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How To Find The Next Great Growth Stock

Now is the time to be paying close attention to the market to find the next group of market leading stocks. Traits that define market leading stocks never change, all past winners have the same traits in common. CAN SLIM Investing and why you need to familiarize yourself with this strategy to discover winning growth stocks. Its a great time to be refreshing your watch list and be closely monitoring the market as volatility tends to bring opportunities. An investor always wants to be in tune with the market no matter how discouraging things may seem. The stocks that hold up the best during market corrections and volatility are the stocks that should be bought when the market gets its footing back. These are the stocks that institutions are refusing to dump even in a difficult environment. These stocks typically all have something in common, they have the “it” factor. The characteristics of great growth stocks never change. The “it” factor is a combination of multiple things. They display exceptional sales and earnings growth, have a new product or service, typically have just gone public within the last eight years or sooner, and are in high demand by institutional investors and come from strong industry groups. These characteristics are the basis of CAN SLIM investing. CAN SLIM is a growth stock investment strategy introduced by William O’neil founder of Investors Business Daily newspaper. O’neil analyzed the top performing stocks dating back to the 1880’s and identified 7 characteristics they all shared. C = Current quarterly earnings and sales growth should be up at least 25% or more for the last two quarters. Accelerating earnings and sales growth for three quarters in a row and you could have a potential big winning stock. A = Annual earnings growth of at least 25% for each of the past 3-5 years. Also look for return on equity (ROE) of at least 17% N = Look for companies with new products, new services, new conditions in their industry, new management, and new price highs. S = Supply and Demand – look for big volume moves in the stock during upside trading. L = Leader – Look for the top stocks both fundamentally and technically, in the best performing sectors and industry groups. I = Institutional sponsorship – Watch what pension funds, mutual funds, banks and other institutions are buying. M = Market Direction – Three out of four stocks follow the market trend, therefore only buy growth stocks when the market is in an uptrend. This time tested and proven strategy is a blend of fundamental and technical analysis. If you can identify companies that fit the “CAN” in the acronym you have taken your first step to discovering the importance of the fundamental side of the equation. Take Palo Alto networks(NYSE: PANW ) for example in May of 2014. The company had just reported its second quarter results showing a 57% increase in Earnings per share and a 49% increase in sales. This fits the “C” part of the equation. The company showed a 84% annual increase in in EPS numbers in 2014 fitting the “A” part of the equation perfectly. At the time of the second quarter earning release the company had posted two quarters of significant earnings growth foreshadowing the strength of projected earnings for the year. Throw in the fact that the company was in a red hot cyber security sector with a new technology to help companies ward of cyber attacks and you then satisfy the “N” part of the equation. You now have the making of a big potential winning stock with strong fundamentals in a red hot industry which is acting like a market leader. The “SLIM” part of the equation covers the technical aspects of growth investing as well as the health of the market. The “S” in the strategy is a whole set of technical analysis skills that must be learned and developed to be able to identify supply and demand. The market repeats symmetrical patterns. Through the use of technical analysis and pattern recognition experience these patterns can be identified for good potential risk reward set ups. Lets go back to Palo Alto Networks( PANW ) in May of 2014. The stock was coming out of a proper technical basing pattern and offered investors a good risk reward entry after it reported earnings. The stock closed at a price of $73.17 on May 29th 2014. The stock was a market leader at the time satisfying the “L” part of the equation, showed strong interest from Institutions and mutual funds covering the “I” part of the equation, and the stock market was in an uptrend satisfying the “M” part of the equation. Palo Alto Networks( PANW ) currently trades at the time this article was written around $180 per share. Well you may be asking yourself is it really that easy to bag a big winner? With the market pulling back twice in the second half of 2014 it wasn’t easy to hold on to growth stocks. The risks of the strategy are that growth stocks typically correct two and a half times the market averages and they can be difficult to hold. The strategy also suggest taking profits at 15% to 20% and using a stop loss of 7% to 8% from your purchase price so bagging a 150% gain requires strong conviction, discipline and patience. There are advantages and drawback within any methodology but this is certainly one that can get you in the best performing stocks when the market is good and protect you from a correction when things turn ugly. My own personal use of this strategy has protected my clients from the market crash of 2008 with a +2% return and also had me outperform the market in 2013 with a +59% return. It has most recently gotten me in such big winners as Gopro(NASDAQ: GPRO ) in 2014, Shake Shack(NYSE: SHAK ) earlier this year and big winners such as Facebook(NASDAQ: FB ) in 2013 before they all made their major price advance. In summary this is a recipe to identify huge potential winning stocks and when to buy them, and can also protect your portfolio from major damage from extreme bear markets. It is a vital tool for any growth investor to have in his or her toolbox. The next big winner is currently out their and is waiting for you to identify it! Get to know CAN SLIM! Current stocks that act well technically and fit the CAN SLIM criteria at the time this article was written: Fitbit(NYSE: FIT ),Amazon.com(NASDAQ: AMZN ), Facebook( FB ), Tableau Software(NYSE: DATA ) to name a few. These stocks merely fit the criteria and as always please do your own diligence and consult your financial professional to help you make decisions on buying or selling stocks that are suitable for your own personal risk profile. To get more information on the strategy please visit the education tab on my website at skoufiscapital.com . Investors Business daily offers great resources and learning tools at Investors.com with actionable ideas. Investors.com also offers educational courses for any level of investor. For the advanced investor who is familiar with technical analysis, visit marketsmith.com for great charting software as wells as screening tools to help identify potential leading growth stocks. Marketsmith also offers a tool to identify technical patterns and offers multiple pre built stock data bases to help you identify stocks that fit the CAN SLIM criteria. Disclosure: I/we have no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours. (More…) I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article.

An ETF Leveraged Pairs Strategy That ‘Works’ (But Would Still Be A Terrible Investment)

Summary In general, shorting pairs of leveraged ETFs does not generate favourable returns. An exception to this is shorting the volatility future TVIX, XIV pair, with this giving seemingly excellent returns. But this strategy is not advised, with the investor effectively selling financial catastrophe insurance. The theory The core equation describing the expected return of a leveraged ETF is as follows: Here ‘underlying return’ is simply the return on the asset the ETF leverages, anything from SPY, industry-specific equity funds, VIX futures and various commodities. λ specifies the ETF’s leverage; typically this is -1 (i.e. inverse), 2 or sometimes 3. Finally, σ is the standard deviation of the underlying return. The equation can be split into two, showing the key drivers of the return: The return on the underlying, leveraged λ times: The decay from volatility: It is the second – volatility decay – term that generates much of the criticism of leveraged ETFs. It reduces the return unless the ETF is unlevered i.e. λ = 1 or has no volatility i.e. σ = 0. Its adverse impact increases with leverage and the volatility of the underlying. The practical reason for volatility decay is the ETF’s daily rebalancing: a, say, 10% fall in the underlying, followed by a 10% rise will leave the underlying unchanged but see a leveraged ETF lose money. A leveraged ETF’s return is, however, not necessarily negative. It depends on the balance between the underlying’s return and the volatility decay factor. For example, SPY – representing the S&P 500 – has a (conservative) expected return of, say, 6% p.a. and a standard deviation of, say, 20% p.a. Plugging these numbers into the above formula gives a long-run expected return of ~8% for a 2x leveraged SPY ETF. This is well below the naïve 2 x 6% = 12% expectation, but is an improvement on the unlevered 6%. It is nevertheless at the cost of more than proportionally increased volatility. The theory applied to shorting leveraged ETF pairs Moving on to this article’s main subject, shorting pairs of leveraged ETFs. Applying the equation to shorting a pair of ETFs with leverage λ and -λ: The next step needs some algebra. Take the above equation and expand out (using Taylor’s series), neglecting any term higher than order 2 (these terms will be small in comparison). Then with λ = 2: Examining this equation shows the return will be positive for realistic pairs of leveraged ETFs: an asset’s return standard deviation (σ) will be bigger than its expected return (U). By shorting a pair of ETFs with opposite leverage and the same underlying, the return of the underlying cancels out and does not impact the strategy’s result. The strategy instead collects the (on average) losses generated by the interaction of the asset’s volatility and the daily rebalancing. In practice: Shorting UPRO and SPXU These two ETFs are designed to give 3x and -3x the compounded daily return on the S&P 500. Shorting $100k of both gives the following return chart: …equating to a stable before cost return of ~2% p.a. Unfortunately, the after cost return is ~-5%! These costs are principally the cost of borrowing the shares to short. I have UPRO costing ~5% p.a. to borrow and SPXU ~3.5% p.a. Notwithstanding the theory above, the market is efficient and has reached such by increasing borrow costs to unusually high levels. Similar results occur for all – bar one – pairs of leveraged ETFs that I have examined. In practice: Shorting TVIX and XIV The exception are a couple of volatility ETFs, TVIX and XIV. TVIX is designed to return 2x the VIX futures short term index. XIV is designed to return -1x the same index. Because the fund’s leverages are not equal and opposite, this strategy involves shorting $2 of XIV for every $1 of TVIX. It results in the following return chart, for a $100k notional investment: After costs, it yields a return of ~10% p.a. with a Sharpe ratio of ~ 2 (compared to the S&P 500’s ~ 0.5). It is also possible to leverage this strategy further; as shown it starts at -$33k TVIX and -$67k XIV, but (if you have portfolio margin) your broker may allow multiples of these amounts. The strategy works because of the exceptionally high volatility of the underlying VIX futures, together with the ETF’s relatively large tracking errors. The large drawdown in early 2012 was caused by a short squeeze on TVIX. Its price rose well above its net asset value. The short squeeze occurred because the issuing bank reached its internal risk limits in respect of VIX futures. It hence stopped creating new TVIX units, removing the normal mechanism for keeping the ETF’s price near its net asset value. Holders of this strategy may well have had their TVIX shares called at the worst possible time – the minimum of the black curve – missing out on the subsequent recovery. The key problem with this strategy is, however, its tail risk. Gains from shorting a stock are limited to 100% of its value. Losses are unlimited. A large enough single-day increase in the value of VIX could see the strategy lose more than 100% of the notional investment. In particular, if a day sees the VIX short term futures index double or more, XIV – if it functions as designed – will go to zero. But TVIX can continue to rise, generating unhedged, potentially unlimited losses for the strategy. I suspect this is the main reason that the market allows this apparent inefficiency. Executing the strategy is equivalent to selling financial catastrophe insurance. Additional disclosure: I am sometimes long / short XIV, but do not execute this strategy.