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Goldman Sachs Uses AI to Identify Five Dividend Winners

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  Published in Stocks and Investing on Wednesday, November 26th 2025 at 8:28 GMT by 24/7 Wall St

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Goldman Sachs Spotlights Dividend Stocks Using AI – Five Strong Buys Now

Goldman Sachs’ latest research note, released on 26 November 2025, takes a deep dive into how the investment bank’s artificial‑intelligence (AI) engine is reshaping the way analysts identify high‑quality dividend‑paying equities. The article, titled “Goldman Sachs spotlights dividend stocks using AI – 5 strong buys now,” draws on the bank’s proprietary “Eagle” platform (an AI‑powered analytics hub that blends natural‑language processing, machine‑learning models, and big‑data analytics) to surface five dividend leaders that the research team believes are poised for sustained payout growth in the current macro‑environment.

Why the AI‑Driven Dividend Play Matters

The piece opens with a reminder that dividend investing has regained traction as investors increasingly look for steady income streams in a backdrop of high bond yields, tightening credit, and a still‑elevated risk‑free rate. In the past two years, corporate dividend yields have fluctuated sharply as companies juggle capital allocation decisions amid volatile commodity prices and supply‑chain constraints. Goldman Sachs analysts argue that in this uncertain setting, a data‑driven approach can identify firms that not only pay attractive yields but also have the resilience to maintain or increase those payouts over time.

Goldman’s research note cites a Bloomberg interview (link included in the article) with the bank’s Chief Investment Officer, who emphasized that “AI enables us to sift through the noise of earnings calls, regulatory filings, and macro reports faster and more consistently than a human team could.” By feeding structured financial data and unstructured text into the Eagle model, the bank can produce a real‑time “Dividend Sustainability Score” that weighs factors such as payout ratio, cash‑flow coverage, debt‑to‑EBITDA, and growth‑free cash‑flow trends.

Methodology: How the AI Picks Dividend Winners

The article details the AI workflow: first, the system scans thousands of quarterly earnings transcripts for keywords related to “dividend,” “payout,” and “share buyback.” Next, it cross‑checks these signals against the company’s financial statements and macro‑economic indicators (interest rates, inflation expectations, and commodity prices). The AI then applies a supervised learning model trained on historical dividend data to predict the likelihood that a firm’s current dividend will be maintained or increased next year.

Goldman Sachs stresses that the model is not “black box” – analysts still review the top‑ranked companies and manually verify that the AI’s assumptions hold. The research note even provides a side‑by‑side chart comparing the AI’s dividend sustainability score to the traditional “free‑cash‑flow‑to‑debt” metric, showing a high correlation but with a sharper distinction between true dividend champions and companies with high cash flow but also heavy debt loads.

The piece also links to a recent Goldman Sachs webinar titled “AI in Equity Research” (link included) where the team discussed how the same AI framework is being adapted for growth‑equity and value‑equity screens. The synergy between the dividend screen and broader equity screens underscores the bank’s ambition to offer a unified, AI‑driven research ecosystem.

Five Dividend‑Focused Picks – In Depth

Goldman’s note then presents the five dividend stocks that have earned the AI’s top recommendation for the near term. Each company is briefly profiled, with key numbers and rationale highlighted:

Each of these firms also appears on the Goldman “Dividend Sustainability” leaderboard that the bank released earlier this year, a proprietary ranking that combines AI‑derived metrics with fundamental analysis. Analysts note that the AI’s top‑five picks share a common trait: a low or moderate payout ratio, high cash‑flow coverage, and an underlying growth catalyst (acquisitions, new product lines, or favorable regulatory changes).

Broader Context and Key Takeaways

The research note closes with a broader commentary on the macro backdrop. As the Federal Reserve’s policy rate hovers at 5.5 % and bond yields remain elevated, many investors are turning to dividend equities for yield. Goldman Sachs’ AI model, the note argues, provides a systematic way to filter out “yield‑hunters” that are simply riding a low share‑price wave. Instead, the model surfaces firms with robust fundamentals that can generate dividend growth even in a higher‑rate environment.

The article links to a recent Goldman Sachs market‑watch briefing, which outlines the bank’s view that the “dividend race” is now transitioning from price‑based chasing to a more disciplined, data‑driven selection of true dividend quality. The note also references a companion research release titled “AI‑Enabled Dividend Valuation: The Next Frontier,” which expands on the statistical underpinnings of the AI scoring system.

For investors, the takeaway is clear: AI is no longer a buzzword at Goldman Sachs; it is a core part of the research engine. By integrating large‑scale text mining, predictive modeling, and rigorous fundamental checks, the bank claims to be able to identify dividend stocks that offer both attractive yields and the potential for future growth—an appealing proposition for those looking to build a resilient income portfolio in 2025 and beyond.

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