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Strategy Ideas: How To Trade Earnings Forecast Upgrades

Summary Buying stocks with recently upgraded earnings is a well-documented and profitable strategy. How does trading costs impact the return of this strategy? What are 2 other factors that can enhance return of stocks with positive earnings revisions? The American Association of Individual Investors is an interesting resource for investors who want to see historical performance for a wide-range of value, growth, momentum and guru-type strategies. One basic strategy that has been of great interest to me for many years is buying after a positive earnings forecast revision . Since 2000, the strategy (as reported by AAII) returned 25.9% annually with an annual performance of -18.1% in the year 2008. Over the past 15+ years, only 2 years ended with a negative return. It would seem like a simple slam-dunk. But before you start hitting the ‘market order’ button – you may want to read about a hidden danger in addition to a couple of enhancements to this strategy. What Is the Earnings Revision Strategy? The concept is very basic. Analysts typically estimate future earnings. The various estimates are averaged and called a consensus. When the analyst consensus rises by 5% or more, you buy and hold for the following month (or some other length of time). Why Might This Work? The thought is that investors, while aware of the earnings revision, are hesitant to fully price this new information in. The price often drifts upwards for 30 days or more after the initial spike that follows the upgrade. Who Might Like This Strategy? This would be a strategy for an active trader. The turnover is bound to be high. Thus, the trader needs to have skill in purchasing (selling) stocks without unduly driving the price up (down). Does This Simple Concept Really Work? The short answer is, yes – but not as high or as consistent as one would hope. We will look at why in a moment. Before we do so, I wanted to run my own independent test of this phenomenon. Gross Returns of Earnings Revision Strategy Utilizing Portfolio123’s back-testing engine I ran a simple test. I stipulated that earnings must be positive and that the current estimate must be 5% greater than it was 4 weeks ago. There are no other rules. This screen is run across all stocks trading on all USA exchanges which have at least some fundamentals. (click to enlarge) The returns are not as high as AAII’s screen but significant outperformance is present on a massive pool of stocks. The Hidden Cost of Trading If you have ever tried to re-create some of the higher performing screens, you may have learned that simple stock screens often have massive hidden costs. Many of the stock recommendations in this screen come from illiquid tickers with huge bid/ask spreads . You may not be able to buy and sell hundreds of thousands of dollars per stock without moving shares 5 – 10% against the trade. So to get around this, you can raise the minimum criteria which will lower trading costs. What would be the impact if you boosted the minimum requirements to the criteria below? Minimum share price $5 Minimum average daily turnover value of $500,000 Minimum market capitalization of $50 million Increasing liquidity lowers trading costs but also gross performance. (click to enlarge) And once you factor in even a very reasonable amount of slippage per trade, your returns will struggle to remain positive. A good idea also needs a well-executed plan. Going to the open market and buying up stocks with a minimum of 5% earnings revisions without any further consideration can destroy capital fast due to high trading costs but also lower than expected alpha if you trade super-liquid tickers. Yet, we do not throw out the earnings revision strategy because of this. We need to consider other factors that play nice with earnings revisions. What other criteria should we be on the lookout for? Size Matters When it comes to revisions in earnings estimates, stock size matters. To prove the point, look at the performance difference (annualized returns since 1999) between the smallest and largest stocks in the Russell 3000 universe. What does this chart mean? The only stocks represented in the chart are Russell 3000 stocks with a 5% or greater earnings revision. Next, I ranked stocks weekly according to market capitalization. The red bar on the left represents the S&P 500 annual return since 1999. The next bar represents the annual return of stocks that ranked in the bottom one-fifth of the universe according to market cap. The bar farthest right represents the largest capitalization stocks which also had a 5% earnings revision. Therefore, if you are timing a short-term trade based on a revision of earnings consensus, you might be less inclined towards buying larger firms. Some big names that revised current fiscal year earnings upwards over the past few months are Amazon (NASDAQ: AMZN ), AT&T (NYSE: T ), Gilead Sciences (NASDAQ: GILD ) and Ford (NYSE: F ). It would seem that any additional value added by such a revision gets priced in quickly in these highly traded stocks. Therefore, trying to jump on a trade after the revision may not produce the desired results. There appears to be a greater likelihood of an upwards drift in smaller names such as Aceto Corp (NASDAQ: ACET ), Concert Pharmaceuticals (NASDAQ: CNCE ) and Exar Corp (NYSE: EXAR ) which have very small market capitalizations. Value Matters Another important concept relating to upgraded earnings is value. If the share price is very low compared to the earnings, perhaps at a multiple of 10x, and then earnings are forecast upwards by a significant amount – one would expect the price to rise accordingly. But if the price was already trading at a very high PE ratio, perhaps 50 or more, and the earnings forecast was bumped up giving the stock a projected PE ratio of 47 – the upwards price drift may be slight to non-existent. Determining if the projected PE is high or low becomes increasingly important when trading big stocks with earnings upgrades. Over the past 10 years, S&P 500 stocks which had a projected PE ratio higher than average (in this case the average of the R3K index), had an annual return of 6% if you rebalanced weekly. S&P 500 stocks with revised earnings which had lower than average projected PE ratios showed an annualized return of almost 15%. If you traded only Russell 2000 stocks (small-cap), then the’ lower than average’ forward PE ratio stocks would have returned 33% annually over the past 10 years vs. 20% if the projected PE ratio was higher than average. Trading Earnings Revisions Of course, you still have the burden of lowering trading costs to keep as much of this potential return as you can. Here are a few tips I would encourage: Try to keep slippage around 0.25 – 0.35%. You can achieve this by lowering your trading size or moving to slightly more liquid stocks. Also remember that 1 cent represents 1% in a stock trading at $1.00. You may also want to buy higher priced stocks so that you do not lose as much on the bid/ask spread. A rule of thumb is to trade no more than 5% of the daily volume, 10% if you have trading skill. Scan the market for upgrades every week and buy as soon as possible. While you can hold for the full 4 weeks, consider selling early if prices move up very fast. When prices go parabolic, you often do well to lock in profit. If you are holding at the 4 week mark and prices have continued to drift up gradually, analyze if there is reason to hold longer – perhaps an additional 4 weeks. Some stocks continue to benefit from the positive revision longer than the first few weeks. Do you trade earnings revisions? What has been your experience? I would like to hear how you trade this strategy.

The Real Cost Of Hedging With Leveraged ETFs

Summary Scaled hedging has some advantages over usual market-timing. Leveraged ETFs are convenient hedging tools, but they suffer from a decay. This article calculates the additional cost of hedging a stock portfolio with leveraged ETFs. A timed, scalable hedging tactic has at least 3 advantages over usual market-timing consisting in going out of the market: adaptability to the risk level, lower transaction costs, and cashing all dividends. This previous article shows how to use a systemic risk indicator to scale a hedging position and protect my premium portfolio with SPXU . ETFs are not necessarily the best hedging tools, but they are available and understandable for all investors. SPXU has the advantage to allow hedging in an account where only long positions in stocks and ETFs are possible, and without margin. Like all leveraged ETFs, it has the drawback of suffering from a decay called beta-slippage. This article calculates the real additional hedging cost incurred by this decay in 2015 for SPXU, and for another leveraged inverse S&P 500 ETF: SDS . It also shows the decay of long leveraged ETFs. What is beta-slippage? If a volatile asset goes up 25% one day and down 20% the day after, a perfect double leveraged ETF goes up 50% the first day and down 40% the second day. On the close of the second day, the underlying asset is back to its initial price. At the same time, the perfect leveraged ETF has lost 10%: (1 + 0.5) x (1 – 0.4) = 0.9 This decay is called beta-slippage. It is a mathematical property of a leveraged and frequently rebalanced portfolio (leveraged ETFs may hold futures, options and/or swap contracts). In a trending market, beta-slippage can be positive. If an asset goes up 10% two days in a row, on the second day, the asset has gone up 21%. The perfect 2x leveraged ETF is up 44%: (1 + 0.2) x (1 + 0.2) = 1.44 It is 2% better than holding the underlying leveraged 2x on margin. Beta-slippage is path-dependent. If the underlying gains 50% on day 1 and loses 33.33% on day 2, it is back to its initial value, exactly like in the first example. This time, the perfect leveraged ETF loses one third of its value, which is much worse than the 10% of the first case: (1 + 1) x (1 – 0.6667) = 0.6667 Without a formal demonstration, it shows that the higher the volatility, the higher the decay. Hence the name of beta-slippage: “beta” is the best known statistical parameter of volatility. Of course, it is uncommon to have such price variations on an ETF’s underlying asset. These numbers are here to give an amplified vision of what happens with more realistic daily returns, day after day and month after month. (click to enlarge) SPXU in red, SPY in blue. Chart and data: portfolio123 Decay of S&P 500 ETFs in 2015 The next table gives the decay of leveraged ETFs on the S&P 500 index from 1/1/2015 to 10/15/2015 (9.5 months). It was a sideways and quite volatile market, with a worse than usual beta-slippage. The decay includes beta-slippage, and also tracking errors and management fees. Ticker Return Return of SPY x leveraging factor Decay (difference) Drag on portfolio SPY -0.52% SH (1xshort) -1.84% 0.52% -2.36% -1.18% SSO (2xlong) -3.92% -1.04% -2.88% -0.96% UPRO (3xlong) -8.69% -1.56% -7.13% -1.78% SDS(2xshort) -4.84% 1.04% -5.88% -1.96% SPXU(2xshort) -9.45% 1.56% -11.01% -2.75% When using SDS or SPXU for hedging, the hedging position represents 1/3 of the total portfolio (stocks + hedge) in the first case, and 1/4 in the second one. So the real drag on the portfolio was respectively 1.96% and 2.75% compared with shorting SPY. This is the additional cost of hedging the whole portfolio during the whole period (setting it in market neutral mode), which is not the best tactic proposed in my previous article (and service ). The cost of using leveraged ETFs with any of the proposed variable hedging tactics was much lower. Rebalancing the hedge weekly also lowers the decay due to beta-slippage (but not tracking errors). Finally, the cost is lower than losing all dividends when going out of the market in a classic market-timing approach, and it is likely to provide a better long-term risk-adjusted performance. SSO and UPRO also look like decent alternatives: SSO had the lowest portfolio drag. But short selling always incurs additional risks and borrowing costs. SDS and SPXU allow to hedge without borrowing cost and with less or no margin cost. These costs depend on the broker, so the best choice for hedging with an ETF may depend on your broker. If you have the skills and possibility to manage other instruments like futures, options, CFDs, they may be more cost-effective. Keep also in mind that the hedge and the stock portfolio can be in different accounts. If you like this article, you might be interested in the next ones. Click the “Follow” tab at the top if you want to stay informed of my free-access publications on Seeking Alpha. You can even choose the “real-time” option if you want to be instantly notified.

Multi-Factor Investing

Multi-factor investing that combines value, momentum, quality (profitability), or low volatility factors is today’s hot new investment approach. There has been an explosion of multi-factor ETFs recently with nine of the fourteen existing U.S. multi-factor funds coming to market this year, and five of them showing up within the past 60 days. Multi-factor funds may be a good thing, since single factor funds can have some serious drawbacks. However, multi-factor funds can also have their own quirks and issues. If the large variety of factors is thought of as the “factor zoo,” then multi-factor approaches may be the “factor circus” with its own collection of silly clowns, dangerous acrobats, and amusing jugglers. Factor Investing Issues With factor investing in general there are three potential problem areas: tractability, scalability, and volatility. With respect to tractability, it is well-known that value investing can have long periods of serious under performance. This happened in the late 1990s and also somewhat during the past two years. Not all value investors may be willing to watch this happen without losing patience and giving up on their factor portfolios. To a lesser degree, momentum and other factors are also subject to sustained tracking error. Scalability has to do with too much money chasing after too few stocks. Factors perform best when you can focus on those stocks having the strongest factor characteristics. For example, Van Oord (2015) showed that from 1926 through 2014, only the top decile of U.S. momentum stocks outperformed the market. Stocks below the top decile added nothing to strategy results. Yet just two out of the twelve large-cap U.S. equities single factor ETFs only include stocks that are within the top decile of their factor rankings. For example, the oldest and largest single factor value ETFs are iShares S&P 500 Value (NYSEARCA: IVE ), iShares Russell 1000 Value (NYSEARCA: IWD ), and Vanguard Value (NYSEARCA: VTV ). They hold 72%, 69%, and 50% respectively of the stocks that are in their investable universes. This makes them, to a great extent, closet broad index funds with higher fees. Their large sizes ($8.3 billion, $23.5 billion, and $34.6 billion, respectively) may impede them from focusing on just fifty (the top decile of S&P 500 stocks) or one-hundred (the top decile of Russell 1000) value stocks. The same is true with respect to momentum. The largest momentum fund, with over $1 billion in assets, is the AQR Large Cap Momentum Style mutual fund with an expense ratio of 0.45. It holds 532 out of an investable universe of 1000 stocks. This is a far cry from the top decile of momentum stocks. Large amounts of investment capital may make it difficult for single factor funds of all types to focus exclusively on the relatively small number of stocks that appear in their top factor deciles. The third problem for single factor portfolios is increased volatility and high bear market drawdowns that accompany value, momentum, and small cap factors. Trend following filters, such as absolute momentum, can help reduce downside exposure with respect to long-term bear markets, but it does little to alleviate uncomfortable short-term volatility. Trend following is also less effective when applied to value factors than when applied to other factors like momentum. Multi-Factor Solutions All three of these problem areas for single factor investing – tractability, scalability, and volatility – can be significantly reduced by using intelligently constructed multi-factor portfolios. Multiple factors can obviously reduce tracking error, since it is unlikely that several factors will substantially under perform at the same time. As for scalability, if a fund uses four factors instead of just one, it can handle four times the investment capital without eroding its ability to enter and exit the markets. Finally, the volatility and large bear market drawdown associated with value and momentum factors can be reduced by combining these factors with less volatile ones, such as quality and low volatility. However, I intentionally included the words “intelligently constructed” when I referred to the potential benefits of multi-factor portfolios. It surprises me that six out of the fourteen U.S. multi-factor funds include small size as a factor. Sponsors of these funds must have been asleep during the past 25 years when abundant academic research showed that small cap stocks, while giving higher returns, add nothing positive on a risk-adjusted basis because of their high volatility. When combined with value or with value and momentum, which is what all six funds of these funds do, small cap can be particularly undesirable, since it can aggravate already high portfolio volatility and bear market drawdown exposure. It is also surprising that the “premier anomaly,” price momentum, is included in only eight of the fourteen U.S. multi-factor funds. Abundant research has shown that momentum is the most powerful factor for generating positive returns. More sleepy time among fund sponsors? The final issue associated with multi-factor funds is their average annual expense ratio of 42 basis points for what are enhanced index funds. This is higher than the Morningstar US ETF Large Blend Strategic Beta expense ratio of 38 basis points and the Morningstar US ETF Large Blend Index expense ratio of 36 basis points. Until just recently, an investor who wanted multi-factor exposure would have been better off creating it herself by combining the single factor iShares MSCI USA Value Factor, USA Momentum Factor, USA Quality Factor, and USA Minimum Volatility ETFs, since these all have expense ratios of only 15 basis points. New Solution This situation changed dramatically last month when Goldman Sachs entered the ETF business with an offering called Goldman Sachs Active Beta U.S. Large Cap Equity (NYSEARCA: GSLC ). GSLC is the only multi-factor fund having what I consider an optimal mix of factors: value, momentum, quality, and low volatility. Here is a description of how they determine these factors: • Value: The value measurement is a composite of three valuation measures, which consist of book value-to-price, sales-to-price and free cash flow-to-price (earnings-to-price ratios are used for financial stocks or where free cash flow data are not available). • Momentum: The momentum measurement is based on beta- and volatility-adjusted daily returns over an 11-month period ending one month prior to the rebalance date. • Quality: The quality measurement is gross profit divided by total assets or return on equity (ROE) for financial stocks or when gross profit is not available. • Low Volatility: The volatility measurement is defined as the inverse of the standard deviation of past 12-month daily total stock returns. Even though the fund holds 432 stocks out of an investable universe of 500, it uses a weighting scheme (most multi-factor funds with a large number of holdings do the same) that allocates substantially more of its capital to stocks with high factor ratings. GSLC rebalances positions quarterly and uses a turnover minimization technique (especially useful for momentum stocks) of buffer zones to reduce the number of portfolio transactions. I use a similar buffer zone technique myself with some of my more active momentum models. What is especially appealing about GSLC is its low cost structure. The fund came into existence because some of Goldman’s largest clients wanted to invest this way using an ETF wrapper to minimize their tax consequences. Because of this sponsorship, the fund was set up with an annual expense ratio of only 9 basis points. This is the same expense ratio as the biggest and most popular ETF in the world, the SPDR S&P 500 ETF Trust (NYSEARCA: SPY ). GSLC already has $78 million invested in it since coming to market one month ago. GSLC is not an ideal investment from our point of view, since it doesn’t have a trend following filter like absolute momentum to help it avoid severe bear market drawdown. GSLC is also unable to benefit from international diversification during those times when international stocks show greater relative strength than U.S. stocks. However, because of its low cost structure, GSLC might be a good asset to consider along with the S&P 500. If GSLC continues to attract considerable assets so that it has good liquidity and if it performs well relative to the S&P 500 over the next year, I may add GSLC to my dual momentum models. Multi Factor Funds Symbol Factors Assets Stocks Exp Ratio 4 Factor Goldman Sachs Active Beta U.S. Large Cap GSLC Value, Mom, Quality, LoVolty $78 m 432 0.09 ETFS Diversified Factor U.S. Large Cap SBUS Value, Mom, Size, LowVolty $17 m 492 0.40 iShares Factor Select MSCI USA LRGF Value, Mom, Size, LowVolty $5 m 135 0.35 3 Factor SPDR MSCI USA Quality Mix QUS Quality, Value, LowVolty $6 m 624 0.15 JP Morgan Diversified Return U.S. Equity JPUS Value, Mom, Quality $11 m 561 0.29 John Hancock Multifactor Large Cap JHML Size, Value, Profit $79 m 772 0.35 AQR Large Cap Multi-Style (non-ETF) QCELX Value, Mom, Profit $1.2 b 338 0.45 iShares Enhanced U.S. Large Cap IELG Value, Quality, Size $71 m 109 0.18 PowerShares Dynamic Large Cap Value PWV Value, Quality, Mom $927 m 50 0.58 FlexShares U.S. Quality Large Cap Index QLC Quality, Value, Mom $3 m 120 0.32 Gerstein Fisher Multi-Factor Growth Equity (non-ETF) GFMGX Size, Value, Mom $227 m 298 1.03 2 Factor ValueShares Quantitative Value QVAL Value, Quality $47 m 41 0.79 FlexShares Morningstar U.S. Market Factor Tilt TILT Value, Size $740 m 2249 0.27 Cambria Value and Momentum VAMO Value, Mom $3 m 100 0.59 Nothing contained herein should be interpreted as personalized investment advice. Under no circumstances does this information represent a recommendation to buy, sell or hold any security. Users should be aware that all investments carry risk and may lose value. Users of these sites are urged to consult their own independent financial advisors with respect to any investment.