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How To Play A Bounce In Oil. Hint: Not USO

It’s the first (real) week back from holiday break, but the story is the same as it was before Christmas, and before Thanksgiving for that matter…. Crude Oil continues to fall like a lead oil filled balloon, falling below the $50 mark on Monday for the first time since 2009. It’s even gotten to the point of family and friends asking where we think Crude Oil will bottom at parties and dinners, getting our contrarian antennas perked up. The million, or actually Trillion, dollar question is where will Crude finally find a bottom and bounce back? Fortune let us know recently that the $55 drop in Brent Oil prices represents about a Trillion dollars in annual savings. Now, while some are no doubt betting on continued downside with the recent belles of the ball – the inverse oil ETFs and ETNs (the PowerShares DB Crude Oil Double Short ETN (NYSEARCA: DTO ), ProShares UltraShort Bloomberg Crude Oil ETF (NYSEARCA: SCO ) and VelocityShares 3x Inverse Crude Oil ETN (NYSEARCA: DWTI ) ), the last of which is up a smooth 527% since July {past performance is not necessarily indicative of future results}. Others are no doubt positioning for the inevitable rebound in energy prices, thinking it is just a matter of when, not if. Crude Oil is back around $70 to $100 a barrel. And what a trade that would be. Consider a move back to just $75 a barrel, the very low end of where Crude spent the last 5 years, would be a 50% return from the current $50 level. It seems like that could happen nearly overnight without anyone really thinking much about it. So how do you play a bounce in Oil? Well, the most popular play, by size and volume ( $1.2 Billion in Assets , $387 million changing hands daily), is no doubt the The United States Oil ETF, LP (NYSEARCA: USO ) . But is that really the best way to ‘play’ a bounce? Consider that USO Is designed to track the “daily” movement of oil. What’s the matter with that? One would hope that the ETF closely matches the daily move of Oil, right? Well, yes and no. Yes if you are going to buy the ETF for one day, or even a couple of days; no if your investment thesis is oil prices will climb higher over an extended period of time. Because, and here’s where it gets tricky – USO’s long term price appreciation won’t match the sum of its daily price appreciations. What? How is that possible? You see, the ETF works by buying futures contracts on Oil, and there are 12 different contracts in Crude Oil futures each year, you guessed it – one for every month. And while the so called ‘front month contract’ is trading near the number you see on the news every night ($50 yesterday), the further out contracts, such as 10 to 12 months from now, may already reflect the idea that Oil prices will be higher. Indeed, the price for the December 2015 contract is $57, versus $50 for the front month. So there’s $7, or a 14% gain, already “built in” to the futures price. What’s that mean for the ETF investor? Well, if you are correct that Oil will rebound, and it does so, to the tune of rising 14%, or $7 per barrel, over the next 11 months; the ETF likely won’t appreciate 14% as well. It likely won’t move at all, because it will have to sell out of its expiring futures positions and buy new futures positions each month. This means it will essentially have to “pay” that $7 in what’s called “roll costs.” This is why USO has drastically underperformed the “spot price” of Oil over the past five years, with USO having lost -39 % while the spot price of Oil went UP 48 %. It is like an option or insurance premium – a declining asset with all else held equal. Just look at what happened during the last big rally for energy prices between January 2009 and May 2011. That’s a 110% difference between what you thought was going to happen and what the ETF rewarded you with. (click to enlarge) (Disclaimer: Past performance is not necessarily indicative of future results) Chart Courtesy: Barchart So if you think Oil will be higher 3 months from now, or 12 months from now, instead of tomorrow, USO is at best going to give you some discounted version of your expected gain, and at worse a possible loss when Oil gains. (P.S., the ProShares Ultra Bloomberg Crude Oil ETF (NYSEARCA: UCO ) will do all this, times two… yeah) You don’t even have to take our word for it. Take the description of the USO over at ETF.com . USO is a great vehicle for riding short-term moves in expected crude prices, but longer-term holders take on heavy roll risk. Roll costs can be steep when the curve is up-sloping. Ok – so don’t offer a problem without a solution… What other options are out there? Well, in our realm of futures based investments, you could: Trade futures in Crude Oil, Heating Oil, RBOB Gasoline that match your time frame for a rise in prices; rolling them once annually if needed, as we’ve recommended instead of commodity ETFs for some time now. For example, if you think Crude Oil will be higher 12 to 18 months from now, buy the June 2016 Crude Oil futures (currently It will still underperform the pure spot price, but will only pay that roll cost once per year instead of multiple times). Invest with an energy focused professional commodity trading advisor, we have some names. Invest with a trend following manager – who should benefit from a long term trend up (just as they are benefiting from the trend down right now {past performance is not necessarily indicative of future results}, albeit the energy portion would only be a small portion (

Large-Cap Portfolio Management System With S&P 500 Minimum Volatility Stocks

Originally published on Jan. 2, 2015 This R2G model trades in highly liquid large-cap stocks selected from those considered to be minimum volatility stocks of the S&P 500 Index. When adverse stock market conditions exist the model reduces the size of the stock holdings by 60% and buys the -1x leveraged ProShares Short S&P500 ETF (NYSEARCA: SH ). It produced a simulated survivorship bias free average annual return of about 36% from Jan-2000 to end of Dec-2014. Minimum volatility stocks should provide exposure to the stock market with potentially less risk, seeking to benefit from what is known as the low-volatility anomaly . Consequently, they should show reduced losses during declining markets, but should also show lower gains during rising markets. However, our backtests show that better returns than the broader market can be obtained under all market conditions by selecting 8 of the highest ranked stocks of a universe made up from minimum volatility stocks of the S&P 500. Minimum volatility stock universe of the S&P 500 By definition, minimum volatility stocks should exhibit lower drawdowns than the broader market and show reasonable returns over an extended period of time. It was found that a universe of stocks mainly from the Health Care, Consumer Staples and Utilities sectors satisfied those conditions. This minimum volatility universe of the S&P 500 currently holds 119 large-cap stocks (market cap ranging from $4- to $295-billion), and there were 111 stocks in the universe at the inception of the model, on Jan-2-2000. Performance of all the stocks in the universe from 2000 to 2014 The backtest period was 15 years, from January 2000 to December 2014. The backtest simulates holding all stocks of the universe equal weight and rebalancing every week to equal weight. Dividends are included in the stock price data, and are therefore accounted for in the backtest. The maximum drawdown during the backtest period would have been 40.7%, considerably less than the 55.4% for SPY , the SPDR S&P 500 ETF Trust. Annualized return (CAGR) of 13.1% was also considerably better than the 4.3% for SPY. (click to enlarge) (click to enlarge) Performance of all the stocks in the universe during up-market conditions The period March 2009 to December 2014 qualifies as an up-market period. The maximum drawdown would have been 14.9%, less than the 20.1% for SPY. The backtest shows an annualized return of 25.0%, marginally better than the 21.3% for SPY. (click to enlarge) (click to enlarge) The Best8(S&P500 Min-Volatility) model One can see from the above analysis that our S&P 500 minimum volatility stock universe provided better returns than what is expected from minimum volatility ETFs, showing less drawdown during declining markets, but also exhibiting gains during rising markets, similar to, or better than, the broader market. Therefore this universe provides the basis for periodically selecting the highest ranked 8 stocks according to a ranking system. Ranking System To find stocks which may be undervalued, all stocks of the S&P 500 point-in-time minimum volatility stock universe were ranked weekly according to the following parameters: Valuation (measured as market capitalization, debt and cash relative to earnings before interest, taxes, depreciation & amortization, future cash flow and projected earnings), Efficiency (measured as future cash flow relative to total assets), Financial Strength (measured as future cash flow relative to total debt), Short Interest (being the short interest ratio), Trend (measured as the stock price relative to a moving average of the price), with the highest rank obtainable being 100. To test the effectiveness of the ranking system, the universe was divided into 15 “buckets”, each holding about 8 stocks and performance was tested over 15 years. One can see that the “bucket” on the very right with the highest ranks also shows the highest annualized return of about 23%. (Note, there are no buy- and sell-rules in the ranking system.) Trading Rules The model assumes stocks to be bought and sold at the next day’s closing price after a signal is generated. Variable slippage accounting for brokerage fees and transaction slippage were taken into account. (See the Appendix for variable slippage.) Taxes are assumed to be deferred, as for retirement accounts. Buy Rules: Short Interest Ratio < 2.8, and exclude some of the largest market cap stocks from being selected. Sell Rules: Performance In the figures below the red graph represents the model and the blue graph shows the performance of benchmark SPY. Figures 1, 2 and 3 show performance comparisons: Figure 1: Performance 2000-2014 and hedging with SH. The model reduces the size of the stock holdings by 60% to buy SH during down-market conditions. (Note: The inception date of SH was June 19, 2006. Prior to this date values are “synthetic”, derived from the S&P 500.) Annualized Return= 36.3%, Max Drawdown= -24.0%. Figure 2: Performance 2000-2014 without hedging. Annualized Return= 25.6%, Max Drawdown= -51.7%. Figure 3: Performance 2009-2014 without hedging. Annualized Return= 42.6%, Max Drawdown= -17.3%. (click to enlarge) (click to enlarge) (click to enlarge) (click to enlarge) (click to enlarge) (click to enlarge) Figures 4 to 8 show performance details: Figure 4: Performance 2000-2014 versus SPY. Over the 15-year period $100 invested at inception would have grown to $10,368, which is 56-times what the same investment in SPY would have produced. Figure 5: 1-year returns. Except for 2006 the 1-year returns were always higher than for SPY. There was never a negative return over one calendar year. Figure 6: 1-year rolling returns. The minimum 1-year rolling return of the 3-day moving average was -3.1% early in 2009. Figure 7: Distribution of monthly returns. One can see that the monthly returns follow a normal distribution, displaced to the right relative to the returns of SPY. Figure 8: Risk measurements for 15-year and trailing 3-year periods. (click to enlarge) (click to enlarge) (click to enlarge) (click to enlarge) Liquidity To calculate the maximum dollar-value of a portfolio without incurring too much slippage the following formula was provided by P123: ($LiquidityBottom20Pct * #Position * 5%) / WeeklyTurnover% where 5% is the maximum amount traded without affecting the stock price. $LiquidityBottom20Pct = $ 16.8-million #Positions = 8 Annual Turnover% = 430% WeeklyTurnover% = 8.3% Maximum Portfolio Size = ($16.8 * 8 * 5%) / 8.3% = $80-million Thus, this model could accommodate a good number of individual investors. Variable Slippage The model assumes that stocks are bought/sold at the next day’s closing price after the signal is generated. Since one may not be able to obtain the closing price, a slippage factor is applied to account for a possible higher/lower price for the transactions. The slippage percentage is calculated for every transaction based on this algorithm: 1) The 10 day average of the daily traded $-amount is calculated (price*volume). 2) The slippage is set according to where the average falls in this table: 0 – $50K 5.00% $50K – $100K 1.50% $100K – $350K 0.75% $350K – $1M 0.50% $1M – $5M 0.25% $5M+ 0.10% 3) Add (1/ Price)% to the result from Step 2. For example, Step 3 would add 1% to the slippage if the stock trades at $1, 0.1% if it trades at $10, etc. For the following transaction Date Symbol Type Shares Trading Volume on day Price excluding slippage 5/28/2013 XXX BUY 64,108 3,338,776 50.27 the slippage percentage would be (0.10 + 1/50.27) = 0.120% of $50.27, amounting to about 6 cents per share. So the average price per share paid for this transaction is $50.33.

Risks And Avoidable Mistakes For 2015

Originally published on Jan. 4, 2015 Introduction For most dollar oriented investors 2014 was an “okay” year with a third year in a row of double digit gains for the S&P 500, but not for the bulk of institutional accounts. Consciously or not, many investors and managers were aware of the length of the present bull market having entered its 61st month. This has created twin dilemmas for the prudent management of responsible money. First dilemma – Large Cap over-ownership As regular readers of these posts recognize and true to my analytical history, I tend to view investments through the lens of mutual funds. When simplifying the fund performance data for 2014 by size of market capitalizations the following is revealed: Large Cap funds 11% Multi-cap funds (Unrestricted/ or “go anywhere” funds 9% Mid Cap funds 8% Small Cap funds 3% In a dynamic economy the rank order of operating earnings power generation would be in the opposite order, being led by Small Caps or possibly the successful “Go Anywhere” funds. Focusing on operational earnings, excluding foreign exchange benefits, I believe that the Large Caps were producing approximately 3 times the long-term growth of the Small Caps. The better market performance of the Large Caps, I believe, was a function of market structure changes. Some institutional investors being concerned with the duration of this bull market moved heavily into Large Cap stocks directly or more importantly through the use of ETFs invested in the S&P 500 and other indices. Because of perceived greater liquidity in Large Caps they were hiding out in what we used to call warehouses. With governments all over the world looking to Large Caps being “social progress” engines, I have some doubts as to the growth prospects for Large Cap companies. Second dilemma – Historical constraints As is often the case, apparent boundaries come with both hard data and locked-in thought processes. The data is the easy part. While as noted we are in the sixty-first month of the recovery, of the nine last market recoveries, four have been over 100 days in length with the longest being 181 days. Thus for a manager a possible career risk is exiting too soon which puts a premium on investing in liquid positions. Because so many others have made similar judgments as to the better liquidity in Large Caps, if there is a sudden drop in the market, I believe the excessive amount invested in Large Caps will find their exit liquidity either expensive or non-existent for those that are late. The biggest risk for investors and their managers are the biases that many of us labor with in making so-called rational decisions. The following are a list of these biases as listed by Essential Analytics. List of biases Outcome, herding, conviction (the curse of knowledge), recency, framing, band wagon effect, information, anchoring, optimism. I suggest that many of these biases find their way into reports; supporting in effect, the reasons we all have made decisions that haven’t worked out. The key for all of us is to understand our biases. Some biases we will be able to overcome. Others we will have to accept as immutable. This suggests that when putting together a portfolio of funds or managers, it would be wise to try to diversify the various biases of the hired portfolio managers as well as our own as the owners or fiduciaries of the capital being deployed. Overcoming biases I have a definite advantage in this task by personality. By nature I am both curious of what I don’t know and often a contrarian. As a contrarian again using the mutual fund microscope, the following may be useful thoughts: Looking to extremes one might wish to set up a pair trade of being long some of the components in the S&P Latin American energy index which declined -39% vs. the average Indian fund which was up 41% in 2014. In a similar fashion one might start to research funds in the following groups that declined in 2014: Energy Commodity funds -34% General Commodity funds -16% Global Natural Resources funds -15% Domestic Natural Resources funds -15% Dedicated Short-bias funds -15% I take some comfort in the contrarian thoughts contained in the headline to John Authers insightful Financial Times column: “The case for gently shifting money away from US.” I believe a well-reasoned portfolio should be looking for opportunities on a global basis both in terms of what companies do and where various securities are traded. Final thought Many year-end predictions are essentially extrapolations of existing market trends and this could be what will happen. However, I am searching for the beginnings of new trends that will produce +20% or -20% in a twelve month period. I would appreciate hearing your thoughts as to when and which direction (or both) you expect price movement. I firmly believe we will once again experience this kind of action.