Over the past two weeks, a routine scan of dividend declarations surfaced something less routine: five companies across financials, industrials, utilities, and services quietly raised payouts by meaningful margins. In a market still debating the durability of earnings and the timing of the next rotation, these decisions stand out as capital-allocation statements rather than maintenance adjustments.

This note breaks down those signals as they appeared in real time through the FMP Dividends Calendar API. We'll walk through what the raises look like individually, what they suggest in aggregate, and how the same API-driven process can be used to systematically detect dividend policy shifts as they happen—before they're flattened into consensus narratives.

Five Notable Dividend Hikes

Goldman Sachs (NYSE: GS)

Goldman Sachs declared a quarterly dividend of $4.50 per share, or $18 annualized, representing a 12.5% increase from the prior $4.00 payout. The dividend is payable on March 30, 2026, to shareholders of record on March 2, 2026, with an ex-dividend date of February 27, 2026. Currently, the annualized yield stands at 1.64%.

For large banks, dividend changes tend to follow internal capital planning cycles that incorporate stress-test outcomes, regulatory buffers, and earnings visibility across trading, advisory, and asset management lines. A double-digit increase at this scale signals comfort with capital generation after a volatile period for deal activity and fixed-income markets.

To contextualize this move, income statement trends—particularly net interest income versus fee-based revenues—help frame how balanced Goldman's earnings mix has become. Pairing that with balance sheet data on CET1 ratios and share repurchase activity provides a clearer view of how dividends fit within the firm's broader capital return posture.

Apogee Enterprises (NASDAQ: APOG)

Apogee Enterprises raised its quarterly dividend to $0.27 per share, or $1.08 annualized, a 3.8% increase from the prior $0.26 payout. The dividend will be paid on February 18, 2026, to shareholders of record on February 3, 2026, with an ex-dividend date of February 2, 2026. The resulting annual yield is 2.94%.

While modest in percentage terms, the increase followed the company's Q3 earnings release, tying the decision to near-term operating performance. For a building-products company exposed to commercial construction cycles, incremental dividend raises often reflect management's read on backlog quality, margin durability, and cost pass-through rather than optimism about top-line acceleration.

Here, the signal is less about growth and more about stability. Cash flow statements—particularly free cash flow generation relative to capex—help assess whether the dividend remains comfortably funded through the cycle. Segment-level revenue data can further clarify whether architectural services or glass fabrication is doing the heavier lifting as demand conditions evolve.

S&P Global (NYSE: SPGI)

S&P Global declared a quarterly dividend of $0.97 per share, or $3.88 annualized, marking a 1% increase from the prior $0.96 dividend. The payment date is March 11, 2026, for shareholders of record on February 25, 2026, with an ex-dividend date of February 24, 2026. The annual yield is 0.71%.

On its own, a 1% raise looks incremental, but for a company with S&P Global's payout history, that restraint is part of the message. Dividend policy here tends to move in lockstep with long-term earnings visibility rather than quarter-to-quarter fluctuations. The firm's index, ratings, and market intelligence businesses generate recurring revenue streams, but they also face sensitivity to debt issuance volumes and market volatility.

To interpret this increase, historical dividend growth rates alongside operating margin trends are instructive. Revenue segmentation—particularly between ratings and subscription-based products—help determine whether the company is prioritizing consistency over acceleration in shareholder returns.

Otter Tail Corp (NASDAQ: OTTR)

Otter Tail Corp announced a quarterly dividend of $0.5775 per share, or $2.31 annualized, reflecting a 10% increase from the prior $0.525 payout. The dividend is payable on March 10, 2026, to shareholders of record on February 13, 2026, with an ex-dividend date of February 12, 2026. The annual yield is 2.44%.

Management framed the increase explicitly as a balance-sheet statement. CEO Chuck MacFarlane noted that the company remains in a position of financial strength and highlighted that this marks a second consecutive year of double-digit dividend growth. For a regulated utility, that cadence stands out, as dividend adjustments are typically gradual and closely tied to rate-base growth and regulatory outcomes.

Evaluating this signal benefits from utility-specific datasets: regulated asset base expansion, capital expenditure plans, and allowed return on equity assumptions. Comparing dividend growth against earnings from the electric segment versus manufacturing operations also helps clarify which parts of the business are underwriting the payout trajectory.

MAXIMUS (NYSE: MMS)

MAXIMUS increased its quarterly dividend to $0.33 per share, or $1.32 annualized, a 10% increase from the prior $0.30 dividend. The dividend will be paid on March 2, 2026, to shareholders of record on February 13, 2026, with an ex-dividend date of February 12, 2026. The annual yield is 1.34%.

For a government services contractor, dividend growth often reflects confidence in contract visibility and cash conversion rather than near-term earnings growth. A double-digit increase suggests management sees sufficient durability in program funding and execution margins to support higher recurring cash outflows.

To ground that view, backlog disclosures, contract win data, and operating cash flow trends provide useful context. Monitoring receivables and working capital movements can also help assess whether the dividend increase aligns with underlying cash dynamics rather than purely accounting earnings.

Reading the Tape: What These Dividend Decisions Reveal in Aggregate

Viewed together, these five dividend actions don't read as a single-sector call—they read as a cross-market expression of balance-sheet confidence surfacing in different forms. A global investment bank, a construction-linked manufacturer, a data monopoly, a regulated utility, and a government services contractor operate under very different economic constraints. Yet each chose to reset cash returns higher within a narrow window. That clustering matters. It suggests dividend policy is being used less as a yield signal and more as a credibility signal—management teams anchoring expectations around what they believe cash flows can sustain through a less forgiving macro backdrop.

The pattern also highlights how dividend increases are becoming more selective. Only one name opted for a largely symbolic raise; the others leaned toward either material percentage increases or yields that remain competitive within their respective peer groups. This split mirrors broader capital allocation behavior. Businesses with high visibility and durable pricing power tend to emphasize continuity, while companies closer to economic cycles or regulatory oversight use dividend actions to reinforce stability narratives. In that sense, dividends are functioning as a form of signaling discipline—what gets raised, how much, and when is increasingly deliberate.

From a workflow perspective, this is where dividends stop behaving like isolated line items and start functioning as connective tissue across the financial statements. When dividend changes are anchored to multi-year payout histories, evaluated against cash-flow durability, and framed alongside leverage and capital return data, the signal becomes clearer and harder to dismiss. That style of synthesis—using dividend events as the trigger and surrounding fundamentals as verification—is emblematic of how broad market datasets are typically applied within platforms like Financial Modeling Prep: not to manufacture conclusions, but to highlight which corporate decisions deserve closer inspection.

In an environment where guidance is constrained and narratives shift quickly, dividend policy remains one of the few decisions that commits cash in plain sight. When multiple firms across unrelated sectors make that commitment at once, it creates a pattern worth isolating, contextualizing, and tracking forward—not for prediction, but for disciplined signal recognition as new data arrives.

Turning Dividend Declarations Into a Repeatable Signal with FMP Data

To consistently capture dividend signals before they're diluted by summaries or commentary, the data needs to be pulled at the moment companies make their declarations. Working directly from the Dividends Calendar API anchors the process in primary events and keeps the workflow straightforward to rerun on a fixed cadence. The underlying logic is uncomplicated: collect new dividend announcements, compare them to each company's prior payout, and distinguish routine maintenance from decisions that reflect an actual change in policy.

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

For straightforward dividend monitoring—simply tracking when payouts are declared or adjusted—a lighter setup is usually sufficient. Basic or Starter tier access supports an event-driven process that captures announcements as they're released and feeds cleanly into a weekly review, alert system, or screening workflow. At this level, the emphasis stays on speed and coverage: identifying changes early without layering on unnecessary context.

The calculus shifts once the goal moves beyond detection and toward evaluation. Premium-level access becomes more relevant when dividend decisions need to be measured against history rather than viewed in isolation. With several years of payout data available, each new declaration can be compared to prior cycles, earnings variability, and sector norms. That historical framing helps distinguish between routine increases and decisions that stand out relative to a company's own behavior.

When a Desk Tool Becomes Shared Research Infrastructure

What starts as a practical workflow for a single analyst often changes character once its output begins circulating. When dividend signals move from a personal screen into portfolio reviews, risk meetings, or internal notes, the question is no longer whether the process works—it's whether everyone is interpreting the same signal the same way. That's the inflection point where a desk tool begins to resemble shared infrastructure.

At that stage, analysts often become informal stewards of standardization. Defining how dividend events are sourced, refreshed, and stored helps prevent the quiet drift that happens when similar workflows are rebuilt across teams with slight variations. A common framework supports shared dashboards, reduces reconciliation work, and makes downstream discussions more efficient because assumptions are aligned from the start.

Institutional use also raises different requirements. Auditability, data lineage, access controls, and refresh discipline stop being abstract concepts once a workflow feeds published research or investment decisions. Supporting that shift typically means placing the system within a governed environment—such as the Enterprise Plan —not as an upgrade for its own sake, but as a practical way to match the tooling to how widely it's being relied on. Done well, the transition preserves what made the original workflow effective while giving it the structure needed to scale across teams without fragmentation.

Dividends as a Quiet Pulse on Corporate Confidence

Tracked over time, dividend decisions tend to surface management conviction before it shows up in guidance or narrative. Pulling those signals directly from the FMP Dividends Calendar API keeps the focus on observable actions—cash committed, not commentary offered. Read consistently, that discipline turns routine payout updates into a low-noise gauge of how confident companies really are in their underlying cash flows.

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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.