Feb 17, 2026
This week's dividend tape reads less like a yield chase and more like a capital allocation check-in. A cross-sector scan of newly declared payouts shows measured increases from retail, utilities, transport, energy, and banking — a pattern that suggests boards are still willing to lean into cash returns despite uneven macro visibility.
The screen was built directly from the Financial Modeling Prep Dividends Calendar API, capturing fresh declarations before they're absorbed into consensus summaries or smoothed into quarterly aggregates.
Below, we break down what moved, why it matters, and how the same API workflow can be used to systematically surface dividend signals in real time.
Tractor Supply declared a quarterly dividend of $0.24 per share, or $0.96 annualized, marking a 4.3% increase from the prior $0.23. The dividend is payable March 10, 2026, to stockholders of record on February 24, 2026, with an ex-dividend date of February 23, 2026. The annual yield stands at 1.8%.
“Tractor Supply remains committed to delivering consistent cash returns to our shareholders. This dividend increase reflects the strength of our business and marks our 17th consecutive year of annual dividend growth,” said Chairman Edna Morris.
A mid-single-digit increase may not command attention in isolation, but the context matters. Tractor Supply operates in a discretionary retail niche tied to rural lifestyle spending — a category sensitive to both consumer confidence and commodity-linked income. Extending a 17-year dividend growth streak suggests management sees enough stability in operating cash flow to continue incremental capital returns despite uneven retail conditions. The raise reinforces continuity rather than acceleration.
To frame the signal more precisely, reviewing the company's cash flow statement and payout ratio trends would clarify whether dividend growth is tracking earnings growth or running ahead of it. Analysts may also examine same-store sales trends and inventory turnover in the most recent quarterly filings to determine whether the dividend increase aligns with operational momentum or reflects balance-sheet resilience.
NextEra Energy declared a quarterly dividend of $0.6232 per share, or $2.4928 annualized, a 10% increase from the prior $0.57. The dividend will be paid on March 16, 2026, to shareholders of record on February 27, 2026, with an ex-dividend date of February 26, 2026. The annual yield is 2.6%.
For a regulated utility and renewable energy developer, a double-digit dividend increase carries interpretive weight. Utilities are typically viewed through the lens of rate base growth, financing costs, and regulatory visibility. A 10% hike signals management's confidence in forward earnings and cash flow stability, particularly in a rate environment that has pressured capital-intensive sectors over the past two years.
Investors seeking to contextualize the move would look to the company's income statement growth trajectory, long-term earnings guidance, and capital expenditure pipeline. Monitoring regulatory filings and debt maturity schedules can also help determine how comfortably dividend growth is supported amid ongoing infrastructure investment. The signal here is not yield expansion alone, but balance-sheet capacity to fund both growth and shareholder returns.
Bladex declared a quarterly dividend of $0.6875 per share, or $2.75 annualized, a 10% increase from the prior $0.625. The dividend will be payable March 12, 2026, to shareholders of record on February 25, 2026, with an ex-dividend date of February 24, 2026. The annual yield stands at 5.5%. The company also reported fourth-quarter results alongside the announcement.
With a yield already above 5%, the incremental 10% raise stands out relative to broader financial-sector norms. Bladex, a trade finance bank focused on Latin America, operates in a rate-sensitive and credit-dependent environment. A higher payout in conjunction with Q4 results suggests management is comfortable with asset quality, capital ratios, and net interest income trends at this stage of the cycle.
Evaluating the strength of that signal requires attention to net interest margin data, loan growth, and non-performing asset ratios from the most recent earnings release. The dividend decision may reflect confidence in capital adequacy metrics or earnings durability, but those assumptions are best tested against quarterly balance-sheet disclosures and regulatory capital figures.
Knight-Swift Transportation declared a quarterly dividend of $0.20 per share, or $0.80 annualized, representing an 11.1% increase from the prior $0.18. The dividend will be paid on March 23, 2026, to shareholders of record on March 6, 2026, with an ex-dividend date of March 5, 2026. The annual yield is 1.3%.
In the trucking sector, dividend changes often intersect with freight rate cycles and capacity normalization. An 11% increase suggests management sees stabilization in cash generation even as freight demand has faced cyclical pressure in recent quarters. Rather than signaling exuberance, the raise reads as a disciplined capital allocation decision during a transitional phase in the freight market.
To assess durability, analysts would likely consult operating ratio trends, freight volume data, and free cash flow metrics in the latest earnings report. Comparing dividend growth to earnings variability over prior cycles can also illuminate whether this move aligns with long-term capital return policy or represents a recalibration after prior softness in transportation margins.
Phillips 66 declared a quarterly dividend of $1.27 per share, or $5.08 annualized, a 5.8% increase from the prior $1.20. The dividend will be payable March 4, 2026, to shareholders of record on February 23, 2026, with an ex-dividend date of February 20, 2026. The annual yield is 3.2%.
“We are increasing the dividend this quarter, reflecting our confidence in our ability to generate through-cycle cash flows,” said Chairman and CEO Mark Lashier. He noted that since the company's formation in 2012, the annual dividend has increased every year, resulting in a 15% compound annual growth rate.
In refining and midstream operations, dividend policy is often interpreted through the lens of commodity volatility and crack spread dynamics. A mid-single-digit increase reinforces a through-cycle framework rather than a reaction to short-term margin strength.
To deepen the analysis, reviewing segment-level earnings, cash flow from operations, and capital allocation disclosures would clarify how the dividend fits within broader shareholder return programs, including buybacks. The raise signals continuity in policy, but the supporting data in quarterly filings determines how conservatively that policy is being funded.
Step back from the individual headlines and a pattern emerges: this was not a yield grab. It was a cross-sector reaffirmation of capital discipline.
Retail (Tractor Supply), regulated utilities (NextEra), trade finance (Bladex), transportation (Knight-Swift), and refining (Phillips 66) operate under very different demand curves and capital requirements. Yet each board approved a dividend increase within the same narrow window. That simultaneity is the signal. When payout decisions cluster across economically distinct industries, it tends to reflect internal balance-sheet comfort rather than a single macro tailwind.
The increases themselves were calibrated — 4% to 11%, with only one yield above 5%. No resets. No freezes. Just steady extensions of existing policies. That pattern aligns more with normalization than expansion: companies appear willing to compound payouts in line with visibility, not stretch them to make a statement.
To test whether that posture is grounded in operating strength, dividend data needs to be paired with fundamentals. Comparing payout growth against multi-year EPS trends using Financial Modeling Prep's Income Statement API and dividend history endpoints provides a clearer read on whether capital returns are tracking earnings capacity. Layering in free cash flow data via the Cash Flow Statement API further clarifies whether distributions are supported by operating cash generation or balance-sheet flexibility.
There is also a sentiment dimension. Aligning dividend hikes with forward revisions from the Analyst Estimates API can reveal whether internal capital decisions and external expectations are moving in tandem. Price behavior adds another layer: cross-referencing announcements with historical price data helps determine whether increases are being issued into strength or weakness — context that shapes how the market interprets the move.
Viewed collectively, the week's declarations resemble a quiet affirmation of durability. For analysts building this type of cross-endpoint workflow, the structure is readily assembled from datasets accessible through Financial Modeling Prep, allowing dividend actions to be evaluated not as isolated headlines, but as part of a broader capital allocation mosaic.
If the goal is to treat dividend changes as live signals rather than backward-looking statistics, the process has to begin at the source: the declaration itself. That means working directly from the FMP Dividends Calendar API, which captures the corporate action at the moment management approves it — before it is rolled into summaries or adjusted datasets.
Before running anything, confirm the API key is active.
The Dividends Calendar endpoint functions as the intake layer, delivering newly declared dividends in a standardized structure that includes the declared amount, key dates, yield, and frequency. That dataset becomes the working universe for the screen.
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.
A dividend screen built for one desk rarely stays there. Once the output starts appearing in portfolio reviews or research meetings, it shifts from a personal tracking tool to a shared reference point. At that moment, the question is no longer whether the signal is useful — it's whether everyone is running the same version of it.
This is where analysts become internal advocates for structure. Standardizing data inputs, refresh schedules, and screening thresholds eliminates the quiet inconsistencies that creep in when multiple teams maintain parallel workflows. A shared dashboard aligned to the same declaration data and historical comparisons reduces interpretation gaps. Cross-team discussions become about insight, not reconciliation. And when assumptions are challenged, the underlying methodology is transparent and defensible.
As adoption widens, operational discipline becomes part of the analysis itself. Governance, permissioning, data lineage, and reproducibility move to the foreground. Institutions typically address that transition by formalizing the setup — often through something like the Financial Modeling Prep Enterprise Plan — not to alter the workflow, but to preserve its integrity as usage scales. The advantage is less about adding features and more about ensuring the signal remains consistent across teams, audit-ready, and free from fragmentation as it becomes embedded in broader research processes.
Dividend declarations rarely dominate headlines, but they remain one of the clearest board-level statements about balance sheet durability and cash flow visibility. When tracked systematically through the FMP Dividends Calendar API, they form a steady, comparable signal set — one that often reveals shifts in corporate posture before broader narratives catch up.
If you found this useful, you might also like: Weekly Signals Desk | Price-Target Gaps Identified via the FMP API (Feb 2-6)
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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