|Executive Summary | Portfolio | Guru Analysis | Watch List|
|Executive Summary||August 1, 2014|
We're now well over halfway into the Federal Reserve's tapering of its quantitative easing program, and, so far, the dire predictions many had for the US economy just aren't coming to fruition.
New claims for unemployment, for example, fell to their lowest level since early 2006 in the week ending July 19. That put them an impressive 14% below where they stood a year earlier. Continuing claims, the data for which lag new claims by a week, were 16% below year-ago levels. New claims moved upward a bit in the most recent week, but still remain at very encouraging levels -- some 8.7% below year-ago levels.
Private payroll processor ADP said the private sector added 218,000 jobs in July, meanwhile, the fourth straight month the figure has been over 200,000. July's jobs-added number was second only to June's 281,000 so far in 2014. The Labor Department's July jobs report hadn't been released as of this writing, but hopefully we'll see a similarly strong number from that.
The first GDP report for the second quarter was also released, and it showed that economic output rose at a strong 4% annualized pace. That supported the idea that the weak first quarter growth was largely caused by bad winter weather.
While conflicts continue in the Middle East and Ukraine, the geopolitical turmoil hasn't sent gas prices upward. In fact, according to AAA, abundant refinery production has pushed gas prices down over the past month. "Oil markets remain relatively unaffected because there has not been an impact to supply or distribution," the group said. "Meanwhile, domestic refinery utilization reached its highest level of the year last week, which has helped to push gas prices lower."
Inflation does continue to be stronger than we've seen it for some time, however. The Consumer Price Index rose 0.3% in May, according to the Labor Department. That put it 2.1% ahead of its year-ago pace for the second straight month, the biggest year-over-year gains in almost two years. Core inflation, which strips out volatile food and energy prices, was up 1.9% year-over-year. That figure is hardly troubling, but inflation remains something to keep an eye on going forward.
As for the Fed, it again reduced its bond-buying program by $10 billion per month at its latest meeting. That means it's now buying $25 billion in bonds a month, down from a peak of $85 billion.
Overall since our last newsletter, the S&P 500 returned -1.4%, while the Hot List returned 0.4%. For the year, the portfolio stands at -14.0% vs. 4.5% for the S&P. Since its inception in July 2003, the Hot List is far outpacing the index, having gained 212.9% vs. the S&P's 93.0% gain.
Enron and WorldCom: They are two of the most infamous names in stock market history. Both of these apparent growth dynamos went up in flames amid major accounting frauds in the early 2000s. And, in both cases, investors who thought they were hitting paydirt were left with nothing.
What's amazing is that if the investing world had paid a little more attention to quantitative models, a lot of that pain could've been avoided. That's one of the many interesting things I've learned from Wesley Gray and Tobias Carlisle's book Quantitative Value. In the book, Gray and Carlisle examine how using quantitative models can help investors beat the market over the long haul. One of the areas they delve into is how to detect accounting manipulation. They found several studies, largely unknown to investors, in which academics developed quantitative models that were able to do a very good job identifying companies that were likely manipulating their financials and/or in danger of going bankrupt. The models they turned up, some of which were several decades old, looked at many different factors, ranging from scaled total accruals to depreciation rates to standard deviations of stock returns.
One of the models was based on a paper by Dr. Messod Beneish of Indiana University's Kelley School of Business, entitled "The Detection of Earnings Manipulation". Using this model, Gray and Carlisle looked back at Enron's financial statements to see what the model would have thought of Enron's likelihood of manipulation. They found that Enron's 2000 score on this model "sent a very strong signal that something fishy was occurring." In 2000, Enron stock was flying high; but not long after -- December of 2001 -- the company filed for bankruptcy and its fraud was exposed. Investors who were aware of and willing to use Beneish's model would have been able to get out of the stock before it came plummeting back down.
Another of the studies that Carlisle and Gray looked at used a quantitative model to classify stocks as being likely or not likely to go bankrupt within the next year. It worked very well, correctly classifying 94% of bankrupt stocks and 97% of non-bankrupt stocks one year prior to filing for bankruptcy -- and it was developed back in 1968, over 30 years before WorldCom went bankrupt. What did the model think of WorldCom? Well, Carlisle and Gray note that two researchers looked at how WorldCom would have scored in the years leading up to its 2002 bankruptcy. The researchers found that "WorldCom's [score] deteriorated precipitously between 1999 and 2001... If investors had not done so in 2000, WorldCom's 2001 [score] was flashing neon warning light to get out of the stock ... At this point WorldCom could be said to be unequivocally in trouble, and probably heading to bankruptcy absent some deus ex machina like a substantial capital raising."
Keep in mind, these models didn't somehow tap into Enron's and WorldCom's true financials; they simply used the "cooked" results that the companies provided to the public. Given that they had been created before WorldCom and Enron went belly up -- and had been proven to be pretty darn accurate -- why didn't they get more press prior to those companies' bankruptcies?
I think the answer involves the same reasons that investors habitually underperform the broader market averages. People are emotional creatures, and we love stories that tug on our emotions. Quantitative models may have cold, hard data, but they don't excite us. Stories about successful companies with brilliant leaders and groundbreaking products and services, on the other hand, send our imaginations running wild. No matter how objective you might try to be, you can't completely turn off those emotions. Once you read that article about "The Next Apple," it's tough to get it out of your head.
Another factor: People like to do things themselves, and they are overconfident in their abilities to do those things. As Philip Tetlock wrote in his book Expert Political Judgment, "Human performance suffers because we are, deep down, deterministic thinkers with an aversion to probabilistic strategies that accept the inevitability of error." We humans want to be right every time. And we think we can be. It's just a matter of learning enough, gaining enough experience, or focusing well enough, we think. Models, on the other hand, come with the near guarantee that they will be wrong a good chunk of the time. For example, history shows that my most successful Guru Strategies tend to be accurate -- that is, make money -- on between 55% and 60% of their picks. If you're going to use these models, you can bet that, over the long term, you'll be wrong 40% to 45% of time.
Humans think we can do a lot better than that, or we think that, unlike models, we can improve our predictive abilities exponentially over time. But the reality is that when it comes to the stock market and all of its unpredictable drivers, we just can't. In fact, in general in terms of predicting political or economic events, 55% to 60% accuracy is far beyond what humans achieve. Tetlock's research detailed a seven-year study he conducted in which supposed experts and non-experts were asked to predict an array of political and economic events. His findings: While the "experts" tended to beat the non-experts, the best human forecasters "were hard-pressed to predict more than 20 percent of the total variability in outcomes" of events. Adjusting for factors like amount of experience, college degree level, or type of expertise made little difference. (One factor that did, however: fame. The more famous, the worse the alleged expert tended to perform!)
Statistical models, meanwhile, aren't perfect at forecasting the future -- but they're a lot better than humans. Tetlock found that sophisticated algorithms could explain 47 percent of outcomes in his study -- more than twice as much as "expert" human forecasters.
James O'Shaughnessy gives some excellent insights into this phenomenon in What Works on Wall Street. In the book, O'Shaughnessy cites additional studies that all found that human prognosticators couldn't match statistical-actuLato forecasting models. In one study, for example, an actuLato model did better in predicting whether certain high school students would be successful in college than did admissions officers at many colleges. In another, a researcher named Jack Sawyer reviewed 45 different studies that compared human and actuLato predictive ability. "In none [of the 45] was the clinical, intuitive method -- the one favored by most people -- found to be superior," O'Shaughnessy writes. "What's more, Sawyer included instances in which the human judges had more information than the model and were given the results of the quantitative models before being asked for a prediction. The actuLato models still beat the human judges!" Several studies have also shown that human forecasters tend to underperform models even when they are given the results of those models' forecasts ahead of time.
How can all of this be? It's because people are emotional creatures, and emotions lead to inconsistency in how we assess problems. Explains O'Shaughnessy: "Models beat human forecasters because they reliably and consistently apply the same criteria time after time. Models never vary. They are always consistent. They are never moody, never fight with their spouse, are never hung over from a night on the town, and never get bored. They don't favor vivid, interesting stories over reams of statistical data. They never take anything personally. They don't have egos. They're not out to prove anything."
The boring consistency of models is why they beat humans -- and why humans avoid models. And their boring consistency highlights a great irony of the stock market: The investors who produce the flashiest returns usually do so in the unsexiest of ways. They don't load up on high-risk, exciting tech stocks, or try new, flavor of the month strategies. They stick to the basics. Whether they use models or not, they analyze a company's balance sheet, competitive position, and valuations. And when the numbers tell them to buy, they buy -- regardless of what their own fickle emotions might be saying. As hedge fund guru George Soros once said, "If investing is entertaining, if you're having fun, you're probably not making any money. Good investing is boring."
Fundamentals and balance sheets and quantitative models may be boring to many investors. But over the long haul, they work -- far better than our own gut instincts. So whatever particular strategy or strategies you use in management managing your portfolio, think "boring". In the market, after all, boring is beautiful.
As we rebalance the Validea Hot List, 6 stocks leave our portfolio. These include: Tal Education Group (Adr) (XRS), Coach Inc (COH), Banco Macro Sa (Adr) (BMA), The Tjx Companies, Inc. (TJX), Tech Data Corp (TECD), Ross Stores, Inc. (ROST), .
4 stocks remain in the portfolio. They are: Agco Corporation (AGCO), Valero Energy Corporation (VLO), Anika Therapeutics, Inc. (ANIK) and Rex American Resources Corp (REX).
We are adding 6 stocks to the portfolio. These include: Williams-sonoma, Inc. (WSM), Bbva Banco Frances S.a. (Adr) (BFR), Usana Health Sciences, Inc. (USNA), Piper Jaffray Companies (PJC), Silicon Motion Technology Corp. (Adr) (SIMO), Liquidity Services, Inc. (LQDT), .
Newcomers to the Validea Hot List
Williams-Sonoma Inc. (WSM): This San Francisco-based cookware and kitchen supply store, which also owns home products and furniture stores like Pottery Barn and West Elm, has nearly 600 stores across the U.S. and Canada.
Sonoma ($6.4 billion market cap) gets strong interest from my Peter Lynch- and James O'Shaughnessy-based models. To read more about it, scroll down to the "Detailed Stock Analysis" section below.
USANA Health Sciences, Inc. (USNA): Utah-based USANA makes nutritional and personal care products such as vitamins, nutrition bars, and skin and hair cleansers. It has customers in the U.S., Canada, Australia, New Zealand, Mexico, the U.K., and a number of countries in Asia. Its subsidiary, BabyCare, Ltd., has a direct selling business in China.
USANA ($900 million market cap) gets strong interest from my Peter Lynch-, Warren Buffett-, and Joel Greenblatt-based models. To read more about it, scroll down to the "Detailed Stock Analysis" section.
BBVA Banco Frances SA (BFR): This Argentina-based bank is engaged in the provision of financial and non-financial services, through a network of over 239 retail branch offices, over 25 branch offices specialized in the middle-market segment, and seven branch offices for corporate and institutional customers. It has a $3 billion market cap.
BBVA gets strong interest from my Peter Lynch-based model and high marks from my Motley Fool- and Martin Zweig-based models. To read more about the stock, scroll down to the "Detailed Stock Analysis" section below.
Liquidity Services, Inc. ( LQDT): Liquidity Services is an auction marketplace for surplus and salvage assets. The company enables buyers and sellers to transact in an automated online auction environment offering over 500 product categories. Its marketplaces provide professional buyers access to a global, organized supply of surplus and salvage assets presented with digital images and other relevant product information. It has a $450 million market cap.
LQDT gets strong interest from my Joel Greenblatt- and Peter Lynch-based models. To read more about it, scroll down to the "Detailed Stock Analysis" section.
Piper Jaffray Companies (PJR): Piper Jaffray is an investment bank and asset management firm, serving corporations, private equity groups, public entities, non-profit entities and institutional investors in the United States and internationally. The company operates in two segments: Capital Markets and Asset Management. It has an $800 million market cap.
Piper gets strong interest from my Peter Lynch-based model and high marks from several other strategies. To read more about it, scroll down to the "Detailed Stock Analysis" section.
Silicon Motion Technology Corporation (SIMO): Silicon Motion is a fabless semiconductor company that makes high-performance, low-power semiconductor solutions for the multimedia consumer electronics market. Its products include mobile storage, mobile communications, multimedia systems-on-a-chip (SoCs) and other products.
Silicon Motion ($800 million market cap) gets strong interest from my Peter Lynch-based model and high marks from several other strategies. To read more about it, scroll down to the "Detailed Stock Analysis" section.
News about Validea Hot List Stocks
Anika Therapeutics (ANIK): Anika shares were hit hard on July 31 after the firm announced earnings that beat expectations but revenues that fell short. Total revenue for the second quarter of 2014 was $26.3 million, compared with $20.8 million in the second quarter of 2013. Operating income was $15.2 million, compared with $9.4 million in the same period in 2013. Net income for the second quarter was $9.3 million, or $0.60 per diluted share, compared with $5.9 million, or $0.40 per diluted share, in the second quarter of last year. Shares fell more than 14%. The Hot List is selling the stock on today's rebalancing, as its fundamentals have been surpassed by those of other companies.
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