Signals Desk Weekly Take via FMP API | Five Biggest Stock Movers (Oct 26-30)

For most of the past two years, AI exposure has been treated as a reason to own a stock. This week the market applied the opposite lens. The two largest decliners in the S&P 500 and MidCap 400 universe, ServiceNow and Reddit, were sold on concerns that AI could erode the business models behind them, through software automation in one case and changes in search traffic in the other. On the other side of the ledger, the largest gains came from businesses with little direct AI exposure: a footwear company that beat and raised, a restaurant chain whose results were less bad than feared, and a natural gas producer agreeing to be acquired.

This edition reviews the five moves and then shows how to reproduce the screen using the FMP EOD Bulk API, including how to anchor two fixed-date snapshots, apply a universe filter, and rank the week's largest gainers and decliners.

Key Takeaways

• ServiceNow and Reddit fell by roughly a third each, and in both cases the selling focused on how AI might reshape demand rather than on the quarter just reported.
• Crocs and Wingstop rallied on results that cleared low expectations, a reminder that weekly moves often reflect the gap between results and positioning rather than the absolute quality of the numbers.
• Range Resources' gain was deal-driven: an all-stock agreement with EQT ties its price to the acquirer's shares and to sentiment around natural gas.
• The EOD Bulk API identifies where prices moved; estimates, segment data and transcripts help explain whether the market's reassessment reflects a lasting change or a reaction to a single event.

Subscribe to FMP on Substack

The Five Moves That Shaped the Week

Crocs, Inc. (CROX)

Weekly Performance: +26.24%

Crocs posted the week's largest gain after a third quarter that beat expectations and a full-year outlook raised for the second time this year. Revenue growth was modest, but adjusted margins and earnings per share moved sharply higher, and the HEYDUDE brand returned to growth after a long period of decline. Management also lifted its adjusted EPS guidance and guided to faster revenue growth in the fourth quarter.

The details show why the reaction was so strong and also where it needs testing. Growth in the core Crocs brand came almost entirely from international markets and direct-to-consumer sales, while North America declined. Part of the per-share improvement reflects aggressive buybacks that reduced the share count by roughly a tenth over the past year, and GAAP results were lifted by a one-time tariff refund. The quality question, then, is whether international momentum and the HEYDUDE turnaround can offset softness in the largest market.

The data suggests this may be an area to monitor through regional and brand-level revenue trends rather than headline EPS. FMP's Revenue Geographic Segments API would show whether international growth continues to carry the brand, and the Cash Flow Statement API helps separate free cash flow generation from the effect of repurchases on per-share figures.

Wingstop Inc. (WING)

Weekly Performance: +25.26%

Wingstop's rally came despite results that, on the surface, were weak. Domestic same-store sales declined, management lowered its full-year comparable sales outlook and trimmed planned restaurant openings, and fourth-quarter marketing spend is expected to weigh on profitability. Earnings per share still rose modestly, helped by unit growth and buybacks.

The reaction says more about positioning than about the quarter. The shares had been under heavy pressure for months as same-store sales turned negative, and the latest update appears to have been read as clarifying the extent of the slowdown rather than worsening it. Management also highlighted improving purchase frequency among loyalty program members, which gave investors a data point on the strategy for rebuilding traffic. The pattern resembles a reset in expectations more than evidence that the slowdown has ended.

The next tests are whether comparable sales stabilize and whether new restaurants continue to open at attractive returns while existing locations slow. FMP's Earnings Report API places upcoming results against reduced expectations, and the Key Metrics TTM API tracks valuation and return measures as the unit base expands.

Range Resources Corporation (RRC)

Weekly Performance: +20.01%

Range Resources rose after agreeing to be acquired by EQT in an all-stock transaction, with Range shareholders set to receive just under one EQT share for each share held. The combination would create the largest natural gas producer in the United States, with a combined enterprise value above $50 billion, and management expects several hundred million dollars in annual cost synergies within two years. Closing is targeted for the first quarter of 2027.

Because the consideration is entirely in stock, Range's price will now track EQT's shares and the market's view of the combined company, rather than Range's own operating results. That shifts the relevant analysis to deal risk, including shareholder and regulatory approvals, and to natural gas fundamentals that drive EQT's valuation. The deal also adds to the consolidation trend in Appalachian gas, where scale and pipeline access have become central to returns as LNG export and power demand develop.

For readers tracking the transaction, FMP's Search Mergers & Acquisitions API records deal terms, while the Commodities Quote API provides natural gas price context that will influence the value of the stock consideration through closing.

Reddit, Inc. (RDDT)

Weekly Performance: -36.45%

Reddit's decline came almost entirely in the final session of the week, after third-quarter results combined strong revenue growth with clear weakness in user metrics. U.S. daily active users fell, and management warned that global users could decline meaningfully in the fourth quarter if search referrals continue to drop. The company plans to test a logged-in experience to reduce its dependence on search traffic.

The scale of the move shows how quickly the market can re-weight a single assumption. Monetization remains strong, and fourth-quarter guidance points to continued high revenue growth and wider margins. But valuation had been built on a combination of growing users and rising revenue per user, and the update put the first of those in doubt. With search engines increasingly answering questions directly through AI-generated summaries, the debate has moved from how well Reddit monetizes its audience to how durable that audience is. Four analyst downgrades followed, covered in this week's revision screen.

User trends by geography and the cost of the logged-in transition are the variables to monitor. FMP's Earnings Transcript API captures management's explanation of traffic sources, and the Financial Estimates API shows how quickly forward revenue and earnings expectations are being adjusted.

ServiceNow, Inc. (NOW)

Weekly Performance: -33.87%

ServiceNow fell after third-quarter results that showed solid subscription growth and revenue slightly ahead of consensus, but a modest earnings shortfall and fourth-quarter guidance below expectations. Selling continued in the following session, extending the decline to roughly a third of the stock's value over the week.

The reaction is out of proportion to the size of the miss, which points to a broader concern. ServiceNow sells workflow software priced largely by seat, and the market is increasingly questioning whether AI agents that automate tasks could reduce the number of users enterprises need to license. A softer guide, even a modest one, gave that concern a data point. The result is a repricing of the long-term growth assumption rather than a reaction to one quarter's earnings.

The data suggests this may be an area to monitor through renewal rates, remaining performance obligations, and the mix between seat-based and consumption-based pricing. FMP's Financial Estimates API shows how far long-term revenue forecasts have moved, and the Price Target Consensus API indicates how much analysts have reset valuations relative to the new share price.

What Sits Beneath This Week's Dispersion

The common thread across the two largest declines is that the market sold stories about the future, not the quarter. Both ServiceNow and Reddit reported revenue growth that would ordinarily be considered healthy. What changed was confidence in a structural assumption, enterprise seat growth for one and search-driven user acquisition for the other, and in both cases AI was the variable behind the doubt. That is a different kind of risk from an earnings miss because it is harder to disprove with a single good quarter.

The gainers point to the inverse pattern. Crocs and Wingstop moved on results that cleared a low bar; neither delivered a clean story, but both reduced uncertainty. Range Resources moved on a corporate event that replaced operating risk with deal risk. Taken together, the week suggests that clarity was rewarded more than growth, and that uncertainty around long-term demand was penalized heavily even when current numbers held up.

A price screen identifies where expectations moved, but not whether the reaction fits the underlying change. Within the FMP data ecosystem, the weekly return from EOD Bulk data can be compared with the Earnings Surprises Bulk API to see how much of the move followed an actual beat or miss. The Financial Estimates API then shows whether analysts are revising long-term forecasts, the key question for ServiceNow and Reddit, or simply the next quarter.

For structural debates, qualitative data adds a further layer. The Earnings Transcript API lets analysts compare how management teams describe AI's effect on demand, while Revenue Product Segmentation data indicates whether specific product lines are slowing. When those signals align with the price move, the repricing is more likely to reflect a durable change in expectations; when they diverge, the weekly move may say more about positioning than fundamentals.

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.

Get your free API key

The Week's Price Action in Context

This week the market drew a sharp line between uncertainty it could size and uncertainty it could not, and AI sat on the wrong side of that line for two of the largest names in the screen. The FMP EOD Bulk API makes that split visible across the full universe, giving each move a starting point for deeper analysis.

If you found this useful, you might also like: Signals Desk Weekly Take via FMP API | Five Biggest Stock Movers (Oct 19-23)

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.