This week's dividend scan surfaced five companies raising payouts across utilities, financials, industrials, and consumer cyclicals—an unusual mix given how uneven capital flows have been under the surface. Rather than clustering in traditional defensives or high-yield corners, the increases appeared across balance-sheet profiles, suggesting management confidence is being expressed selectively, not thematically.

The names were identified directly from the FMP's Dividends Calendar API, which captures dividend declarations at the moment they're announced, before they're filtered, summarized, or contextualized by secondary screens. In this article, we'll break down what those declarations reveal individually—and what they begin to signal in aggregate when treated as real-time data rather than backward-looking yield metrics.

Five Dividend Hikes Worth Noting This Week

Charles Schwab (NYSE: SCHW)

Charles Schwab declared a quarterly dividend of $0.32 per share, or $1.28 annualized, representing an 18.5% increase from the prior $0.27 payout. The dividend is payable February 27, 2026, to shareholders of record on February 13, with an ex-dividend date of February 12. At current levels, the annual yield stands at roughly 1.2%.

The size of the increase is notable relative to Schwab's recent operating backdrop. Fourth-quarter earnings matched analyst expectations, reinforcing a narrative of stabilization after a period dominated by balance sheet scrutiny and funding-cost sensitivity. Against that context, an 18.5% raise reads less like incremental housekeeping and more like a statement that internal capital planning has moved past defensive positioning. The signal is reinforced by timing: dividend growth arriving alongside earnings normalization rather than after a prolonged rebound phase.

Leadership changes at Charles Schwab Bank add another layer. Paul Woolway's planned retirement and the succession of Tyler Woulfe as President and CEO introduce a transition moment for the banking subsidiary, making capital decisions particularly visible. To contextualize this signal further, pairing dividend data with bank segment income statements and deposit trend disclosures would help frame how confidence at the holding-company level maps to the operating bank underneath.

Consolidated Edison (NYSE: ED)

Consolidated Edison raised its quarterly dividend to $0.8875 per share, or $3.55 annualized, up 4.4% from the prior $0.85 level. The dividend will be paid March 16, 2026, to shareholders of record on February 18, with an ex-dividend date of February 17. The current annual yield is approximately 3.4%.

This marks Con Edison's 52nd consecutive annual dividend increase, extending the longest such streak among utilities in the S&P 500. Management explicitly tied the increase to its targeted payout ratio of 55%-65% of adjusted earnings, reinforcing the dividend as a policy-driven outcome rather than a discretionary one. In a sector often treated as bond-adjacent, that consistency continues to anchor Con Edison's profile as a capital return instrument linked tightly to regulated earnings visibility.

What stands out this year is how routine the increase appears despite elevated capital spending tied to grid resilience and clean energy transition investments. The signal here is less about growth acceleration and more about balance—maintaining shareholder returns while funding long-duration infrastructure. Examining multi-year cash flow statements alongside regulated asset base growth would further illuminate how dividend continuity is being sustained without crowding out reinvestment.

Polaris Industries (NYSE: PII)

Polaris Industries declared a quarterly dividend of $0.68 per share, or $2.72 annualized, up 1.5% from $0.67 previously. The dividend is payable March 16, 2026, to shareholders of record on March 2, with an ex-dividend date of February 27. The resulting yield is roughly 4.2%.

The modest size of the increase contrasts with a stronger-than-expected fourth-quarter earnings report. Polaris posted Q4 EPS of $0.08, exceeding consensus by $0.03, while revenue of $1.92 billion came in above the $1.81 billion estimate. That divergence—earnings outperformance paired with a conservative dividend adjustment—suggests management remains cautious in translating near-term results into longer-term payout commitments.

In cyclical consumer manufacturing, dividend restraint following a volatile demand environment can be as informative as aggressive hikes. The data implies a preference for preserving flexibility amid uneven retail trends rather than extrapolating a single quarter's beat. To deepen the picture, margin history and inventory turnover metrics would help assess whether recent earnings strength reflects transient cost dynamics or more durable operating improvements.

Jacobs (NYSE: J)

Jacobs announced a quarterly dividend of $0.36 per share, or $1.44 annualized, representing a 12.5% increase from the prior $0.32 payout. The dividend will be paid March 20, 2026, to shareholders of record on February 20, with an ex-dividend date of February 19. The annual yield remains modest at approximately 1.1%.

For a professional services and engineering firm, the magnitude of the increase carries more weight than the yield itself. Jacobs operates with comparatively asset-light economics, making dividend changes a clearer reflection of confidence in backlog quality and cash conversion rather than balance sheet leverage. A double-digit increase suggests management views current contract visibility as supportive of higher recurring capital return.

The signal is best interpreted alongside order backlog trends and free cash flow consistency rather than market yield comparisons. Tracking segment-level operating margins and government versus private-sector revenue mix would add context to whether the dividend hike aligns with improving project execution or simply steady-state performance.

First Bancorp (NASDAQ: FBNC)

First Bancorp raised its quarterly dividend to $0.20 per share, or $0.80 annualized, an 11.1% increase from $0.18 previously. The dividend is payable March 13, 2026, to shareholders of record on February 26, with an ex-dividend date of February 25. The current yield stands near 3.9%.

The increase follows a solid fourth-quarter earnings print. First Bancorp reported Q4 EPS of $0.55, exceeding analyst expectations by $0.04, while revenue of $257.17 million modestly topped consensus. In the regional banking space, where capital actions are often constrained by regulatory and funding considerations, dividend growth at this pace points to improving internal capital generation rather than external balance sheet relief.

What makes the move notable is its alignment with earnings momentum rather than a delayed response to prior strength. The data suggests management is incrementally re-opening the capital return channel as profitability stabilizes. Reviewing net interest margin trends and loan growth composition alongside dividend history would help clarify whether this represents a normalized payout path or a recalibration after a conservative period.

Interpreting the Pattern: What These Dividend Decisions Reveal in Aggregate

Taken together, this week's dividend increases don't point to a single thematic trade, but they do reveal a shared behavioral pattern. Across financials, utilities, industrial services, consumer cyclicals, and regional banking, management teams are raising payouts in ways that are measured and deliberate, closely tied to operating visibility rather than broad macro optimism. The signal isn't about stretching for yield; it's about maintaining control. Each increase appears sized to what the underlying business can support today, not what market narratives might suggest about tomorrow.

What stands out is the spread in magnitude. Charles Schwab and Jacobs delivered double-digit increases, while Polaris and Consolidated Edison opted for smaller, policy-consistent adjustments. That dispersion is meaningful. It implies dividend decisions are being made bottom-up, informed by company-specific cash flow confidence and balance sheet positioning, rather than driven by sector-wide sentiment or capital flow themes. In that sense, dividends are acting less like a sector signal and more like a management-level disclosure, quietly marking where internal thresholds have shifted.

This is where treating dividends as structured data rather than commentary becomes useful. When declaration data from the Dividends Calendar API is layered against multi-year cash flow and income statement trends, routine increases begin to separate from changes that reflect a different capital posture. Access to a broader, unified dataset—such as the financial statements, balance sheets, and estimates available through FMP — allows those comparisons to be made systematically rather than anecdotally. Adding balance sheet context helps clarify whether higher payouts are being supported by operating strength or balance sheet flexibility, while analyst expectation data can highlight when dividend actions align with, or diverge from, prevailing assumptions.

Viewed this way, the aggregate takeaway is neither bullish nor defensive—it's selective. Companies are not racing to expand payouts, nor are they pulling back across the board. They are adjusting commitments in line with internal durability. For analysts, the value lies in watching how consistently dividend changes, cash generation, and expectations remain aligned over time. When those signals reinforce each other, the message strengthens; when they diverge, the disconnect often becomes the more informative development to monitor.

Turning Dividend Declarations Into a Repeatable Signal with FMP Data

To work with dividends as forward-facing signals instead of after-the-fact observations, the workflow has to begin at the moment companies commit to the payout. Using the Financial Modeling Prep Dividends Calendar API keeps the analysis tied to the original declaration—what was announced and when—before summaries, averages, or secondary screens reshape the data. That anchoring makes the process both repeatable and comparable across weeks.

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"

}

]

Step 1: Capture Recent Declarations

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.

Step 2: Stack It Against the Prior Dividend

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.

Step 3: Filter for Material Moves

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.

Example Workflow: Detecting 5%+ Dividend Hikes

1. Pull a fresh 14-day window from the Dividends Calendar API.
2. For each ticker, fetch its prior payout via the historical dividend endpoint.
3. Compute the percentage change using the formula above.
4. Keep only companies posting 5%+ increases and yielding 2% or more.

Expanding Your Dividend Tracking Setup

When the objective is straightforward monitoring—knowing when dividends are declared or adjusted—a lean setup does the job. Basic or Starter access supports an event-driven workflow that captures announcements as they hit the tape and routes them into a weekly review, alert, or internal dashboard. At this level, the emphasis is speed and completeness: catching changes as they occur without adding analytical layers that aren't yet necessary.

The calculus changes once dividend data becomes something to assess rather than simply log. Premium-level access starts to matter when new declarations need to be evaluated against a longer record instead of treated as standalone events. With several years of historical payouts in view, increases can be framed against prior cycles, earnings volatility, and sector norms. That broader context makes it easier to distinguish routine maintenance raises from moves that meaningfully diverge from a company's own pattern.

Scaling Dividend Monitoring Beyond the Individual Desk

The transition from individual use to institutional adoption is rarely deliberate—it tends to surface organically. A dividend-tracking workflow that starts as a personal tool begins to inform team conversations, portfolio discussions, and shared research notes. At that stage, the issue shifts from signal quality to signal alignment. The value increasingly depends on whether everyone is referencing the same inputs, timing, and assumptions.

This is where analysts often step into a different role: not just users of data, but advocates for standardization. Formalizing how dividend events are sourced, how frequently data refreshes, and which filters define the signal removes a surprising amount of friction. Shared dashboards become more stable, cross-team comparisons stop requiring reconciliation, and discussions move faster because the underlying data logic is consistent rather than improvised. The workflow gains durability as it scales, instead of fragmenting into parallel versions.

Once dividend signals feed broader research outputs or investment processes, additional requirements come into play. Auditability, data lineage, access controls, and disciplined refresh schedules become essential rather than optional. That's typically the point where teams look to formalize the setup within a governed environment—such as the FMP Enterprise Plan—as a way to preserve the integrity of a proven desk-level workflow while making it reliable at firm scale. When done well, the result isn't just broader adoption, but a piece of research infrastructure that holds up under institutional scrutiny without losing the clarity that made it useful in the first place.

Dividends as a Quiet Pulse on Corporate Confidence

Dividend changes rarely command headlines, but tracked consistently at the point of declaration, they offer a clean read on how management teams are calibrating commitment amid shifting conditions. Pulled directly from the FMP Dividends Calendar API, these decisions function less as yield signals and more as behavioral data—revealing when companies are confident enough to lock in capital return. Over time, that quiet accumulation of choices becomes a useful lens on corporate conviction, especially when viewed without noise or narrative overlay.

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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.