How To Squeeze Yields Up To 6.9% From Blue-Chip Stocks

Closeup of blue poker chip on red felt card table surface with spot light on chip

Preferred stocks are the little-known answer to the dividend question: How do I juice meaningful 5% to 6% yields from my favorite blue-chip stocks? “Common” blue chips stocks usually don’t pay 5% to 6%. Heck, the S&P 500’s current yield, at just 1.3%, is its lowest in decades.

But we can consider the exact same 505 companies in the popular index—names like JPMorgan Chase (JPM), Broadcom (AVGO) and NextEra Energy (NEE)—and find yields from 4.2% to 6.9%. If we’re talking about a million dollar retirement portfolio, this is the difference between $13,000 in annual dividend income and $42,000. Or, better yet, $69,000 per year with my top recommendation.

Most investors don’t know about this easy-to-find “dividend loophole” because most only buy “common” stock. Type AVGO into your brokerage account, and the quote that your machine spits back will be the common variety.

But many companies have another class of shares. This “preferred payout tier” delivers dividends that are far more generous.

Companies sometimes issue preferred stock rather than issuing bonds to raise cash. And these preferred dividends have a few benefits:

  • They receive priority over dividends paid on common shares.
  • Sometimes, preferred dividends are “cumulative”—if any dividends are missed, those dividends still have to be paid out before dividends can be paid to any other shareholders.
  • They’re typically far juicier than the modest dividends paid out on common stock. A company whose commons yield 1% or 2% might still distribute 5% to 7% to preferred shareholders.

But it’s not all gravy.

You’ll sometimes hear investors call preferreds “hybrid” securities. That’s because they act like a part-stock, part-bond holding. The way they resemble bonds is how they trade around a par value over time, so while preferreds can deliver price upside, they don’t tend to deliver much.

No, the point of preferreds is income and safety.

Now, we could go out and buy individual preferreds, but there’s precious little research out there allowing us to make a truly informed decision about any one company’s preferreds. Instead, we’re usually going to be better off buying preferred funds.

But which preferred funds make the cut? Let’s look at some of the most popular options, delivering anywhere between 4.2% to 6.9% at the moment.

Wall Street’s Two Largest Preferred ETFs

I want to start with the iShares Preferred and Income Securities (PFF, 4.2% yield) and Invesco Preferred ETF (PGX, 4.5%). These are the two largest preferred-stock ETFs on the market, collectively accounting for some $27 billion in funds under management.

On the surface, they’re pretty similar in nature. Both invest in a few hundred preferred stocks. Both have a majority of their holdings in the financial sector (PFF 60%, PGX 67%). Both offer affordable fees given their specialty (PFF 0.46%, PGX 0.52%).

There are a few notable differences, however. PGX has a better credit profile, with 54% of its preferreds in BBB-rated (investment-grade debt) and another 38% in BB, the highest level of “junk.” PFF has just 48% in BBB-graded preferreds and 22% in BBs; nearly a quarter of its portfolio isn’t rated.

Also, the Invesco fund spreads around its non-financial allocation to more sectors: utilities, real estate, communication services, consumer discretionary, energy, industrials and materials. Meanwhile, iShares’ PFF only boasts industrial and utility preferreds in addition to its massive financial-sector base.

PGX might have the edge on PFF, but both funds are limited by their plain-vanilla, indexed nature. That’s why, when it comes to preferreds, I typically look to closed-end funds.

Closed-End Preferred Funds

CEFs offer a few perks that allow us to make the most out of this asset class.

For one, most preferred ETFs are indexed, but all preferred CEFs are actively managed. That’s a big advantage in preferred stocks, where skilled pickers can take advantage of deep values and quick changes in the preferred markets, while index funds must simply wait until their next rebalancing to jump in.

Closed-end funds also allow for the use of debt to amplify their investments, both in yield and performance. Should the manager want, CEFs can also use options or other tools to further juice returns.

And they often pay out their fatter dividends every month!

Take John Hancock Preferred Income Fund II (HPF, 6.9% yield), for example. It’s a tighter portfolio than PFF or PGX, at just under 120 holdings from the likes of CenterPoint Energy (CNP), U.S. Cellular (USM) and Wells Fargo (WFC).

Manager discretion means a lot here. That is, HPF doesn’t just invest in preferreds, which are 70% of assets. It also has 22% invested in corporate bonds, another 4% or so in common stock, and trace holdings of foreign stock, U.S. government agency debt and cash. And it has a whopping 32% debt leverage ratio that really helps prop up the yield and provide better returns (though at the cost of a bumpier ride).

You have a similar situation with Flaherty & Crumrine Preferred and Income Securities Fund (FFC, 6.7%).

Here, you’re wading deep into the financial sector at nearly 80% exposure, with decent-sized holdings in utilities (7%) and energy (7%). Credit quality is roughly in between PFF and PGX, with 44% BBB, 37% BB and 19% unrated.

Nonetheless, smart management selection (and a healthy 31% in debt leverage) has led to far better, albeit noisier, returns than its indexed competitors. The Cohen & Steers Select Preferred and Income Fund (PSF, 6.0%) is about as pure a play as you could want in preferreds.

And it’s also a pure performer.

PSF is 100% invested in preferred stock (well, more like 128% if you count debt leverage), and actually breaks out its preferreds into institutionals that trade over-the-counter (83%), retail preferreds that trade on an exchange (16%) and floating-rate preferreds that trade OTC or on exchanges (1%).

Like any other preferred fund, you’re heavily invested in the financial sector at nearly 73%. But you do get geographic diversification, as only a little more than half of PSF’s assets are invested in the U.S. Other well-represented countries include the U.K. (13%), Canada (7%) and France (6%).

What’s not to love?

Brett Owens is chief investment strategist for Contrarian Outlook. For more great income ideas, get your free copy his latest special report: Your Early Retirement Portfolio: 7% Dividends Every Month Forever.

I graduated from Cornell University and soon thereafter left Corporate America permanently at age 26 to co-found two successful SaaS (Software as a Service) companies. Today they serve more than 26,000 business users combined. I took my software profits and started investing in dividend-paying stocks. Today, it’s almost impossible to find good stocks that pay a quality yield. So I employ a contrarian approach to locate high payouts that are available thanks to some sort of broader misjudgment. Renowned billionaire investor Howard Marks called this “second-level thinking.” It’s looking past the consensus belief about an investment to map out a range of probabilities to locate value. It is possible to find secure yields of 6% or more in today’s market – it just requires a second-level mindset.

Source: How To Squeeze Yields Up To 6.9% From Blue-Chip Stocks

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Critics:

A blue chip is stock in a stock corporation (contrasted with non-stock one) with a national reputation for quality, reliability, and the ability to operate profitably in good and bad times. As befits the sometimes high-risk nature of stock picking, the term “blue chip” derives from poker. The simplest sets of poker chips include white, red, and blue chips, with tradition dictating that the blues are highest in value. If a white chip is worth $1, a red is usually worth $5, and a blue $25.

In 19th-century United States, there was enough of a tradition of using blue chips for higher values that “blue chip” in noun and adjective senses signaling high-value chips and high-value property are attested since 1873 and 1894, respectively. This established connotation was first extended to the sense of a blue-chip stock in the 1920s. According to Dow Jones company folklore, this sense extension was coined by Oliver Gingold (an early employee of the company that would become Dow Jones) sometime in the 1920s, when Gingold was standing by the stock ticker at the brokerage firm that later became Merrill Lynch.

Noticing several trades at $200 or $250 a share or more, he said to Lucien Hooper of stock brokerage W.E. Hutton & Co. that he intended to return to the office to “write about these blue-chip stocks”. It has been in use ever since, originally in reference to high-priced stocks, more commonly used today to refer to high-quality stocks.

References:

How The Power Of Predictive Analytics Can Transform Business

Tableau analytics visual

With the acceleration of digital transformation in business, most CTOs, CIOs, and even middle management or analysts are now asking, “What’s next with data?” and what ongoing role will technology play in both digital and data transformations. Other questions that keep these individuals up at night include:

  • How can people throughout all organizational levels be more empowered to use data and help others make better decisions?
  • What prevents people from more deeply exploring and using data?
  • In what ways can analytics tools and methods help more people use data in the daily routine of business—asking questions, exploring hypotheses, and testing ideas?

With this in mind, plus observations and discussions with many Tableau customers and partners, it seems that today’s circumstances, behaviors, and needs make it the right time for predictive data analytics to help businesses and their people solve problems effectively.

Current realities and barriers to scale smarter decision-making with AI 

With growing, diverse data sets being collected, the analytics use cases to transform data into valuable insights are growing just as fast. Today, a wide range of tools and focused teams specialize in uncovering data insights to inform decision-making, but where organizations struggle is striking the right balance between activating highly technical data experts and business teams with deep domain experience.

Until now, using artificial intelligence (AI), machine learning (ML), and other statistical methods to solve business problems was mostly the domain of data scientists. Many organizations have small data science teams focused on specific, mission-critical, and highly scalable problems, but those teams usually have a long project list to handle.

At the same time though, there are a large number of business decisions that rely on experience, knowledge, and data—and that would greatly benefit from applying more advanced analysis techniques. People with domain knowledge and proximity to the business data could benefit greatly, if they had access to these techniques.

Instead, there’s currently a back-and-forth process of relying on data scientists and ML practitioners to build and deploy custom models—a cycle that lacks agility and the ability to iterate quickly. By the end, the data that the model was trained on could be stale and the process starts again. But organizations depend on business users to make key decisions daily that don’t rise to the priority level of their central data science team.

The opportunity to solve data science challenges

This is where there’s an opportunity to democratize data science capabilities, minimizing the trade-offs between extreme precision and control versus the time to insight—and the ability to take action on these insights. If we can give people tools or enhanced features to better apply predictive analytics techniques to business problems, data scientists can gain time back to focus on more complex problems. With this approach, business leaders can enable more teams to make data-driven decisions while continuing to keep up with the pace of business. Additional benefits gained from democratizing data science in this way include:

  • Reducing data exploration and prep work
  • Empowering analyst experts to deliver data science outputs at lower costs
  • Increasing the likelihood of producing successful models with more exploration of use cases by domain experts
  • Extending, automating, and accelerating analysis for business groups and domain experts
  • Reducing time and costs spent on deploying and integrating models
  • Promoting responsible use of data and AI with improved transparency and receiving guidance on how to minimize or address bias

Business scenarios that benefit from predictive analytics 

There are several business scenarios where predictive capabilities can be immensely useful.

Sales and marketing departments can apply it to lead scoring, opportunity scoring, predicting time to close, and many other CRM-related cases. Manufacturers and retailers can use it to help with supply chain distribution and optimization, forecasting consumer demand, and exploring adding new products to their mix. Human resources can use it to assess the likelihood of candidates accepting an offer, and how they can adjust salary and benefits to meet a candidate’s values. And companies can use it to explore office space options and costs. These are just a few of the potential scenarios.

A solution to consider: Tableau Business Science

We are only at the beginning of exploring what predictive capabilities in the hands of people closely aligned with the business will unlock. AI and ML will continue to advance. More organizations, in a similar focus as Tableau, will also keep looking for techniques that can help people closest to the business see, understand, and use data in new ways to ask and answer questions, uncover insights, solve problems, and take action.

This spring Tableau introduced a new class of AI-powered analytics that gives predictive capabilities to people who are close to the business. In this next stage of expanded data exploration and use, we hope business leaders embrace data to help others make better decisions, and to provide transparent insight into the factors influencing those decisions.

When people can think with their data—when analysis is more about asking and answering questions than learning complex software or skills—that’s when human potential will be unleashed, leading to amazing outcomes. Learn more about Tableau Business Science, what this technology gives business teams, and the value it delivers to existing workflows.

Olivia Nix is a Senior Manager of Product Marketing at Tableau. She leads a team focused on the use of AI and ML in analytics and engagement, including how to use technology to enable more people in organizations to make data-driven decisions. Olivia has been at Tableau for four years where she has worked closely with development teams on new product launches. Prior to Tableau, Olivia worked as an analyst at the Pew Center on Global Climate Change (now C2ES) and Johnson Controls. She has her MBA from the UCLA Anderson School of Management.

Source: How The Power Of Predictive Analytics Can Transform Business

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Critics:

Predictive analytics encompasses a variety of statistical techniques from data mining, predictive modelling, and machine learning that analyze current and historical facts to make predictions about future or otherwise unknown events.

In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision-making for candidate transactions.

The defining functional effect of these technical approaches is that predictive analytics provides a predictive score (probability) for each individual (customer, employee, healthcare patient, product SKU, vehicle, component, machine, or other organizational unit) in order to determine, inform, or influence organizational processes that pertain across large numbers of individuals, such as in marketing, credit risk assessment, fraud detection, manufacturing, healthcare, and government operations including law enforcement.

Predictive analytics is used in actuarial science,marketing,business management, sports/fantasy sports, insurance,policing, telecommunications,retail, travel, mobility, healthcare, child protection, pharmaceuticals,capacity planning, social networking and other fields.

One of the best-known applications is credit scoring,[1] which is used throughout business management. Scoring models process a customer’s credit history, loan application, customer data, etc., in order to rank-order individuals by their likelihood of making future credit payments on time.

Predictive analytics is an area of statistics that deals with extracting information from data and using it to predict trends and behavior patterns. The enhancement of predictive web analytics calculates statistical probabilities of future events online. Predictive analytics statistical techniques include data modeling, machine learning, AI, deep learning algorithms and data mining.Often the unknown event of interest is in the future, but predictive analytics can be applied to any type of unknown whether it be in the past, present or future.

For example, identifying suspects after a crime has been committed, or credit card fraud as it occurs.The core of predictive analytics relies on capturing relationships between explanatory variables and the predicted variables from past occurrences, and exploiting them to predict the unknown outcome. It is important to note, however, that the accuracy and usability of results will depend greatly on the level of data analysis and the quality of assumptions.

Predictive analytics is often defined as predicting at a more detailed level of granularity, i.e., generating predictive scores (probabilities) for each individual organizational element. This distinguishes it from forecasting. For example, “Predictive analytics—Technology that learns from experience (data) to predict the future behavior of individuals in order to drive better decisions.”In future industrial systems, the value of predictive analytics will be to predict and prevent potential issues to achieve near-zero break-down and further be integrated into prescriptive analytics for decision optimization.

See also

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