Tag Archives: real estate

Ivy Portfolio March Update

The Ivy Portfolio spreadsheet tracks the 10-month moving average signals for two portfolios listed in Mebane Faber’s book The Ivy Portfolio: How to Invest Like the Top Endowments and Avoid Bear Markets . Faber discusses 5, 10, and 20 security portfolios that have trading signals based on long-term moving averages. The Ivy Portfolio spreadsheet on Scott’s Investments tracks both the 5 and 10 ETF Portfolios listed in Faber’s book. When a security is trading below its 10-month simple moving average, the position is listed as “Cash”. When the security is trading above its 10-month simple moving average, the position is listed as “Invested”. The spreadsheet’s signals update once daily (typically in the late evening) using dividend/split adjusted closing price from Yahoo Finance. The 10-month simple moving average is based on the most recent 10 months, including the current month’s most recent daily closing price. Even though the signals update daily, it is not an endorsement to check signals daily or trade based on daily updates. It simply gives the spreadsheet more versatility for users to check at his or her convenience. The page also displays the percentage each ETF within the Ivy 10 and Ivy 5 Portfolio is above or below the current 10-month simple moving average, using both adjusted and unadjusted data. If an ETF has paid a dividend or split within the past 10 months, then when comparing the adjusted/unadjusted data you will see differences in the percent an ETF is above/below the 10-month SMA. This could also potentially impact whether an ETF is above or below its 10-month SMA. Regardless of whether you prefer the adjusted or unadjusted data, it is important to remain consistent in your approach. My preference is to use adjusted data when evaluating signals. The current signals based on February 29th’s adjusted closing prices are below. This month VNQ , TIP and BND are above their moving average and the balance of the ETFs are below their 10-month moving average. The spreadsheet also provides quarterly, half year, and yearly return data courtesy of Finviz . The return data is useful for those interested in overlaying a momentum strategy with the 10-month SMA strategy: Click to enlarge I also provide a “Commission-Free” Ivy Portfolio spreadsheet as an added bonus. This document tracks the 10-month moving averages for four different portfolios designed for TD Ameritrade, Fidelity, Charles Schwab, and Vanguard commission-free ETF offers. Not all ETFs in each portfolio are commission free, as each broker limits the selection of commission-free ETFs and viable ETFs may not exist in each asset class. Other restrictions and limitations may apply depending on each broker. Below are the 10-month moving average signals (using adjusted price data) for the commission-free portfolios: Click to enlarge Click to enlarge

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.