Signals Desk Weekly Take via FMP API | Five Biggest Stock Movers (Oct 19-23)

Thursday did most of the work this week. A renewed scare over whether AI spending converts to cash sent the chip index sharply lower, and Treasuries had their strongest session in more than two years, pulling the 10-year yield back below 5%. Capital that left the AI trade went looking for rate-sensitive and defensive homes, and the screen of the S&P 500 and S&P MidCap 400 reflects that rotation as clearly as it reflects earnings. RH, Qualys and Hasbro led the gainers; Carpenter Technology and Corcept Therapeutics posted the steepest declines.

What makes this week's five useful is that each move had a different source: a rate trade, a sector rotation, a clean earnings beat, an expectations reset and a competitor's clinical readout. This edition works through each one and then shows how to rebuild the screen with the FMP EOD Bulk API, comparing fixed-date closing snapshots across a defined universe to rank the largest gainers and decliners.

Key Takeaways

• Two of the three gainers had no company-specific catalyst, which suggests the week's leadership owed as much to falling yields and rotation out of AI hardware as to individual news.
• Hasbro was the exception on the upside, with a raised outlook and a sharp margin gain giving its move a fundamental base that the rotation trades lack.
• Carpenter Technology fell roughly a quarter despite record operating income and an EPS beat, a reminder of how little room a premium valuation leaves for a revenue shortfall.
• Corcept's decline came from another company's data, showing that the largest single-stock risks can sit entirely outside a firm's own disclosures.

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The Five Names That Set This Week's Tape

RH (RH)

Weekly Performance: +21.63%

RH climbed on every session of the week, with the largest gain arriving on Thursday as Treasury yields fell sharply and homebuilders and housing-linked retailers caught a bid. No company release accompanied the move. The luxury home furnishings retailer has spent much of the past year near the bottom of its range, weighed down by a frozen housing market, high mortgage rates and tariff costs on imported goods, which made it one of the more rate-sensitive names in the universe when the bond market turned.

That context matters for interpretation. A move driven by a change in the discount rate and housing sentiment is different from one driven by a change in RH's own trajectory, and the stock gave back a little on Friday as the rally cooled. The relevant question is whether the operating data begins to confirm what the price is anticipating. FMP's Treasury Rates API provides the yield series that the stock appears to be trading against, and pairing it with gross and operating margin trends from the Income Statement API ahead of RH's next report helps separate a rate-driven rebound from an improvement in the business itself.

Qualys Inc. (QLYS)

Weekly Performance: +19.69%

Qualys rose steadily through the week without a company-specific announcement, and the gain lines up with a broader move back into software. Salesforce's agreement to acquire Braze on Tuesday put a fresh valuation marker on application software, and Thursday's rotation out of AI hardware sent capital toward software names that had lagged the semiconductor trade for much of the year. Qualys, a cloud-based security and compliance vendor with high margins and steady cash generation, fits the profile of a quality name that had been left behind rather than one with a new growth story.

The cleanest way to read the move is relative rather than absolute. If software outperformance persists, it would suggest a broader reallocation; if it fades, the week will look more like a positioning flush. FMP's Historical Sector Performance API shows whether the technology sector's internal mix shifted toward software, while the Key Metrics TTM API puts Qualys' free cash flow yield and valuation multiples in context against its own history ahead of third-quarter results.

Hasbro Inc. (HAS)

Weekly Performance: +18.91%

Hasbro supplied the clearest fundamental case on the leaderboard. Third-quarter revenue rose 15% to $1.6 billion and adjusted operating profit climbed more than 50%, led by Magic: The Gathering, which crossed $600 million in quarterly revenue. Management raised its full-year revenue growth outlook to 7% to 9% at constant currency from 5% to 7%, lifted its margin and EBITDA targets, and doubled its share repurchase authorization. Most of the weekly gain arrived on the day of the release.

The signal here is mix. Hasbro's Wizards of the Coast and digital gaming business carries substantially higher margins than traditional toys, and its growing share of revenue is changing the company's earnings profile. The variables to monitor are whether Magic's growth reflects a durable expansion of the player base or a strong release calendar, and how consumer products perform through a holiday season shaped by tariff-driven price increases. FMP's Revenue Product Segmentation API separates the Wizards contribution from consumer products and entertainment, which is the most direct way to see whether the margin gains are structural.

Carpenter Technology Corporation (CRS)

Weekly Performance: -24.51%

Carpenter fell about 21% on Thursday after reporting fiscal first-quarter results that, on most measures, were strong. Operating income reached a first-quarter record, up 28% from a year earlier, adjusted operating margin in the Specialty Alloys segment was 37.7%, EPS beat consensus, and management described accelerating demand and rising bookings across the commercial aerospace supply chain. Sales, however, came in below expectations, partly reflecting lower production time from planned maintenance outages.

For a stock that had risen sharply on the aerospace recovery and was carrying a premium multiple, a revenue miss was enough to trigger a reset, and the same session's broad selloff in AI and industrial momentum names likely amplified the reaction. The question is whether the shortfall was a timing issue tied to maintenance or a sign that volume growth is lagging pricing gains. FMP's Earnings Report API places the EPS beat and revenue miss in the context of Carpenter's recent surprise history, and the Enterprise Values API shows how much of the prior valuation premium the decline has removed.

Corcept Therapeutics Incorporated (CORT)

Weekly Performance: -22.06%

Corcept's decline was concentrated in the final two sessions, with the heaviest loss on Friday after Genmab reported that its Phase 3 trial of rinatabart sesutecan met its primary progression-free survival endpoint and a key overall survival endpoint in platinum-resistant ovarian cancer. That is the same setting in which Corcept's relacorilant, marketed as Lifyorli, gained FDA approval earlier this year, and the market moved quickly to price in a stronger competitor for a product central to Corcept's growth outside its established Cushing's syndrome franchise.

The move is a case of read-across risk: nothing in Corcept's own disclosures changed, but the expected commercial runway for one of its key products did. How large that effect turns out to be depends on how the two therapies are positioned in practice, including differences in mechanism, safety profile and line of therapy. FMP's Financial Estimates API is the place to watch whether analysts reduce revenue expectations for coming years, and comparing those revisions with the company's third-quarter results will show whether the market's reaction runs ahead of any change in the numbers.

What Sits Beneath This Week's Biggest Moves

The week's leaderboard carries an unusual amount of macro information. RH and Qualys rose without company news, on the same session that Treasuries rallied and money moved out of AI hardware and into housing-linked, software and defensive names. Hasbro was the only gainer with a full earnings explanation. On the downside, Carpenter showed how little tolerance remains for any shortfall in a richly valued stock, while Corcept was repriced on someone else's data. Taken together, the pattern suggests a market that was reallocating as much as it was reacting, with the source of each move worth identifying before drawing conclusions from its size.

That distinction has a practical edge. A weekly return ranking treats a rate-driven rebound and an earnings-driven rerating as equivalent, when the first can unwind as quickly as yields reverse and the second is anchored in reported numbers. The data suggests that sorting movers by catalyst type, including earnings, macro, sector flow and external events, makes the screen considerably more informative than ranking by magnitude alone.

Building that layer requires connecting price data to several other datasets, and the joins are simpler when they share a common source. Within the FMP data environment, EOD Bulk snapshots can be matched against the Earnings Surprises Bulk API to flag which movers reported during the window, while the Treasury Rates API and Historical Sector Performance API identify moves that coincide with rate shifts or sector rotation rather than company events. For names that did report, the Income Statement API and Financial Estimates API show whether the price reaction is tracking a change in fundamentals or a change in expectations.

The final step is follow-through. Rotation-driven moves tend to be tested by the next macro print, and with a Federal Reserve decision on October 28 and oil above $110, this week's rate-sensitive gainers face an early test. Earnings-driven moves are tested by the next quarter. Tracking both groups over subsequent weeks is what turns a weekly screen into a record of which kinds of moves have tended to hold.

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:

https://financialmodelingprep.com/stable/eod-bulk?date=2024-10-22&apikey=YOUR_API_KEY

Sample Response:

[ { "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 Tape Left Behind

This week's leaderboard was written as much by the bond market and sector flows as by company results, and that mix is worth recognizing before treating any of the moves as a verdict. Run consistently through the FMP EOD Bulk API, the screen builds a running record of which kinds of moves hold and which fade once the macro backdrop shifts.

If you found this useful, you might also like: Weekly Signals Desk | Five Notable Valuation Disconnects from the FMP API (Oct 12-16)

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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.