This week's dividend screen surfaced five companies across banking, healthcare real estate, industrials, and building services, with increases ranging from incremental to double-digit. The mix matters: payout growth is appearing across very different operating profiles, suggesting that corporate confidence is not confined to a single sector or market narrative.
In this edition of Weekly Signals Desk, we examine the five dividend increases flagged through the FMP Dividends Calendar API, then break down how that API can be used to identify fresh declarations, compare them with prior payouts, and turn routine dividend announcements into a repeatable signal screen.
Key Takeaways
- The five dividend increases reflect different signals, ranging from regulated capital returns at Bank of America and Avidia Bancorp to cash-backed payout growth at Comfort Systems USA.
- The size of a dividend increase matters less than the earnings quality, free cash flow, leverage and capital requirements supporting it.
- Combining the FMP Dividends Calendar API with income statement, cash flow, key metrics and estimates data helps distinguish durable payout capacity from policy-driven increases.
Five Dividend Moves That Stand Out This Week
Bank of America (NYSE: BAC)
Bank of America declared a quarterly dividend of $0.32 per share, equivalent to $1.28 annualized. The new payout represents a 14.3% increase from the prior quarterly dividend of $0.28. It will be paid on September 25, 2026, to stockholders of record on September 4, 2026. The ex-dividend date is September 3, 2026, and the annual dividend yield is approximately 2.1%.
The size of the increase makes this more than a routine adjustment. Large-bank dividends are closely tied to regulatory capital requirements, stress-test outcomes and management's assessment of how much capital can be returned without weakening the balance sheet. Bank of America announced the increase after the Federal Reserve's latest stress-testing process, while CEO Brian Moynihan framed the decision around returning excess capital to shareholders. The bank also entered the announcement with stronger second-quarter results, including record trading performance and increased investment-banking activity.
The signal is therefore best read as a capital-management decision supported by recent earnings capacity, rather than as a standalone statement about the economic cycle. The next layer of analysis should compare the higher dividend with Bank of America's common equity tier 1 ratio, stress capital buffer, credit-loss provisions and share-repurchase activity. FMP balance-sheet, cash-flow and key-metrics datasets would help show whether distributions are rising alongside capital generation, while the income statement would clarify how much of the recent earnings improvement came from recurring net interest income versus more variable trading and advisory revenue.
Avidia Bancorp (NYSE: AVBC)
Avidia Bancorp declared a quarterly dividend of $0.06 per share, or $0.24 annualized, marking a 20% increase from the previous dividend of $0.05. The payment is scheduled for August 27, 2026, for stockholders of record on August 18, 2026. Its ex-dividend date is August 17, 2026, and the annual yield is approximately 1.2%.
Although the percentage increase is the largest in this group, the absolute change is one cent per quarter. That distinction matters when evaluating a relatively small banking institution, where modest changes in funding costs, credit quality or loan performance can have an outsized effect on distributable earnings. Avidia reported second-quarter net income of $7.2 million, or $0.39 per share, up from $6.0 million, or $0.32 per share, in the first quarter. Its net interest margin improved by three basis points to 3.64%, while the cost of interest-bearing liabilities declined by four basis points to 1.89%.
Those figures provide operating context for the dividend increase, but they do not remove the need to examine credit risk. The most useful follow-up would be a review of nonperforming assets, charge-offs, loan-loss reserves, deposit composition and reliance on higher-cost funding. FMP income-statement and balance-sheet data could track margin and capital trends, while SEC filings would provide the necessary detail on the loan book. Insider-trading data would also add context around how directors and executives are positioning after the company's recent earnings and payout decisions, without treating those transactions as a signal on their own.
Omega Healthcare Investors (NYSE: OHI)
Omega Healthcare Investors declared a quarterly dividend of $0.68 per share, equal to $2.72 annualized. This is a 1.5% increase from the prior quarterly dividend of $0.67. The dividend will be paid on August 14, 2026, to common stockholders of record on August 3, 2026, with an ex-dividend date of July 31, 2026. Its annual yield is approximately 5.3%.
The increase is small, but it carries added significance because Omega had maintained the dividend at $0.67 for an extended period. For a healthcare REIT, the key issue is not simply the nominal dividend amount. It is whether rental collections, operator coverage and adjusted funds from operations are sufficient to support the payout after interest expense and property investment requirements. At the end of 2025, Omega guided to 2026 adjusted FFO of $3.15 to $3.25 per diluted share.
Because Omega's second-quarter results are scheduled for release on July 29, 2026, the dividend announcement arrives before investors have the full quarterly operating update. The data suggests this is an area to monitor for confirmation rather than interpret in isolation. FMP cash-flow and financial-ratios datasets could be used to compare dividend obligations with operating cash generation, but REIT-specific measures such as adjusted FFO, funds available for distribution and tenant coverage should remain central. Debt-maturity data and SEC filing disclosures would further show whether the higher payout is being introduced alongside stable leverage and manageable refinancing requirements.
Stanley Black & Decker (NYSE: SWK)
Stanley Black & Decker declared a quarterly dividend of $0.84 per share, or $3.36 annualized, representing a 1.2% increase from the previous dividend of $0.83. The payment date is September 22, 2026, for shareholders of record on September 8, 2026. The stock's ex-dividend date is September 4, 2026, and its annual dividend yield is approximately 3.8%.
The increase is modest in percentage terms, but Stanley Black & Decker's dividend record gives the decision a different analytical weight. The company has raised its dividend annually since 1968, making continuity part of its established capital-allocation framework. That history means a one-cent increase should not automatically be treated as evidence of a broad operating acceleration. It is better viewed against the company's cash generation, restructuring progress and commitment to maintaining a long-running payout record.
Stanley Black & Decker entered 2026 targeting $700 million to $900 million in free cash flow and adjusted earnings of $4.90 to $5.70 per share. First-quarter sales rose 3% year over year to $3.8 billion, while organic sales were flat and adjusted gross margin declined by 20 basis points. That mix makes margin development and working-capital conversion more informative than the dividend increase alone. FMP income-statement and cash-flow datasets would help track gross margin, operating expenses, inventory and free cash flow. Analyst-estimate and price-target data could also illustrate whether consensus expectations are moving in response to operating results, although those estimates should be presented as market expectations rather than fundamental confirmation.
Comfort Systems USA (NYSE: FIX)
Comfort Systems USA declared a quarterly dividend of $0.90 per share, equivalent to $3.60 annualized. The increase from the previous quarterly dividend of $0.80 is 12.5%. It will be paid on August 24, 2026, to stockholders of record on August 13, 2026, with an ex-dividend date of August 12, 2026. Despite the double-digit increase, the annual yield remains approximately 0.2%.
The low yield is important context. For Comfort Systems, the dividend functions less as a primary income feature and more as one component of a broader capital-allocation policy. The company reported second-quarter revenue of $3.27 billion, up from $2.17 billion a year earlier. Net income increased to $441.6 million, or $12.53 per diluted share, from $230.8 million, or $6.53 per diluted share, while quarterly operating cash flow reached $1.14 billion, compared with $252.5 million in the prior-year period. Against those results, the higher payout represents a limited claim on current earnings and cash generation.
The more relevant question is how the company allocates the remainder of that cash across acquisitions, workforce investment, project capacity and balance-sheet management. Comfort Systems operates in mechanical, electrical and plumbing contracting, with exposure to commercial, industrial and institutional construction activity. FMP cash-flow and acquisition data would help place the dividend alongside deal spending and capital expenditures, while income-statement and segment data could show whether recent growth is broadly distributed or concentrated in specific end markets. Backlog disclosures from earnings materials would add another useful dimension by connecting current revenue strength with contracted work, without assuming that backlog converts into revenue or margins at a fixed rate.
Reading the Pattern: What These Moves Suggest
Taken together, these five increases do not point to a single sector-wide dividend trade. They show how the same corporate action can carry very different information depending on the company behind it. Bank of America and Avidia Bancorp are returning more capital from regulated balance sheets. Omega Healthcare Investors is making a small adjustment to a high-yield payout. Stanley Black & Decker is preserving a long-established dividend record, while Comfort Systems USA is raising a low-yield distribution from a position of strong recent cash generation.
The more useful signal is the relationship between the size of the increase and the financial capacity supporting it. A double-digit raise is not automatically stronger than a one-cent increase, just as a high yield is not automatically evidence of a more attractive or durable payout. Dividend decisions become analytically meaningful only when they are tested against earnings quality, free cash flow, leverage, capital requirements and the competing uses of cash.
That distinction is easier to see when the declarations are treated as the starting point rather than the conclusion. A research workflow built across FMP can place each increase beside operating cash flow, capital expenditures, earnings trends, leverage and valuation metrics, helping distinguish improving payout capacity from decisions driven mainly by policy, regulatory capital or dividend history.
The initial declarations can be captured through FMP's Dividends Calendar API, then compared with the Cash Flow Statement API to measure operating cash flow, capital expenditures and free cash flow across the group. The Income Statement API can place each raise against revenue, operating income and net income trends, while the Key Metrics TTM API adds comparable measures such as free cash flow yield, return on invested capital and net debt to EBITDA. Together, those datasets provide a more complete view of whether the payout change is supported by the underlying business.
Expectations data provides another layer. FMP's Financial Estimates API can show whether analyst forecasts for revenue and earnings are moving in the same direction as the payout, while Price Target Consensus and upgrades-and-downgrades datasets can indicate whether the market's published view is changing alongside management's capital-allocation decision. Those measures should not be treated as confirmation, but they can reveal whether a dividend increase is occurring during improving consensus sentiment, stable expectations or a widening gap between corporate actions and analyst assumptions.
The broader conclusion is that dividend increases work best as classification signals, not standalone conclusions. In this group, the raises reflect five distinct combinations of capital strength, income orientation, payout tradition and business momentum. The screen identifies where management has changed the distribution. The surrounding financial, valuation and expectations data explains what that change actually means.
From Declaration to Insight: Building a Repeatable Dividend Screen via FMP API
If dividend adjustments are going to function as usable signals, the process has to begin at the point where the decision actually occurs: the declaration itself. That means sourcing the data directly from the FMP Dividends Calendar API, which captures dividend announcements at the moment companies publish them, before those entries are absorbed into broader aggregated datasets.
Before running any queries, confirm that your API key is active. Once authenticated, the Dividends Calendar endpoint effectively becomes the intake layer for the entire workflow. It returns a structured dataset containing the ticker symbol, declared dividend amount, key payout dates (declaration, record, payment, and ex-dividend), yield, and payment frequency. That initial pull forms the starting universe from which dividend changes can be identified and analyzed.
Endpoint:
|
https://financialmodelingprep.com/stable/dividends-calendar?apikey=YOUR_API_KEY |
Sample Response:
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[ { "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
- Pull a fresh 14-day window from the Dividends Calendar API.
- For each ticker, fetch its prior payout via the historical dividend endpoint.
- Compute the percentage change using the formula above.
- Keep only companies posting 5%+ increases and yielding 2% or more.
Expanding Your Dividend Tracking Setup
Most dividend screens start out as simple monitoring systems. The initial goal is usually straightforward: capture new declarations quickly enough to feed a watchlist, weekly report, or alert workflow before the information gets absorbed into broader market data. Using the Financial Modeling Prep Free plan, that process stays lean and reactive, centered on pulling fresh entries from the Dividends Calendar as they are published.
The analysis becomes more useful once historical comparison enters the workflow. Access to roughly one year of dividend history through the Starter plan makes it possible to place each declaration against its prior payout rather than treating every entry as a standalone event. That shift matters because recurring dividends and genuine increases often look identical in raw calendar data. Once the historical layer is added, patterns begin to emerge around consistency, timing, and how management teams behave across different operating environments.
A longer historical window changes the screen again. With up to five years of dividend history available through the Premium, payout decisions can be evaluated across multiple business cycles instead of isolated reporting periods. At that depth, dividend changes stop functioning as simple event flags and start becoming part of a broader operating history — one that can be compared against earnings pressure, margin expansion, sector slowdowns, or shifts in capital allocation strategy over time.
When a Desk Tool Turns Into Firmwide Infrastructure
Most market-monitoring workflows do not begin as institutional systems. They start as tightly scoped analyst processes — a dividend screen running weekly, a historical comparison model maintained on a single desk, or a signal tracker built to support a specific coverage universe. The transition happens when those outputs start influencing conversations outside their original context. Once portfolio managers, strategy teams, and risk committees begin referencing the same screen, consistency becomes more important than the screen itself.
At that point, the analyst who built the workflow often becomes something else entirely: the internal advocate for standardization. The challenge shifts away from finding signals and toward ensuring every team is interpreting the same dataset under the same assumptions. That means aligning declaration feeds, refresh schedules, historical comparison windows, and filtering logic across desks. Without that coordination layer, firms gradually accumulate fragmented versions of the same model — similar screens producing slightly different conclusions because the underlying methodology drifted over time.
Centralized workflows reduce that friction. When dividend declaration data, historical payout comparisons, cash flow metrics, and analyst estimate revisions feed into a shared dashboard environment, research teams can spend less time reconciling discrepancies and more time debating interpretation. A portfolio manager reviewing a dividend signal should be looking at the same benchmark logic as the analyst who generated it and the risk team evaluating its exposure implications. Shared infrastructure turns isolated analysis into an institutional reference point.
As adoption broadens, governance naturally becomes part of the analytical process. Questions around lineage, revision tracking, access controls, and auditability start carrying as much weight as the signal itself. Teams want to know whether historical dividend records were revised, whether screening thresholds changed between quarters, and whether prior outputs can be reproduced under the same assumptions. In institutional environments, repeatability is credibility.
That is usually the stage where successful desk-level workflows migrate onto more formal infrastructure layers, not to change the analytical framework, but to preserve it as usage expands across teams. In practice, that often means consolidating data access and workflow management through systems designed for broader internal distribution, such as the Financial Modeling Prep Enterprise Plan, where the emphasis shifts toward stability, consistency, and operational transparency across the research stack.
Dividends as a Quiet Pulse on Corporate Confidence
The five increases captured through the FMP Dividends Calendar API show that payout changes are most useful when read in context, not isolation. Across sectors, they offer a compact view of how management teams are balancing confidence, cash generation and capital discipline.
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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.
