Tag Archives: economy

5 Consumer Discretionary Funds To Buy On A Spending Splurge

Consumer spending levels increased at the fastest pace in eight months this January. Not only consumers bought big-ticket items like cars and houses, but they also ramped up purchases of a range of goods that include other discretionary products. Rise in wages, decline in the jobless rate, cheap gasoline price and winter thaw helped spending levels to move north. Given the loosening of purse strings, investing in funds from the consumer discretionary sector may prove to be profitable as a major part of consumer expenditures go to this sector. What is more encouraging, income levels rose in January for the 10th straight month. This shows that consumers are in a position to spend more in the coming months. Consumer Expenditure Climbs in January The Commerce Department on Feb. 26 reported that personal spending increased 0.5% in January. The rise exceeded the consensus estimate of a rise by 0.3%. Moreover, a price index of consumer spending level went up in January. The personal consumption expenditures (PCE) index in the 12 months through January advanced 1.3%, the highest increase since Oct. 20 14. The core-PCE index that excludes food and energy prices also rose 1.7% in the 12 months through January, the largest increase since July 20 14. Telltale Signs of Consumption Pickup Retail sales are off to a good start this year, indicating strength in consumer spending. The Commerce Department said on Friday that sales at retail stores rose 0.2% in January. Consumers mostly bought big-ticket items while online store sales also moved north. The so-called core retail sales figure that excludes automobiles, gasoline, building materials and food services also increased 0.6% in January following a decline of 0.3% in December. Car sales too spiked in January. At a seasonally-adjusted annualized rate (“SAAR”), car sales increased to 17.55 million units in Jan. 20 16 from 17.32 million units in Dec. 20 15, the highest SAAR for any January since 2006. Moreover, existing home sales for January hit the highest level since last July. Existing home sales increased 0.4% in January to a seasonally-adjusted annual pace of 5.47 million. Rise in Wages, Low Fuel Price Boost Spending Higher wages and steady hiring helped consumers to step up their purchases in January. Average hourly wage growth increased to 2.5% in January compared with year-ago levels. Wages grew in January at the best pace in about six years. Wage growth picked up momentum after remaining almost flat for several years following the recession. Moreover, the U.S. unemployment rate was 4.9% in January, the lowest since Feb. 2008. Many analysts believe that it is close to “full employment”. Cheap gasoline is also powering Americans’ ability to lift spending levels. Until recently, gasoline prices had hit a 12-year low across the Midwest. The Federal government had lowered its national average gasoline price forecast for this year by 5 cents to $ 1.98 a gallon. Separately, a return to normal winter temperatures also boosted spending. 5 Consumer Discretionary Funds to Buy Banking on these encouraging trends witnessed in January, it is expected that consumer spending levels will improve further in the coming months. Additionally, households’ purchase on a range of goods not only increased in January, but also their incomes rose too. According to the Commerce Department, personal income gained 0.5%, more than the consensus estimate of a 0.4% increase. More income generally translates into more expenditure. Moreover, U.S. consumer sentiment has already started showing signs of recovery in February. The Thomson Reuters/University of Michigan’s final consumer sentiment reading for this month came in at 9 1.7, which was higher than the preliminary reading of 90.7. Given the healthy pattern of consumer spending, it will be wise to invest in funds linked to the consumer discretionary or cyclical sector. More money in consumers’ pocket will eventually increase spending on discretionary items. The consumer discretionary sector includes companies that sell nonessential goods and services. This sector includes companies involved in retail, automobiles, media, consumer services, consumer durables and leisure products. Here we have selected five such consumer discretionary or cyclical funds that boast a Zacks Mutual Fund Rank # 1 (Strong Buy) or #2 (Buy), have positive three-year and five-year annualized returns, have minimum initial investment within $5,000 and carry a low expense ratio. Fidelity Select Consumer Discretionary Portfolio No Load (MUTF: FSCPX ) seeks growth of capital. This fund invests a large portion of its assets in securities of companies involved in the manufacture and distribution of consumer discretionary products and services. FSCPX’s three-year and five-year annualized returns are 14.3% and 12.9%, respectively. Annual expense ratio of 0.79% is lower than the category average of 1.4 1%. FSCPX has a Zacks Mutual Fund Rank # 1. Putnam Global Consumer Fund A (MUTF: PGCOX ) seeks growth of capital. This non-diversified fund not only invests a major portion of its assets in companies in the consumer staples space, but also invests in discretionary products and services industries. PGCOX’s three-year and five-year annualized returns are 10.6% and 9.3%, respectively. Annual expense ratio of 1.26% is lower than the category average of 1.43%. This fund has a Zacks Mutual Fund Rank # 1. Fidelity Select Retailing Portfolio No Load (MUTF: FSRPX ) invests the major portion of its assets in securities of firms involved in merchandising finished goods and services to consumers. FSRPX’s three-year and five-year annualized returns are 20. 1% and 18.4%, respectively. Annual expense ratio of 0.8 1% is lower than the category average of 1.4 1%. This fund has a Zacks Mutual Fund Rank #2. Fidelity Select Automotive Portfolio No Load (MUTF: FSAVX ) seeks capital appreciation. The fund invests a large portion of its assets in companies involved in the manufacture or sale of automobiles, trucks, specialty vehicles, parts, tires and related services. FSAVX’s three-year and five-year annualized returns are 7.9% and 2.7%, respectively. Annual expense ratio of 0.85% is lower than the category average of 1.4 1%. This fund has a Zacks Mutual Fund Rank #2. Fidelity Select Multimedia Portfolio No Load (MUTF: FBMPX ) seeks capital appreciation. The fund invests a major portion of its assets in companies engaged in the production, sale and distribution of goods or services used in the broadcast and media industries. FBMPX’s three-year and five-year annualized returns are 1 1.2% and 12.4%, respectively. Annual expense ratio of 0.8 1% is lower than the category average of 1.4 1%. This fund has a Zacks Mutual Fund Rank #2. Original post

Irrational Pessimism – Throwing The Baby Out With The Bath Water

Since first publishing kortsessions.com in February 2013, I have tried not to get into the weeds on individual stocks ideas. We are all about the media and the adverse outcomes in store for those who rest their investment policy on their pronouncements. When I did mention a name for illustrative purpose, I did my best to make certain that I acknowledged my fallibility and ownership position (if I had one), and to advise all readers consuming my work to make certain companies mentioned were suitable to their own investment circumstance and tolerance for risk before they considered purchase… caveat emptor. Today’s post is going to sound like a recommendation. But because of the irrational pessimism surrounding a certain segment of the market, the securities covered have great illustrative value. Nonetheless, I urge you refer to the admonition in paragraph one before rushing out and buying any of the two names that I will refer to. I own positions in both names. Background of the craziness My example today comes from a former client, Tortoise Capital Advisors . As their name implies, “Slow and steady wins the race.” They are not trying to hit the ball out of the park every time they come to the plate. Singles do just fine. Tortoise manages both separate accounts, closed-end, exchange-traded funds and open-ended funds (AUM $13 billion), the majority of which specialize in the shares of Master Limited Partnerships (MLPs). I have owned their shares in the past, and recently initiated positions in their flagship fund, the Tortoise Energy Infrastructure Corporation (NYSE: TYG ), and the Tortoise MLP Fund (NYSE: NTG ). These two funds, in particular, are midstream (processing, storage and pipelines – not production ) oriented. They are conduits and not terribly sensitive to commodity prices. The management at Tortoise is very conservative. I can vouch for this from personal experience. As an institutional broker with both A.G. Edwards and Wells Fargo, I had the opportunity to bring managements in for meetings with their PMs and analysts. I will attest that they were an extremely tough sell. They have scrupulously avoided commodity risk and the risk of anything questionable in financing plans/needs and capitol structures (excessive leverage). They looked for simple businesses with long-term repeatable revenue streams. They did their homework. Both TYG ($23.58, yielding 11%, a/o-2/26) and NTG ($15.20, yield 11%, a/o- 2/26), after making all-time highs in 2013 ($50.64 and $30.18 respectively), began precipitous declines in 2014. Interestingly, TYG, because it had the word “Energy” in its name, began to plummet first; even though none of its MLP investments owned oil and gas reserves or production. Its holdings were all fee-based conduits, storage or processors, whose prices had collapsed due to oversupply issues in commodities that they transported, but whose demand (ergo, fee-generating capacity) continued to grow. This was crazy, but par for the course for the stock market. Linn Energy LLC (NASDAQ: LINE ) and Kinder Morgan, Inc. (NYSE: KMI ) exacerbate matters In the case of Linn, it is an upstream (ergo, highly exposed to commodity risk via owned oil and gas production) MLP that came under bear attack for its hedge accounting (completely unwarranted). The company made a large acquisition, with the idea that it could swap out pieces for lower-risk producing assets and sell equity to finance the rest. It did this on the credit card. The crude market turned. Linn Energy could not sell or swap assets. When oil collapsed, its stock price collapsed. The company could not sell equity to pay down debt. Linn’s stock, which at one time traded as high as $42, is now less than $.50 per share. Importantly, Linn and the upstream partnerships are outliers. Though midstream MLPs, for the most part, have little commodity exposure, investors did not want to be confused with the facts and sold. KMI was another case of a bear attack on what was considered at one time “best of breed” in the midstream MLP space. It was also a situation where an acquisition was made in a market that was not sympathetic to financing MLPs. Ergo, to put itself back on sound financial footing (which it did – see here ), the company slashed its dividend 75%, proving the naysayers correct and causing further group-wide liquidation… throwing the babies out with the bathwater. The Elephant in the Room: Is the MLP model broken? According to Tortoise portfolio manager, Matt Sallee …Looking at the facts, midstream MLPs, their fundamentals are not broken. Our portfolio has average cash flow growth of 20% year over year looking at EBITDA, 10% per unit. And while not every company has announced their 4th quarter distributions, north of half of our portfolio has, and that weighted average distribution as I mentioned previously is up about 3% over the prior quarter, so we feel pretty good about that. Along with that, our MLP portfolio companies, have not experienced any distribution cuts. You read that? Over half their portfolio companies in the last year increased distributions with no distribution cuts! Source: Transcript of Tortoise first quarter 2016 conference call (Additional context: Video presentation by Tortoise CEO, Kevin Birzer) How irrational has the pessimism been in the MLP space? My favorite recent example came on January 20, 2016. In the wake of a horrific (pardon my sarcasm) 1/4 point increase in the Fed Funds rate, a continuing collapse in the price of oil, the Chinese market in free fall, a potential European banking crisis (punctuated by rumors of problems at Deutsche Bank AG (NYSE: DB )), the market opened and fell almost immediately by 550 Dow points. During the panic selling that ensued, TYG hit a low of $18.50 (yielding 14%) and NTG fell to $11.60 (yielding 14.5%). Don’t confuse us with the facts! We can’t stand this anymore! Get us out! The above panic is a descriptive of what one normally sees at a market bottom, not at a top … an example of – “… nameless, unreasoning, unjustified terror … (- Franklin D. Roosevelt ).” It is Irrational Pessimism of the highest order. I believe that the MLP space is a good proxy for much of the craziness afoot in today’s market… healthy babies being tossed out with the bath water. What is your take? Disclaimer: The information presented in kortsessions.com represents my own opinions and does not contain recommendations for any particular investment or securities. I may, from time to time, mention certain securities for illustrative purpose, names where I personally hold positions. These are not meant to be construed as recommendations to BUY or SELL. All investments and strategies should be undertaken only after careful consideration of suitability based on the risks, tolerance for risk and personal financial situation.

Tactical Asset Allocation For The Real World

Managing risk via tactical asset allocation (TAA) offers a number of encouraging paths for limiting the hefty drawdowns that take a toll on buy-and-hold strategies. But what looks good on paper can get ugly in the real world. There’s a relatively easy fix, of course: consider the total number of trades associated with a strategy as another dimension of risk. The dirty little secret is that many TAA backtests don’t survive the smell test after considering the impact of trading frictions – particularly for taxable accounts. Deciding where to draw the line for separating the practical from the ridiculous varies, based on the usual lineup of factors – an investor’s risk tolerance, time horizon, tax bracket, etc. But there’s an obvious place to start the analysis. Let’s kick the tires for some perspective using some toy examples. An obvious way to begin is by using the widely cited TAA model outlined by Meb Faber in what’s become a staple in the literature for this corner of finance – “A Quantitative Approach to Tactical Asset Allocation.” The original 2007 paper studied the results of applying a simple system of moving averages across asset classes. The impressive results are generated by a model that compares the current end-of-month price to a 10-month average. If the end-of-month price is above the 10-month average, buy or continue to hold the asset. Otherwise, sell or hold cash for the asset’s share of the portfolio. The result? A remarkably strong return for the Faber TAA model over decades, in both absolute and risk-adjusted terms, vs. buying and holding the same mix of assets. The question is whether running the Faber model as presented would be practical after deducting trading costs and any taxable consequences? Let’s ask the same question for two other simple strategies: Percentile strategy: apply the rules in Faber but limit the buy/hold signal so that it only applies when the asset price is above the 70th percentile for the ratio of the price above the trailing 10-month average. The same logic applies in reverse for the sell signal: the asset price is below the 30th percentile for the ratio of price below the 10-month moving average. For signals between that 30th-70th percentile range, the previous signal remains in force. Relative-strength strategy: apply the Faber rules but limit the buys to assets in the top half of the performance results for the target securities, based on the trailing 10-month results. The same rule applies in reverse for triggering a sell signal. In other words, sell only assets in the bottom half of the performance results via the trailing 10-month period if a sell signal applies . Note that for all strategies, the signals are lagged by one month to avoid look-ahead bias. To test the strategies, we’ll use the following portfolio (see table below), which consists of 11 funds representing a global mix of assets, spanning US and foreign stocks, bonds, REITs and commodities. In essence, this is a global twist on the standard 60%/40% US stock/bond mix. The initial investment date is the close of 2004 with results running through this month as of Feb. 26. All the models start with the same allocation. The chart below compares the results for the three strategies and a buy-and-hold portfolio. The Faber model delivers the best results. A $1 investment in the strategy at 2004’s close was worth roughly $1.48 as of last Friday. The Relative Strength model was in second place at $1.39, followed by the Percentile Strategy ($1.34) and Buy and Hold ($1.20). Raw performance data tells us that the Faber model is the winner. Note, too, that all three TAA models deliver superior results in risk-adjusted terms. For instance, historical drawdown for the three strategies is relatively light compared with the Buy and Hold model. In particular, the Buy and Hold portfolio suffers a hefty drawdown in excess of 40% in 2008-2009 whereas the three TAA models never venture below a roughly 10% drawdown. Given what we know so far, it appears that the Faber model is the superior strategy via a mix of strong performance and limited drawdown risk. But the results look quite a bit different once we add in the dimension of total trades associated with each strategy. Buy and Hold, of course, excels on this front. But the lack of trades (or trading costs) is more than offset by the steep drawdown for Buy and Hold. The question, then, is what is the superior TAA model if we consider real-world costs? The numbers provide the answer via a summary of total trades for each strategy, as shown in the table below. The Percentile model’s trades number just 93 for the 2004-2016 test period – less than half the trades for other two strategies. The Percentile model’s total return trails the Faber results, but only modestly so. In short, the Percentile model generates 90% of the Faber model’s returns, with a comparable level of superior drawdown risk compared with Buy and Hold. Add in the Percentile’s substantially lower turnover clinches the deal, or so one could argue. If this was an actual consulting project, we would run additional tests before making a final decision. For instance, we might consider other models and look at longer historical periods, perhaps using daily prices and compare results with a variety of risk metrics. Running Monte Carlo simulations to effectively test the models thousands of times would be useful too. Looking at the results in terms of the number of trades associated with each strategy is no less valuable. This subtle but crucial aspect of backtesting tends to be ignored. But if you’re comparing TAA models for use in the real world, it’s essential to adjust for real-world trading frictions. In some cases, adding this extra layer of analysis may end up as a determining factor for separating failure from success.