Ray Dalio and Warren Buffett Own Both Own These Three Stocks

Ray Dalio and Warren Buffett are both at the top of their profession: Dalio's Bridgewater Associates usurped the title of best-performing money management firm in history from George Soros in recent years. Warren Buffett of Berkshire Hathaway is the preeminent investor of the century with a long track record of above-average returns.

As investors, they could not be more different. Warren Buffett famously assesses the strength of companies' fundamentals when researching stocks, looking for good but cheap businesses. Ray Dalio is a macro investor who hunts for companies poised to benefit from macro events he predicts based on his unique economic model. Yet, in the intersection of their views lie several stocks they both embrace. The largest on the list are: General Electric ( GE ), Johnson & Johnson ( JNJ ) and Davita ( DVA ).

These results were found using GuruFocus' Aggregated Portfolio Screener, which finds the stocks that two or more investors like.

General Electric ( GE )

Ray Dalio owns 886,750 shares of GE, valued as $18 million as of March 31, 2012, which accounts for 0.28% of his equity portfolio. Warren Buffett owns 7,777,900 shares of GE, valued as $156 million as of March 31, 2012, which accounts for 0.21% of his equity portfolio.

General Electric matches Buffett's "buy and hold forever" type of company. He bought his holding over five years ago, and it is one of the largest and most diversified industrial corporations in the world. From 2002 to 2008, its revenue increased annually except for one year, and it has produced steady cash flow over $20 billion for the last decade. In the fourth quarter of 2011, GE generated record cash from industrial operating activities of $5.5 billion.

Revenue at GE declined 2 percent in 2011 over 2010, and was up 7 percent excluding the impact of NBC Universal. General Electric sold its majority stake in NBCUniversal to Comcast in January 2011.

The company, which was hit hard by the financial crisis, had to slash its dividend from $1.24 a year in 2008 to $0.61 a year in 2009. In December 2011, it was able to increase its quarterly dividend for the fourth time in two years as its financial situation improved. The raise was $0.02 to $0.17.

Ray Dalio has also bought and sold shares of GE for over five years. Most recently, he sold 218,600 shares in the first quarter at an average price of $19. Last year, he bought and sold shares in each quarter as the stock vacillated between a broad 52-week range of $14 to $21, reflecting his shorter-term strategy.

General Electric has a market cap of $196.29 billion; its shares were traded at around $18.65 with a P/E ratio of 13.5 and P/S ratio of 1.3. The dividend yield of General Electric stocks is 3.7%.

Johnson & Johnson ( JNJ )

Ray Dalio owns 222,093 shares of JNJ, valued as $15 million as of March 31, 2012, which accounts for 0.23% of his equity portfolio. Warren Buffett owns 29,018,127 shares of JNJ, valued as $1.9 billion as of March 31, 2012, which accounts for 2.5% of his equity portfolio.

Warren Buffett had been holding Johnson and Johnson for a long-term period, but recently began reducing his stake, selling a total of 13,606,436 shares in 2011.

In March 2012, Buffett said that he would consider selling his stake in the company because of its recent problems. "J&J obviously has messed up in a lot of ways in the last few years," he said on a CNBC interview. "You know, my friend Jim Burke used to run that and it does not have the reputation now that it had, you know, a few years back. It's still got a lot of wonderful products and it's got a wonderful balance sheet and all of that, but there have been too many mistakes made at Johnson & Johnson."

He added that the company was "still an attractive business at its price," but if he needed money it would be on his sell list.

J&J has had several product recalls in recent years and most recently, in the first quarter of 2012, it had a suspension of manufacturing at its McNeil Consumer Healthcare facility in Pennsylvania that significantly impacted U.S. sales of over the counter medicines.

Ray Dalio also sold about half of his much smaller holding in the first quarter and has made numerous short-term trades of the company for over five years.

Johnson & Johnson is engaged in the manufacture and sale of a broad range of products in the health care field in many countries of the world. Johnson & Johns has a market cap of $169.62 billion; its shares were traded at around $62.53 with a P/E ratio of 12.3 and P/S ratio of 2.6. The dividend yield of Johnson & Johns stocks is 3.9%. Johnson & Johns had an annual average earnings growth of 7.2% over the past 10 years. GuruFocus rated Johnson & Johns the business predictability rank of 4-star.

Davita ( DVA )

Ray Dalio owns 151,263 shares of DVA, valued as $14 million as of March 31, 2012, which accounts for 0.21% of his equity portfolio. Warren Buffett owns 6,000,000 shares of DVA, valued as $541 million as of March 31, 2012, which accounts for 0.72% of his equity portfolio.

It is speculated that newly purchased Davita was a stock chosen by one of Buffett's new investment managers. The company has an outstanding balance sheet with a 10-year annual revenue growth rate of 18.6%, 17.8% for EBITDA and 10.8 percent for free cash flow. It bears $1.9 billion in cash and about $5 billion in long-term liabilities and debt.

Davita provides kidney dialysis treatment to patients with chronic kidney failure and end state renal disease. It has 1,841 outpatient dialysis centers in the U.S. serving about 145,000 patients each week. There are almost 400000 kidney patients who undergo dialysis in the U.S. in every year. Davita has a small international presence with 15 dialysis centers in three countries outside the U.S. It also leads the nation in many areas measuring quality of care.

In the first quarter of 2012, Davita acquired 28 centers and opened 13 in the U.S. It also opened four centers outside the U.S. It raised its operating income guidance for 2012 to $1.23 billion to $1.31 billion, from its previous estimate of $1.2 billion to $1.2 billion.

Though the industry is quite consolidated which will limit future acquisitions, the company expects to achieve growth also through an expected increase in the number of patients each year, de novos and same-store growth and financial leverage possible in the form of additional debt or buying back stock, Davita CEO Kent Thiry said on the first quarter conference call.

The company is also expecting pricing pressure on the cost of care per patient due to higher pharma expense, wage and benefit costs and travel costs.

Davita Inc. has a market cap of $7.52 billion; its shares were traded at around $83.83 with a P/E ratio of 14.2 and P/S ratio of 1.1. Davita Inc. had an annual average earnings growth of 17.8% over the past 10 years. GuruFocus rated Davita Inc. the business predictability rank of 3.5-star.

Texas Teachers' Harris Taking Alternative Investing to New Risk

June 6 (Bloomberg) -- After working for almost two decades as a money manager, Britt Harris at age 45 was what most people would consider a success. Bridgewater Associates LP's Ray Dalio and Bob Prince had just tapped him to be chief executive officer of the world's largest hedge fund.

A father of four, Harris also found time to coach his kids' baseball teams and teach Bible classes at his church. Still, something was gnawing at him. "I didn't sleep for one night," the Texas native recalls. "I didn't sleep for a week. Then, after not having slept for three months, I told Bob and Ray I wanted to resign."

Although Prince and Dalio urged Harris to remain, he quit Bridgewater in June 2005 after just six months, Bloomberg Markets magazine reports in its July issue. Harris says he felt that he wasn't contributing enough to the firm or the wider world, so he embarked on an 18-month-long search for meaning. He traveled to Asia and New Zealand. He tried teaching, setting up a class on investing at Texas A&M University, his alma mater.

Then, in late 2006, a headhunter approached him about taking the top investing job at the Teacher Retirement System of Texas.

It was a place where he could make an impact. With $110.3 billion under management as of March 31, TRS is the fifth- largest public pension plan in the U.S.

When Harris joined in 2007, the teachers fund wanted someone who could boost returns without making risky bets that could jeopardize the pensions of its 1.3 million public school teachers and state university employees. "It's a plan I really care about," says Harris, who's now 54. "It's my home state, a place I love."

Double Squeeze

Pension funds across the U.S. are facing an unprecedented double squeeze: Baby boomers entering retirement are placing growing demands on resources, while investment returns during the past decade have dropped. Nationwide, public pensions faced more than $4 trillion in unfunded liabilities as of October, according to Joshua Rauh of Northwestern University.

At TRS, Harris is reacting by ramping up stakes in so- called alternative assets ranging from private equity to real estate to hedge funds. The Texas fund had about a third of its money in these investments at the end of March -- more than any of the 10 largest public pension funds, according to London researcher Preqin Ltd. The California Public Employees' Retirement System has 25 percent of its $237.6 billion of assets in such investments.

Bridgewater Stake

Harris, a devout Christian with a taste for Texas barbecue, is also forming partnerships with Wall Street firms. He has pledged $3 billion each to two private-equity joint ventures, with Apollo Global Management LLC and KKR & Co. He'll be investing in individual deals with them rather than solely placing money in their funds, as other pension plans do. And in February, the Texas fund bought a 2.5 percent private- equity stake in Bridgewater, Harris's former employer, for $250 million.

The moves are controversial. "Are they in the business of managing employee pensions or are they in the business of running hedge funds on Wall Street?" says Edward Siedle, a former Securities and Exchange Commission attorney who's now in the private sector investigating pension fraud. "When you look at public pension partnerships with Wall Street, generally they end up bad for the public pensions and good for Wall Street."

Returns Improve

Harris, who recused himself from decision making on the Bridgewater investment, says the hedge fund has consistently made high returns and is an attractive investment.

Why Correlations Are Unreliable Risk Indicators

I just finished reading Maneet Ahuja’s new book, The Alpha Masters: Unlocking the Genius of the World’s Top Hedge Funds. It’s a great book, packed with investing insight. I have no idea how she got all these guys to talk on the record: Ray Dalio, Tim Wong, Pierre Lagrange, John Paulson, Mark Lasry, Sonia Gardner, David Tepper, Bill Ackman, Daniel Loeb, James Chanos and Boaz Weinstein, plus an introduction by Mohamed El-Erian and an afterword by Myron Scholes.

Unlike some profiles that focus on lifestyles or dramatic events, these concentrate on how the subject built a world-class hedge fund and how he or she invests. These topics are not as closely connected as you might think. You obviously need to be a great investor to found a hedge fund that succeeds in the long run, but it’s far from the only requirement. The book is fun to read and contains important advice on almost every page.

I’m not going to review the book here. Instead I want to expand on a great point made by Ray Dalio of Bridgewater Associates. “To create the proper balance and diversification is even more important than any particular bets, which is the opposite of how most investors operate.”

He gives a simple example of an investor with 15 different uncorrelated bets, each with an expected return of 3% and a standard deviation of return of 10%. If you combine two bets, your standard deviation falls 29% to 7.1%. If you take all 15 bets, your standard deviation falls to 2.6% (the book inexplicably claims the reduction from the second bet is 15% and that 15 bets reduces your risk 80%, in fact that takes 25 bets). This is basic statistics, with equal volatility uncorrelated bets the standard deviation declines with the square root of the number of bets.

Then Dalio makes a less familiar point. “The most important rule is not to compare the correlations against each other in a quantitative sense, but according to their drivers.”

One problem with using correlations in risk analysis is you may misestimate them; things you think are uncorrelated may not be. A common way to misestimate is to assume correlations from the recent past will continue into the future. But it is possible to form moderately reliable correlation estimates by combining careful study of history with fundamental analysis. I believe portfolios constructed from these estimates can deliver long-term superior performance.

There is a more subtle problem, one that can hurt you even if your estimates are perfect. Suppose Dalio’s bets above are coin flips, heads you win 13%, tails you lose 7%. These bets have the required 3% expected return and 10% standard deviation of return. If you make one bet, there is a 50% chance you will lose money. If you make 15 bets, you might think you reduce your probability of losing money by the square root of 15, to 13%. If you think that, you’re wrong. Correlation tells you what happens to your standard deviation, which is not necessarily what happens to your risk.

The key problem is correlation is only a pairwise concept. Since the bets are uncorrelated, we know that if we pick any two, the chance is 1 in 4 that both will lose 7%, 1 in 4 that both will make 13%, and 1 in 2 that one will lose and one will win. Knowing that all the pairwise bets are uncorrelated tells us very little about how 15 bets will turn out. That is what Dalio means by understanding the drivers.

This next part gets a bit mathematical, but it’s worth following because it’s so important. Suppose there are two possibilities: the economy will do well and nine of the 15 bets will pay off, or the economy will do badly and only five of the 15 bets will pay off. The probability of a good economy is 5/8, and the probability of a bad economy is 3/8.

The expected number of bets that win is (5/8)*9 + (3/8)*5 = 60/8 = 7.5, so there is the required 50% chance for each bet to win. Suppose we are told that one bet paid off. This increases the probability that we are in the good state from 5/8 to 3/4, by Bayes Rule. If we are in the good state, the probability is 8/14 that any given other bet will win. If we are in the bad state, the probability is 4/14. The unconditional chance that any given other bet will win is (3/4)*8/14 + (1/4)*4/14 = 28/56 = 1/2. This is all “uncorrelated” means: that the chance of any bet winning is the same whatever I learn about the outcome of any other bet.

If this is the situation, the chance of losing money making all 15 bets is 37.5%, almost three times the 13% naïve calculation that was done above. The problem is that although there were 15 uncorrelated bets, there was a single driver (whether the economy was good or bad).

This may seem like a technicality that is not important for real investing. Nothing could be farther from the truth. It is easy to find uncorrelated bets, thousands of them. Take any stock, for example, and hedge it with its industry index. The result will be close to uncorrelated with some other stock hedged with its industry index. But a diversified portfolio of 100 stocks hedged with their respective industry indices will not have 10% of the risk of any single position. It may have 10% of the standard deviation, but the probability of a tail event may be almost as great as holding a single position.

If there are too many numbers in that explanation, here is a less quantitative example. Suppose you wanted to bet on the percent of the vote the winning candidate got in the 2010 US congressional election. The median was 63%. If I told you the incumbent was a Republican, the median is still about 63%, the incumbent’s party is uncorrelated with your bet. If I told you the winning candidate was a Democrat, the median is still about 63%. The winning candidate’s party is also uncorrelated. But if I told you the incumbent was a Republican and the winning candidate was a Democrat, the median drops to 53% (there were only three such races), and you will lose all the time if you bet the winning candidate will get over 63% of the vote. Two pieces of information are individually uncorrelated with the bet, but when combined they have a high correlation with the bet.

Here is one final example that illustrates another good way to think about correlation. Suppose Dalio’s bets are all bonds that cost $100 and are supposed to pay $1 per quarter interest and $101 at maturity in one year. The bonds will all make their three quarterly interest payments but may default if the issuer cannot refinance at maturity. The probability of that is 1/101 for each bond, and the defaults are uncorrelated.

The expected return from each bond is (100/101)*$4 - (1/101)*$97 = $303/101 = $3, as required. The standard deviation is $10. Because the bonds are uncorrelated, the probability of any two defaulting is one in 101 squared, or 1/10,201. This leads some people to assume the probability of more than two bonds defaulting must be negligible.

Imagine I have a hat with 10,201 slips of paper in it. Some of the papers have the names of bonds written on them. I’m going to draw one slip to see which bonds default. Since I know each bond has 1/101 chance of default, each bond must be on 101 different slips of paper. Since I know the defaults are uncorrelated, each pair of bonds must be on exactly one slip of paper. But that’s all I know. There are many ways to write the bond’s names on papers to satisfy these conditions.

For example, I could write all 15 bonds on one piece of paper, then each bond alone on 100 other pieces of paper each, leaving 8,700 blank slips. That makes the chance that all 15 bonds default 1 in 10,201. That’s low, but if you take risks like this every day, sooner or later one will catch up with you. When it does, someone half-trained in mathematics will say the probability of the event was 1 in 101 to the 15th power, something that should never have happened in the history of the universe. They will be wrong because they do not understand correlation. Uncorrelated bets are no guarantee that you will not have extreme tail events. As in the first example, you have 15 uncorrelated events, but only one driver.

Another thing I can do is write three bonds on 35 pieces of paper. Each individual bond is on seven of these slips, each time with a different pair of other bonds. Then I write each bond by itself on 94 slips of paper and leave 8,756 blank slips. Now the probability of more than two defaults is 35/10,201, or 0.3%. It’s low, but it’s not negligible.

This raises the question of how many drivers there are in the world, on which you can bet enough money to be meaningful to a portfolio. I don’t know the answer, but I’d guess it’s something like Dalio’s 15. Call it five that are available to any investor in low-cost, simple vehicles, five that are for professionals, and five that are cutting-edge hedge fund strategies. In fact, one important economic purpose of hedge funds is to seek out new drivers, which eventually become well-understood and liquid enough to be offered in cheaper form to less sophisticated investors, and eventually to be incorporated in index funds for everyone at minimal cost.

Fifteen drivers doesn’t mean you only make 15 investments. Finding uncorrelated bets that depend on the same driver still reduces your risk, just not all the way to zero, and perhaps not your extreme tail risk at all. Correlation is a powerful tool for building portfolios, but never confuse a tool with a driver.