Tag Archives: income

When Graham Found Momentum

Originally published on March 9, 2016 The enterprising investor may confine his choice to industries and companies about which he holds an optimistic view, but we counsel strongly against paying a high price for a stock (in relation to earnings and assets) because of such enthusiasm. – Benjamin Graham Popularity is fleeting. For some, it only lasts the proverbial “15 minutes”. For others, it drags on longer than any rational person can comprehend. We see this in people and things. It explains why fads come and go. It eventually ends because there are not enough new eyeballs to replace the ones that lose interest, or something newer and shinier comes along to draw our attention away. Ben Graham noticed this popularity effect on stocks and briefly covered it in The Intelligent Investor : Here we found – contrary to our investment philosophy – that companies that combined major size with a large good-will component in their market price did very well as a whole in the 2 1/2 year holding period. (By “good-will component” we mean the part of the price that exceeds the book value.) Graham separated stocks into several groups based on a single factor to see how each performed over a 30-month period between December 1968 and August 1971. Each group held a basket of 30 stocks. The group in question held the largest stocks with the highest price-to-book value. Our list of “good-will giants” was made up of 30 issues, each of which had a good-will component of over a billion dollars, representing more than half of its market price. The total market value of these good-will items at the end of 1968 was more than $120 billions! Basically, these were the “most expensive” large caps trading over 2x book value. I think it’s a safe guess that the price-to-book was much higher. Yet, despite the “expensive” price tag, the group outperformed all other tests and beat the market by about 15% for the period. Here’s Graham’s explanation for why: A fact like this must not be ignored in work on investment policies. It is clear that, at the least, a considerable momentum is attached to those companies that combine the virtues of great size, an excellent past record of earnings, the public’s expectation of continued earnings growth in the future, and strong market action over many past years. Even if the price may appear excessive by our quantitative standards the underlying market momentum may well carry such issues along more or less indefinitely. It is difficult to judge to what extent the superior market action shown is due to “true” or objective investment merits and to what extent to long-established popularity. No doubt both factors are important here. I think Graham’s explanation does a good job of describing potential drivers behind the momentum factor. Put simply, people like to buy stocks that are going up and avoid whatever’s going down. And if they feel good about stocks, they’re willing to pay more. He goes so far as to say it’s something investors might consider. It’s just not something he’d consider, since it goes against everything he believes. In most situations, value investors are doing the opposite. They’re buying when stocks are most unpopular and selling as popularity takes over. The popularity effect becomes the selling opportunity for value investors and buying opportunity for anyone willing to exploit the momentum factor. Note: Graham doesn’t list the 30 stocks in the “goodwill giants” group. Based on the few mentioned in the book – IBM Corp. (NYSE: IBM ), Xerox, Polaroid – and the time frame, I’m guessing several fit the Nifty-Fifty mold. For those who don’t know, The Nifty-Fifty were a select group of high-growth stocks that reached a “buy at any price” popularity (much like the dot-com stocks of the late ’90s, except the Nifty-Fifty had actual earnings). The shiny thing was high earnings growth that initially got people’s attention, popularity and momentum drove prices higher. In some cases, investors were paying P/E multiples of 50x and higher. For a while, anyway, it worked… until it didn’t. What seemed like a great idea in ’68 became a terrible one in ’73 when the market crashed.

High Dividend Sector ETFs Hitting All-Time Highs

A few days back, the market was abuzz with faster-than-expected rate hike bets in the U.S. on hawkish tip-offs from some Fed officials after a dovish meeting in mid March. However, recently Fed chair Yellen put all hearsay to rest by emphasizing global growth issues. Also, the Fed chair indicated a ‘cautious’ stance that may be adopted by the U.S. central bank on the policy tightening issue going forward. Following the dovish statements, stocks and bonds soared. Investors should also note that yields on U.S. treasuries dropped following Yellen’s remarks. Below we discuss a few ETFs that popped after Yellen’s speech and could remain in focus ahead. In a falling yield environment, investors rushed to tap every possible option that can cater to their income need. Along with broader dividend funds, high yielding sector ETFs have also been witnessing strong pricing performance lately. Below we highlight a few winning sectors and their ETFs that hit all-time highs reflecting Yellen’s comments. Utilities Utilities usually have strong yields and are embraced by investors when Treasury bond yields fall. Also, the utility sector is considered a safer option when volatility levels spike. This sector is less volatile in nature and relatively immune to the market peaks and troughs. Moreover, the space is less exposed to currency translation due to lack of foreign coverage (read: Protect Your Portfolio with These Utility ETFs ). By virtue of their stronger yields and defensive nature, the following utilities ETFs touched all-time highs lately. The Utilities Alphadex First Trust (NYSEARCA: FXU ) hit an all-time high on March 31 2016. The fund added over 2.7% in the last five trading days (as of March 31, 2016) and yields about 2.85% annually and FXU has a Zacks ETF Rank #3 (Hold) with a Medium risk outlook (read: Smart Beta ETFs That Stood Out Amid Market Volatility ). The Guggenheim S&P Equal Weight Utilities ETF (NYSEARCA: RYU ) hit an all-time high on March 31, 2016. The fund returned 2.6% in the last five trading days (as of March 31, 2016) and yields 3.20% annually and follows an index which is the equal weighted version of the S&P 500 Utilities Index. Real Estate Real Estate is also a highly interest rate sensitive sector. These firms are usually highly leveraged and face maximum interest rate risk in the REIT world. Now, REITs are required to distribute 90% of their annual taxable income through dividends which make them high dividend yield vehicles. With interest rates expected to remain subdued for longer and bond yields trending down, the Real Estate Select Sector SPDR ETF (NYSEARCA: XLRE ) hit an all-time high, adding about 2.2%, on March 29, 2016. The fund yields 2.06% annually (as of the same date). The fund was up over 3.7% in the last five trading days (as of March 31, 2016). Telecom Telecom, another defensive sector by nature, also offers investors solid dividend yields. The Fidelity MSCI Telecommunications Services Index ETF (NYSEARCA: FCOM ) hit an all-time high on March 31, 2016. The product advanced about 4.4% in the last five trading days (as of March 31, 2016). Consumer Staples Consumer staple stocks have been performing better in recent months as investors are slowly moving toward defensive sectors. Also, consumer staples stocks and ETFs are high yield in nature which put this sector in focus following Yellen’s speech. Also, many consumer staples stocks are rich in global presence and are likely to outperform amid falling dollar. The Fidelity MSCI Consumer Staples Index ETF (FTSA) touched an all-time high on March 31, 2016. The fund added about 1.5% in the last five trading days (as of March 31, 2016). The fund yields about 2.52% annually (as of March 31, 2016). Link to the original post on Zacks.com

The Riddle About Differing Fund Flows And Assets Under Management

By Detlef Glow Click to enlarge Looking at market statistics from different providers such as data vendors, associations, central banks, and others, one realizes that none of the providers state the same number for a fund’s flows or assets under management for a specific date. Even though this may sound a bit odd, it is normal and the nature of the beast. Since all data vendors, associations, and others have a different basis for the data they provide, flow numbers will be different from one provider to another. Data vendors calculate flows based on the funds in their database, while associations use the data on flows and assets under management they receive from their members. These data may include mandates and special-purpose vehicles such as pension funds, which are not mutual funds at all. In contrast, central banks use the holdings data from their associated banks to evaluate the holdings of mutual funds. Statistics calculated for the same topic and for the same market can vary widely, since the underlying data can be totally different. Good examples of the differences in databases for a market segment are the several reports available on the European exchange-traded fund (ETFs) market. While the Thomson Reuters Lipper report focuses only on ETFs, i.e., products that are funds and regulated as such, other reports focus on the whole market of exchange-traded products (ETPs), which means those reports also include structured notes such as exchange-traded commodities (ETCs). Another factor that always leads to differences in numbers is the currency in which the report is calculated, since some providers use euros, while others use the U.S. dollar for the denomination of fund flows and assets under management. But even if two providers of fund flows reports use the same data to calculate the flows for a given region, they may end up with totally different results. The employed methodology for the calculation of the flows might be different and would by definition lead to different results. In addition, all results are dependent on correct and timely data input from the fund promoters, since any inaccurate numbers in the database impact the quality of the statistics. Even though a data vendor might have quality checks in place for the incoming data, it may not find all the corrupted data. Even though quality checks do help to get the numbers right, some data may be missing and have to be estimated with an algorithm. This also explains why flows and assets under management data change over time, since it takes a while for all the fund promoters to deliver correct data. All in all, it can be said that the most recent fund flows and assets under management statistics published shortly after the end of month should be seen more as a guide to evaluate market trends than as a scientific result. Anybody who uses these kinds of statistics should make a decision about which statistics suit their needs best and then stay with those statistics. This does not mean that one should not question whether the displayed data are right, but one should realize that there always will be differences in flows data for any given month. The views expressed are the views of the author, not necessarily those of Thomson Reuters Lipper.