Signals Desk Weekly Take via FMP API | Five Biggest Stock Movers (Oct 12-16)

The semiconductor index closed lower for an eighth straight session on Friday, a losing streak the group had not seen since 2008, and the damage this week reached well beyond the largest chipmakers. Inside the S&P 500 and MidCap 400, at least a dozen names tied to optical networking, power delivery, AI servers and cloud infrastructure fell more than 24% between the Friday closes. Meanwhile, the stocks at the top of the table had almost nothing to do with AI: two credit-scoring companies and a mall-based fragrance retailer that had spent much of the year near its lows.

That split is the story of the week. Using the FMP EOD Bulk API, this edition ranks the largest gainers and decliners from the October 9 close to the October 16 close, examines what sat behind Bath & Body Works, Fair Isaac, TransUnion, Vicor and Semtech, and shows how to rebuild the screen from two end-of-day snapshots.

Key Takeaways

• The two largest decliners were AI power and connectivity suppliers whose selling continued a broader de-risking of AI hardware, with higher oil prices and long-term yields adding pressure on richly valued growth names.
• Fair Isaac and TransUnion rose together even though they sit on opposite sides of the credit-score competition in mortgage lending, which points to a sector-wide bid rather than a clear winner in that debate.
• Bath & Body Works led the gainers without a fresh company announcement, a move consistent with valuation-driven buying in a stock that had fallen to a very low earnings multiple.
• Several of these moves came ahead of earnings reports later in October, so the next set of results will show whether operating data supports or contradicts this week's price action.

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Five Moves That Shaped This Week's Market

Bath & Body Works, Inc. (BBWI)

Weekly Performance: +16.15%

Bath & Body Works posted the largest gain in the screen, and the most notable feature of the move is that it arrived without a new earnings release, guidance change or corporate announcement. Most of the advance came in the middle of the week, with the largest single-day jump on Thursday. The stock had fallen sharply this year and was trading at a low single-digit multiple of earnings, and analysts had been nudging targets higher after a wave of bearish calls in September. In that setting, even modest shifts in sentiment can move the shares meaningfully.

The question is whether the rally reflects a change in fundamentals or simply a reset in how much pessimism was priced in. The company's Consumer First transformation plan is aimed at reviving traffic and product newness, while heavy debt and margin pressure remain the main concerns. With the holiday quarter accounting for a large share of annual profit, the next earnings report and holiday commentary are the natural checkpoints.

FMP's Key Metrics TTM API is a practical starting point here, since it shows how valuation, free cash flow yield and leverage compare with the company's own history. Pairing that with the Financial Estimates API would reveal whether analysts are lifting earnings forecasts or only their targets, a distinction that separates a sentiment bounce from a genuine change in the earnings outlook.

Fair Isaac Corporation (FICO)

Weekly Performance: +15.52%

Fair Isaac's gain was largely a recovery from a steep sell-off in late September, when the federal housing regulator moved to put VantageScore 4.0 on pricing parity with classic FICO scores at Fannie Mae and Freddie Mac. This week brought evidence pointing the other way. On Monday, the largest US mortgage lender said it would keep FICO scores on broker credit pulls and add FICO Score 10T, and the stock jumped roughly 11% that day. A second leg higher came on Thursday as credit-reporting names rallied together.

The broader debate has not been settled. The regulatory change still allows lenders to use a competing score, and several analysts trimmed targets after the September decline while keeping positive ratings. The company has also announced a workforce reduction as it reorganises around AI-driven products. This week's move shows how sensitive the stock is to individual lender decisions, which makes mortgage-channel adoption the variable to track.

The Revenue Product Segmentation API is the most direct lens, separating the Scores segment, where mortgage pricing sits, from the Software business. Running that alongside FMP's Stock Grades API helps show whether analyst sentiment is stabilising after the September target cuts or continuing to drift lower as lenders make their choices.

TransUnion (TRU)

Weekly Performance: +13.13%

TransUnion's move came almost entirely on Thursday, when the stock rose more than 10% from a level close to its 52-week low. The company launched a personalised credit offer service that day, combining a long-standing lender marketplace partner's platform with TransUnion credit data inside its consumer app. Earlier in the week, a large mortgage lender began using VantageScore 4.0, the score TransUnion co-owns with the other two bureaus. Equifax also rose more than 10% over the week, which suggests the buying extended across the credit bureau group rather than resting on a single announcement.

What makes this pairing interesting is that TransUnion and Fair Isaac sit on opposite sides of the mortgage scoring debate, yet both rallied in the same week. That pattern suggests investors were adding exposure to credit activity broadly, with consumer lending and mortgage volumes as the common thread, rather than picking a winner in the scoring contest. TransUnion reports third-quarter results later this month, which will provide a read on volume trends across its lending verticals.

The Earnings Report API provides a quick view of how often TransUnion has beaten estimates in recent quarters, a useful baseline ahead of the report. For a broader read on the group, FMP's Industry Performance Snapshot API can show whether this week's strength was confined to credit services or part of a wider rotation into financial data companies.

Vicor Corporation (VICR)

Weekly Performance: -28.03%

Vicor was the largest decliner in the screen, and the selling was heaviest on Wednesday through Friday, when the stock fell on three consecutive sessions. There was no major company-specific announcement behind the move. Vicor's high-density power modules are tied closely to AI server and accelerator demand, and that exposure has made it one of the more volatile names in the AI hardware complex. As the broader semiconductor group declined for eight straight sessions, stocks with concentrated AI exposure and elevated valuations absorbed the sharpest losses.

The context matters for interpretation. This week's selling was driven by doubts about how AI infrastructure build-outs will be financed, an unwind of a crowded trade, and a rise in oil prices that pushed long-term yields higher and pressured long-duration growth stocks. That makes Vicor's decline as much a function of positioning and macro conditions as of anything specific to its own operations. This was also the second week in a row that Vicor ranked among the largest decliners in the screen.

With third-quarter results due shortly, the Income Statement API is useful for tracking whether revenue and gross margin trends from its AI-related product lines are holding up despite the share price weakness. The Earnings Transcript API offers a way to follow how management describes order visibility and customer concentration from quarter to quarter.

Semtech Corporation (SMTC)

Weekly Performance: -27.00%

Semtech's decline had a clearer trigger. The company held an analyst day on Thursday at which it set long-term targets for fiscal 2030 revenue and earnings and raised its outlook for the current quarter. Despite that, the stock fell about 10% on the day. Analysts pointed to an implied slowdown in growth in the later years of the plan and limited detail on its copper interconnect products for AI data centres. One firm described the reaction as a reset in growth expectations rather than a change in the investment case.

The timing amplified the reaction. Semtech had roughly doubled over the past year on its exposure to AI connectivity, and long-term targets that would have been well received in a calmer market were scrutinised more harshly in a week when AI hardware was under broad pressure. Analyst targets were mixed around the event, with some raised beforehand and at least one lowered, which suggests the debate is now about the slope of growth rather than its direction.

The Financial Estimates API is the most useful tool here, showing whether analysts adjust forward revenue and EPS assumptions after the analyst day or leave them broadly intact. If estimates hold steady while the share price resets, the move reflects valuation compression rather than a change in the earnings path, a distinction that matters for reading what comes next in the data.

What the Tape Said About Where Capital Moved

The week had two distinct currents. On the downside, the selling was concentrated and thematic: power delivery, optical networking, AI servers and semiconductor test equipment all fell hard as the market reconsidered how much it is willing to pay for AI hardware exposure. Vicor and Semtech topped the decliners, but they were part of a cluster that included Coherent, Fabrinet, Hewlett Packard Enterprise, Lumentum and Ciena. On the upside, the gainers came from businesses with very little AI sensitivity, and in two of three cases the move followed specific, identifiable news.

That combination resembles a rotation more than a broad risk-off move. Capital leaving crowded AI hardware positions appears to have found its way into areas where valuations were depressed and sentiment was already low, credit bureaus after the September mortgage scoring shock and a retailer trading near its lows. Within the FMP dataset, the Historical Market Sector Performance API makes it possible to check whether that rotation shows up at the sector level, while the Biggest Stock Losers API offers a quick daily cross-check on how concentrated the selling was across the market.

The more durable signal will come from the earnings reports later this month. Price moves of this size often run ahead of fundamental data, so comparing this week's returns with the next set of results through the Earnings Report API and the Income Statement API will show whether the declines in AI hardware reflect weaker operating trends or mainly a repricing of expectations. Where results hold up while prices remain lower, the gap becomes a valuation question rather than a demand one.

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.

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What This Week's Price Action Left Behind

This week's tape drew a sharp line between crowded AI hardware and overlooked, lower-multiple businesses, and much of that line was drawn before the earnings data arrived. Tracking the same universe through the FMP EOD Bulk API over the coming reporting weeks will show whether results confirm the rotation or narrow it.

If you found this useful, you might also like: Signals Desk Weekly Take via FMP API | Five Biggest Stock Movers (Oct 5-9)

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.