FMP
Feb 10, 2026
This week's dividend tape didn't light up with yield outliers or headline-grabbing specials. Instead, a quieter pattern emerged across utilities, exchanges, industrials, retail, and regional banking—five companies raising payouts in ways that reflect capital discipline more than income marketing. Looked at together, the moves read less like coincidence and more like a cross-section of where management teams are comfortable locking in commitments right now.
All five declarations were surfaced directly from the FMP's Dividends Calendar API, which captures dividend decisions at the moment they're announced—before screens, summaries, or yield rankings flatten the signal. In this piece, that same API becomes the backbone of a repeatable workflow: first to flag the week's notable increases, then to frame what those decisions say, in aggregate, about confidence, cadence, and sector-level behavior.
Ameren declared a quarterly dividend of $0.75 per share, or $3.00 annualized, representing a 5.6% increase from the prior $0.71 payout. The dividend is payable March 31, 2026, to shareholders of record March 10, with an ex-dividend date of March 9. At the new rate, the annualized yield stands at 2.9%.
For a regulated utility, the signal here isn't yield expansion—it's cadence and confidence. Ameren's increase fits cleanly within its long-standing pattern of mid-single-digit annual dividend growth, reinforcing the idea that management is comfortable locking in incremental capital return against a backdrop of steady rate-base growth and predictable cash flows. Utilities often serve as balance-sheet barometers; this move suggests Ameren sees limited near-term pressure on its capital plan or regulatory recovery assumptions.
To contextualize this raise further, the most relevant datasets are cash flow statements and capex guidance. Tracking operating cash flow relative to planned infrastructure spending helps determine how much flexibility exists for continued dividend growth without increasing leverage. Regulatory filings and rate-case timelines would also add useful texture to the signal.
PriceSmart announced a semi-annual dividend of $0.70 per share, or $1.40 annualized, marking an 11.1% increase from the prior $0.63 payout. The dividend will be paid February 27, 2026, to holders of record February 17, with an ex-dividend date of February 13. The resulting yield is 0.9%.
At first glance, the yield looks immaterial—but the size of the increase is not. PriceSmart has historically treated dividends as a secondary use of capital, prioritizing reinvestment and geographic expansion across Latin America and the Caribbean. A double-digit raise in that context reads as a margin and cash-flow statement, not an income play. It implies management sees enough durability in earnings to commit more cash outward while still funding store growth and logistics investments.
The most telling supporting data here would come from income statement trends—particularly gross margin stability—and free cash flow generation across cycles. Same-store sales metrics and FX-adjusted revenue growth would also help assess whether this dividend step-up aligns with operational momentum rather than a one-off distribution decision.
Intercontinental Exchange raised its quarterly dividend to $0.52 per share, or $2.08 annualized, an 8.3% increase from the prior $0.48. The dividend is payable March 31, 2026, to shareholders of record March 17, with an ex-dividend date of March 16. The updated yield is 1.3%. The increase was announced alongside the company's Q4 earnings report.
For ICE, dividends function as a credibility check rather than a return driver. The company operates asset-light, high-margin marketplaces where earnings quality and recurring revenue matter more than headline growth. Announcing a dividend increase in conjunction with earnings ties the payout decision directly to reported performance, reinforcing the view that cash generation across exchanges, clearing, and data services remains resilient.
To deepen this read, segment-level revenue data and operating margin trends are key. Watching how transaction revenues compare to recurring data and listings income helps determine whether dividend growth is being supported by stable fee streams or more cyclical activity. Debt maturity schedules and interest expense sensitivity also provide insight into how comfortably ICE can maintain its capital return profile.
Advanced Drainage Systems declared a quarterly dividend of $0.18 per share, or $0.72 annualized, representing a 12.5% increase from the prior $0.16. The dividend will be paid March 16, 2026, to shareholders of record March 2, with an ex-dividend date of February 27. The annual yield remains modest at 0.4%. The increase followed the company's Q3 earnings.
The magnitude of the raise stands out relative to the yield. WMS operates in a capital-intensive corner of industrials tied to infrastructure, construction, and municipal spending cycles. A double-digit dividend increase in this space tends to signal confidence in backlog visibility and pricing discipline rather than a shift toward shareholder yield optimization.
Here, order backlog data, EBITDA margins, and working capital trends offer the clearest lens. Tracking how inventory and receivables move relative to revenue can help assess whether cash conversion is keeping pace with growth—an important factor when dividends are being raised off a low base in a cyclical industry.
International Bancshares raised its quarterly dividend to $0.73 per share, or $2.92 annualized, a 4.3% increase from the prior $0.70. The dividend is payable February 27, 2026, to shareholders of record February 13, with an ex-dividend date of February 12. At the new rate, the dividend yields 4.1%. Management explicitly framed the increase as discretionary, citing strong capital, liquidity, and a six-decade earnings record.
Among this week's group, IBOC's move carries the most balance-sheet signaling weight. Regional banks operate under constant capital scrutiny, and dividend decisions are closely watched as proxies for credit confidence and earnings durability. A measured increase—paired with management's emphasis on liquidity and capital strength—suggests comfort with current reserve levels and funding conditions rather than an aggressive posture.
To evaluate this signal properly, capital ratios, loan loss provisions, and net interest margin trends matter more than headline earnings. Insider ownership data and historical payout ratios can also help determine how conservatively management tends to calibrate shareholder returns relative to underlying bank risk.
Taken together, this week's five dividend increases don't point to a single sector call or macro thesis—but they do outline a consistent behavioral pattern. These are not defensive raises aimed at stabilizing sentiment, nor are they aggressive resets designed to reshape equity narratives. Instead, they cluster around companies with relatively clear visibility into cash generation: regulated utilities, exchange infrastructure, disciplined retail operators, infrastructure-linked industrials, and conservatively managed banks. The shared signal is not yield expansion, but a willingness to formalize capital return while uncertainty around rates, growth, and funding conditions remains unresolved—a pattern that becomes clearer when dividend actions are captured cleanly at the source, as they are across datasets aggregated by platforms like FMP.
What stands out is how proportional each increase is to the underlying business model. Utilities and banks stayed incremental. Industrials and retailers raised from low bases without altering capital priority stacks. The exchange operator aligned its decision with earnings disclosure rather than market momentum. That restraint is meaningful. It suggests dividend decisions are being gated by internal thresholds—coverage ratios, liquidity buffers, and payout durability—rather than short-term price action or external pressure. In this context, dividend policy functions less as signaling rhetoric and more as operational confirmation.
Viewed through a broader analytical lens, dividends gain relevance when treated as cross-dataset evidence rather than isolated events. When declaration data is evaluated alongside historical payout behavior, operating cash flow trends, margin stability, and balance-sheet structure, the increase itself becomes a reference point instead of the conclusion. Framed this way, this week's pattern reads more like a governance signal than a macro one: companies across very different sectors converging on the same posture—preserve flexibility, but commit capital once internal data supports it.
If dividends are going to be used as signals rather than historical footnotes, the workflow has to start where the decision actually happens: at declaration. Pulling data from the FMP Dividends Calendar API keeps the analysis anchored to the original corporate action—what management approved and when—before the data is aggregated, smoothed, or reclassified elsewhere. That timing discipline is what allows weekly comparisons to stay consistent instead of drifting as the dataset evolves.
Before running the process, ensure the API key is active.
The Dividends Calendar endpoint serves as the entry point, returning newly declared dividends across the market in a structured format that includes amounts, dates, yields, and payment frequency. This raw feed becomes the working universe for the analysis.
Endpoint:
https://financialmodelingprep.com/stable/dividends-calendar?apikey=YOUR_API_KEY
Sample Response:
[
{
"symbol": "1D0.SI",
"date": "2025-02-04",
"recordDate": "",
"paymentDate": "",
"declarationDate": "",
"adjDividend": 0.01,
"dividend": 0.01,
"yield": 6.25,
"frequency": "Semi-Annual"
}
]
Start by querying the Dividends Calendar over a short, controlled time frame—typically the most recent 10 to 14 days. This window is long enough to capture new declarations while limiting contamination from older entries that sometimes reappear due to reporting delays. The output from this step forms the working universe for the rest of the analysis.
Next, for every ticker surfaced in the initial pull, retrieve the previous dividend using the historical dividend endpoint. This historical anchor is critical. Without it, unchanged recurring payments and true increases are indistinguishable. The comparison introduces context and allows the workflow to focus on intent rather than repetition.
With both the new and prior dividend values in hand, calculate the percentage change using
(New Dividend − Old Dividend) ÷ Old Dividend × 100.
Apply your screening criteria to narrow the list. A common approach is to flag increases of 5% or more paired with an annual yield of at least 2%, which helps remove token raises while preserving economically relevant moves. Thresholds can be tuned depending on whether the focus is income generation, payout discipline, or signal detection.
If the objective is basic coverage—tracking when dividends are declared, raised, or adjusted—a lightweight setup is sufficient. Free access supports short-horizon monitoring, allowing recent dividend activity to be captured and routed into a weekly review, alert stream, or internal dashboard. At this level, the emphasis is immediacy: ensuring new declarations are logged as they occur, even if historical context is limited.
The workflow changes once dividend data becomes something to evaluate rather than simply record. Starter access extends that view to a full year of historical payouts, making it possible to distinguish true increases from routine repeats. Premium access pushes the lens further, with up to five years of dividend history available—enough to frame new declarations against prior cycles, earnings variability, and sector norms. That added depth is what allows analysts to separate routine maintenance from decisions that break pattern, and to assess whether a dividend move is incremental or structurally meaningful.
Workflows like this rarely stay personal for long. What starts as an analyst-level tool—used to track dividend changes and frame weekly notes—tends to surface in team discussions, portfolio reviews, and shared research channels. As more people reference the same outputs, the question shifts from whether the signal is useful to whether it's being generated consistently. At that point, scale becomes less about volume and more about alignment.
This is where analysts often end up playing a different role: not just consumers of data, but stewards of process. Standardizing inputs, refresh timing, and screening logic reduces friction that quietly accumulates when each desk runs its own version of the workflow. Shared dashboards stop drifting out of sync. Cross-team comparisons no longer require reconciling assumptions. Audit trails become clearer because everyone is working from the same declared events, historical context, and update cadence.
As dividend monitoring feeds broader research or investment processes, operational considerations start to matter. Governance, access control, data lineage, and repeatability move from “nice to have” to baseline requirements. That's typically where firms look to formalize the setup—often through something like the Financial Modeling Prep Enterprise Plan — not to change the workflow, but to preserve what already works while making it durable at scale. Done well, the result isn't just wider adoption, but a shared research utility that holds up across teams without fragmenting into parallel versions.
Dividend changes rarely dominate the news cycle, but tracked at the point of declaration, they offer a steady read on how management teams are translating confidence into commitment. Pulled directly from the Financial Modeling Prep Dividends Calendar API, these decisions function less as yield signals and more as behavioral data—showing when capital return shifts from optional to intentional. Over time, that accumulation of small, deliberate choices becomes a useful way to monitor corporate conviction without relying on narrative or price action.
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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.

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