP data set. Once the EOD Bulk API has ranked the week's moves, the Dividend Adjusted Price Chart API helps confirm whether a price change has been adjusted for distributions, and the Stock News API identifies which moves were driven by spin-offs, mergers or regulatory decisions. The Search Mergers & Acquisitions API adds deal context for names like Synaptics, where the price is now anchored to an offer.
Sector context completes the picture. The Industry Performance Snapshot API shows whether a gain such as Viavi's reflects stock-specific information or a broader industry move, while the Financial Estimates API indicates whether analysts are revising forward numbers in response to events like Fair Isaac's. Combined, these datasets turn a weekly leaderboard into an explanation of which moves represent a change in fundamentals and which simply reflect the market adjusting to a single decision.
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 the Week's Leaderboard Actually Recorded
Most of this week's largest moves came from decisions made outside the companies themselves, by an acquirer, a regulator or a spin-off schedule. Two closing snapshots from the FMP EOD Bulk API capture the size of those moves; separating events that are already priced from those that will play out over time is the part that requires further work.
If you found this useful, you might also like: Weekly Signals Desk | Price-Target Gaps Identified via the FMP API (Sept 21-25)
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.
