This week's tape wasn't defined by a single theme. Instead, the market delivered a sharp mix of AI enthusiasm, earnings-driven repricing, consumer weakness, and renewed pressure across parts of the energy complex. When leadership fragments like this, the biggest percentage movers often reveal more about where capital is rotating than the major indexes themselves.
Using FMP's EOD Bulk API, we screened weekly performance across a defined universe to identify the five S&P 500 stocks that generated the strongest directional signals during the week. Beyond the headline gains and losses, the exercise offers a structured view of how institutional positioning shifted beneath the surface, and later in this article, we'll walk through the EOD Bulk API workflow that makes this type of market-wide movers analysis possible.
Key Takeaways
- This week's biggest movers highlighted a growing market preference for measurable execution over broad thematic exposure, particularly across AI, consumer, and energy-related narratives.
- The strongest gains were concentrated in companies tied to infrastructure, manufacturing, and deployment activity, while the largest declines followed signs of weaker visibility around spending, demand, or growth expectations.
- Looking beyond price performance revealed a more nuanced story: identical-sized moves often reflected very different underlying drivers, ranging from earnings revisions and guidance changes to strategic positioning and capital allocation trends.
- Using the FMP EOD Bulk API as a starting point, a structured movers screen can help identify where conviction concentrated across the market and which developments warranted deeper fundamental research.
Five Names That Defined This Week's Tape
Accenture plc (ACN)
Weekly Performance: -24.84%
Accenture suffered the sharpest decline among this week's major movers after management trimmed its full-year revenue growth outlook, a development that quickly shifted attention away from the company's long-term AI narrative and toward the near-term demand environment. The reaction was notable not simply because of the size of the move, but because Accenture often functions as a proxy for broader enterprise technology spending. When one of the largest global consulting firms signals softer client activity, investors tend to view it as a read-through for corporate IT budgets more broadly. Recent commentary from the company pointed to weaker bookings and slower decision-making cycles, reinforcing concerns that portions of the services market remain cautious despite continued enthusiasm around artificial intelligence.
The more interesting signal may be what the decline says about current market expectations. Over the past year, many technology-related names benefited from AI-linked spending themes, but this week's reaction suggested investors are increasingly separating AI potential from near-term revenue visibility. Accenture remains deeply embedded in enterprise AI implementation projects, yet the market focused on growth guidance and demand trends rather than strategic positioning.
For analysts studying the move, backlog trends, bookings data, segment-level revenue growth, and geographic performance from the company's income statement and earnings disclosures provide a more complete picture than the share-price decline alone. Those datasets help distinguish whether weakness reflects a temporary slowdown in project timing or a broader moderation in client spending behavior.
SLB N.V. (SLB)
Weekly Performance: -14.40%
SLB's decline reflected a broader reassessment occurring across energy-related equities as investors weighed changing oil-market expectations. While the company remains one of the industry's dominant oilfield-services providers, service businesses sit downstream from commodity pricing. When expectations for exploration and production spending soften, service providers often absorb that change through lower activity levels, delayed projects, or slower contract awards.
This week's move came against a backdrop of shifting energy-market sentiment. Oil prices experienced significant volatility as traders evaluated geopolitical developments and the possibility of additional supply returning to global markets. At the same time, energy companies continue balancing growth plans with shareholder-return priorities, a dynamic that has generally produced more disciplined capital spending than in previous cycles.
What makes SLB particularly interesting is that the company has increasingly emphasized digital infrastructure, automation, and AI-driven energy solutions alongside its traditional service business. Days before the decline, management highlighted ambitions to substantially expand digital revenue by the end of the decade. The contrast between those long-term initiatives and the market's near-term concerns illustrates how investors currently appear more focused on immediate activity levels than strategic transformation efforts.
To evaluate the underlying signal, analysts would likely monitor segment revenue trends, operating margins, rig-count data, capital expenditure trends among major producers, and international versus North American exposure. Those datasets often provide earlier evidence of changing industry activity than commodity prices alone.
The Kroger Co. (KR)
Weekly Performance: -12.52%
Kroger moved sharply lower following its earnings release, despite reporting revenue that exceeded expectations. The company generated quarterly revenue of approximately $46.1 billion, while adjusted earnings came in near expectations. Yet the market response suggested investors were more concerned with consumer behavior trends and margin pressure than headline revenue performance.
The earnings report highlighted an increasingly selective consumer environment. Management noted that shoppers remain value-focused, purchasing more promotional items and demonstrating continued sensitivity to food inflation and household budgets. For retailers, that distinction matters. Revenue growth can remain stable while profitability becomes more difficult to sustain if customers trade down, buy fewer discretionary items, or respond aggressively to discounting efforts.
Viewed through a broader lens, Kroger's decline was less about a single earnings report and more about what the results revealed regarding consumer spending quality. Many investors have spent the past year searching for evidence of whether consumer demand remains resilient beneath the surface. Grocery retailers occupy a unique position in that discussion because they capture purchasing behavior across income groups and product categories.
To deepen the analysis, same-store sales metrics, gross-margin trends, inventory data, loyalty-program statistics, and management guidance from earnings filings provide particularly useful context. Together, those datasets help determine whether changing consumer behavior is affecting volumes, pricing power, or both.
Marvell Technology, Inc. (MRVL)
Weekly Performance: +11.04%
Marvell emerged as one of the strongest performers of the week after reports that Amazon plans to make its Trainium AI chips available to external customers. The development renewed attention on the expanding AI infrastructure ecosystem and highlighted the growing importance of custom silicon providers operating alongside the industry's largest cloud platforms.
The significance extends beyond a single news headline. Markets have increasingly rewarded companies positioned within AI infrastructure supply chains, but investors are also becoming more selective about where value accrues. In Marvell's case, the company's exposure to custom chips, networking infrastructure, and data-center connectivity places it close to several of the highest-growth areas within AI deployment. The week's advance reflected renewed confidence in those underlying themes rather than a standalone earnings event.
What stands out from a signal perspective is how quickly capital continues to concentrate around infrastructure enablers whenever evidence emerges that AI adoption is broadening. Rather than focusing solely on end-user applications, investors remain highly attentive to companies supplying the hardware and networking layers required to support expanding workloads.
For additional context, analysts would likely examine data-center revenue growth, customer concentration metrics, segment disclosures, capital-spending trends among hyperscalers, and analyst-estimate revisions. Those datasets provide a clearer framework for evaluating whether enthusiasm is being matched by measurable business activity.
Intel Corporation (INTC)
Weekly Performance: +7.56%
Intel gained ground after reports tied the company to potential domestic manufacturing cooperation involving Apple and U.S. semiconductor production initiatives. The move added another chapter to an ongoing market debate surrounding Intel's role in the effort to expand advanced semiconductor manufacturing capacity within the United States.
Unlike many semiconductor rallies driven primarily by product launches or earnings surprises, this move centered more on strategic positioning. Intel occupies a unique place within the industry because investors evaluate the company through multiple lenses simultaneously: chip design competitiveness, foundry expansion, government-supported manufacturing initiatives, and broader supply-chain resiliency efforts. News affecting any one of those areas often influences perceptions of the entire investment case.
The stock's gain also illustrated how sensitive semiconductor names remain to developments connected to domestic production and AI infrastructure buildouts. While the immediate catalyst attracted attention, the larger question remains operational execution across manufacturing, customer acquisition, and capital allocation—areas that investors have monitored closely for several years.
For analysts examining the signal, foundry revenue disclosures, capital expenditure trends, customer announcements, government incentive programs, and balance-sheet metrics are particularly informative. Those datasets offer a more durable framework for assessing strategic progress than short-term price movements driven by headlines alone.
What the Week's Movers Actually Signaled
Viewed individually, this week's biggest movers appeared to be driven by unrelated catalysts: a consulting giant cutting guidance, an oilfield-services leader caught in shifting energy sentiment, a grocery chain navigating consumer behavior changes, and two semiconductor names benefiting from AI and domestic manufacturing narratives. Taken together, however, a more coherent pattern emerges.
The common thread was not sector leadership: it was investor sensitivity to evidence. Across multiple industries, the market rewarded companies tied to measurable infrastructure, deployment, and capacity-building themes while showing less tolerance for businesses where forward visibility became less certain. Marvell and Intel benefited from developments connected to AI infrastructure and semiconductor manufacturing, both areas where spending commitments remain tangible and easier to track. By contrast, Accenture, Kroger, and SLB faced scrutiny tied to demand signals, spending behavior, and activity levels that require greater interpretation and carry more uncertainty.
That distinction matters because it suggests the market is currently placing a premium on observable execution metrics rather than broad thematic exposure alone. Simply being associated with AI, consumer resilience, or energy demand was not enough this week. Investors appeared more focused on where those narratives translated into identifiable business activity, customer commitments, or capital allocation decisions.
A deeper review of the underlying data reinforces that interpretation. Price performance alone captures the outcome, but understanding the signal often requires connecting several layers of information. A weekly movers screen may identify where attention concentrated, but combining analyst estimate revisions, earnings history, ownership data, and financial statements often reveals why. Working across datasets available through FMP allows researchers to compare price reactions against underlying business trends rather than treating market moves as isolated events. Revenue growth and margin data can help separate earnings-driven repricing from narrative-driven momentum, while cash flow trends provide additional context around whether investment themes are translating into measurable operating performance.
The same framework becomes even more informative when insider trading activity, institutional ownership changes, and earnings-surprise history are added to the analysis. A stock posting a large weekly move carries a different signal if executives are accumulating shares, analysts are revising forecasts higher, and operating cash flow is improving simultaneously. Conversely, sharp price reactions that occur without corresponding changes in underlying business metrics often warrant a more cautious interpretation.
In that sense, this week's screen was less about identifying winners and losers than observing where conviction concentrated across the market. The largest moves highlighted the ongoing tension between narrative and execution - a theme that continues to shape capital allocation decisions across technology, consumer, industrial, and energy sectors. The companies changed, but the underlying message was familiar: investors are increasingly looking for evidence that a story is becoming measurable.
Building a Weekly Movers Screen 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:
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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 Analyst Workflow to Standardized Research Framework
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
The week's biggest movers underscored a market that remains highly selective, rewarding measurable execution while quickly repricing uncertainty around growth, spending, and demand visibility. Using the FMP EOD Bulk API as a starting point, the underlying pattern was less about sector leadership and more about where conviction concentrated when new information entered the market.
If this week was any indication, the most informative signals were not found in the indexes themselves, but in the individual companies where expectations were forced to adjust.
If you found this useful, you might also like: Weekly Signals Desk | Price-Target Gaps Identified via the FMP API (June 8-12)
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.
