Tag Archives: seeking-alpha

A Strategy To Defend Your Portfolio From Bear Markets

It is important to protect one’s portfolio from crashes like 2007-2009 where the major market indices lost more than 50%. Historically, markets have seen long 4-10 years runs of steady Bull market interspersed with shorter 1-2 year Bear markets. Most losses in a bear market come within a short span of few months. An investor playing good defense will look to time an early exit in a crash. When market is sufficiently oversold, short term bounce backs present further opportunity to make gains. An investor who remained invested during stock market crash from October 2007 to February 2009 lost more than 50% of his investment (based on SPY performance ) during that period. Similarly, between September 2000 and September 2002, fully invested investors lost ~40% ( using SPY as a benchmark ). Both bear markets wiped out 3-5 years of preceding year gains. While timing the market is a hard proposition, it is incredibly important to preserve your portfolio from a major whitewash during a crash. “Defense wins Championships” is a famous saying in football but is more aptly relevant for investors that can successfully maneuver through a bear market. NFL teams with good defense minimize points scored against them by opposition; a good portfolio needs strong defensive strategies to protect from bear market onslaught. Further just like strong defense can actually add to score by triggering turnovers, bear market presents opportunities for sizeable gains which if not exploited means missed opportunity cost. For example, investors who were too risk averse and did not participate in the post-crash rally of 2009-2011, lost out on capital appreciation opportunity of 80-90% within that 2-year period. To build a good defensive strategy, an investor needs to understand the market dynamics. The picture below best illustrates the US stock market history of bull-bear markets ( Source: Business Insider ). (click to enlarge) Key takeaways we can derive from the above picture are: The large part of this graph is dominated by long running bull markets, with most runs lasting many years or even more than a decade. During this multi-year period, the market sees steady returns with small intermittent corrections interspersed. Some examples include the bull market in 1990s, 1980s, 1950s and 1940s, all of them lasted 10+ years. Bear markets are relatively short in terms of overall duration (1-2 years), and the losses come at a much faster rate (compared to gains in bull market). For example, 2008 crash lasted 1.3 years and 2002 crash lasted 2.1 years. The longest bear market was in the 1930s and lasted close to 3 years. “Market goes up in an escalator but down in an elevator” is a famous stock market quote that can summarize the overall dynamics. Understanding the wisdom behind these select few words is important for all investors. The picture below shows 1 example of Bull-Bear cycle in SPY adjusted close graph during the 2003-2008 period ( Source: Yahoo Finance data ). Notice the steady increase in SPY for 4+ years (escalator) followed by a dramatic 1-year crash in 2008, wiping out a large part of multi-year gains. Hence the saying, market goes like an escalator and comes down like an elevator. (click to enlarge) Here is another graph that shows SPY monthly returns ( Source: Yahoo Finance data ) during the 2008 crash period. Notice even during the bear market, the bulk of losses (~-46%) came over a short 9-month period from June 2008 to February 2009. Hence the analogy of elevator coming down vertically or fast. (click to enlarge) The above historical perspective presents multiple takeaways that should influence our investing strategy. Given the long runs of Bull market, sitting out of stock market for extended period of time has significant opportunity cost of not participating in Bull rally. If one wants to protect their portfolio in the event of a crash, they need to get out of market early in a crash. However, getting out too early has risks too as it may only be a temporary dip i.e. no crash, market recovers and one has to get back in at a higher price. So timing the market exit is a balancing act between these two scenarios. Exiting out late in a Bear market can double the pain as one will take the losses but not participate in the rally that should be soon to follow. Buy and hold investors who finally give up on stocks after seeing their portfolios trounced for a year or two, have the risk of exiting out at close to bottom of crash. Building a Defensive Strategy: The above takeaways can be formulated to build a variation of Simple Moving Average (SMA) based strategy. For our example, we will use SPY as a representative market index that we play the strategy on. However, the strategy should be verifiable on most indices with varied performance. The SMA gives an overall trend of market direction that is not easily seen with day-to-day variations. So a simple strategy could be to stay long in SPY when SPY is above its say 50-day SMA and sell all holdings when SPY falls below its 50-day SMA. When SPY index is above the SMA, it is pulling the SMA upwards i.e. leading to a positive trend in index. One big drawback of SMA-based strategies is the whipsaw effect. This happens when stock dips below the SMA, we sell the index but then stocks recover, goes above SMA and we get back. Because we are selling at a lower point and then buying back again at a higher price, this leads to a loss. If this happens with large enough frequency, the strategy can lead to sizeable losses and NEGATIVE returns as compared to Buy and Hold. Since history is dominated by large bull runs interspersed with shorter bear runs, it is probably wiser to side on being long for the most part. So we assume that more often than not the market is expected to bounce back after a dip below SMA leading to whipsaw. To reduce the number of times we go out of market and whipsaw, we can use a longer duration SMA. The longer the duration, the less likely the chance of temporary short-term dips breaching SMA and giving a false sell signal. Let’s take 250-day SMA which is equivalent to 1 year in terms of trading days. Further even when SPY touches or breaches the 250-day SMA that is a major support level indicating a high chance of bounce back. So I would propose the sell SPY signal to be even lower, say when SPY has breached more than 2% below 250-day SMA. So let’s assume that we sell SPY when it’s hit more than 2% below 250-day SMA. On top of this, let’s try to take advantage of the fact that once market is sufficiently down, volatility increases and we expect to see several bounce backs from the lows. The bounce back can be temporary though as we don’t know for sure when the actual bottom is or if the bear market is close to end. To take advantage of this short-term bounce backs, we can define a lower point at SMA for market to be oversold. In this zone, we could look to do some bottom fishing by trying to do the reverse, i.e. buy SPY when SPY is below its short-term SMA, say 4-day SMA and sell it as soon as it recovers. So our strategy becomes as follows: Stay long in SPY as long as SPY is greater than -2% (say X) of its 250-day average. Sell and go in cash if it falls below X. If SPY falls below 6% (say Y) of 250-day SMA look to bottom fish. Buy SPY when it is below its 4-day SMA expecting a short term bounce back and sell as soon as it comes back above its 4-day average. These are short-term trades that take advantage of market’s volatility. Now while the thresholds pick (X and Y) may feel like magic numbers, in my test almost all combinations of X and Y where X

Bring More Data

Several months ago we posted an article called ” Bring Data ” where we showed the importance of having abundant data for system development and validation. This was further reinforced to us recently when someone actually brought us additional U.S. stock sector data. Previously, we only had Morningstar sector data that went back to 1992, which we used to construct our Dual Momentum Sector Rotation (DMSR) model. (S&P sector data also goes back to only the early 1990s.) DMSR was shown in my book as one example of other ways you might use dual momentum. When we were given equivalent Thompson Reuters U.S. stock sector data back to 1973, we immediately extended our DMSR back test to include this additional data. After incorporating the new data, DMSR still looked considerably more attractive than buying and holding the S&P 500 index. But one could argue that the performance of models using broad-based equity indexes, such as Global Equities Momentum (GEM), now looks better than DMSR. Here are the comparative performance figures from January 1974 through October 2105: GEM DMSR S&P 500 Average Annual Return 17.36 15.86 12.21 Standard Deviation 12.32 14.55 15.43 Sharpe Ratio 0.89 0.67 0.42 Maximum Drawdown -17.84 -33.96 -50.95 Results are hypothetical, are NOT an indicator of future results, and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Please see our website’s Performance and Disclaimer pages for more information. Because the monthly correlation between GEM and DMSR is only 0.59, sector rotation can still have a useful but modest role to play in a diversified equities-oriented portfolio. But DMSR is not the best choice as a core portfolio holding. Sector rotation programs that use data no further back than the early 1990s to develop their models may be in for a rude awakening someday if future drawdowns are higher and returns are lower than they expect based on back testing with a limited amount of data. Along the same lines, there are also momentum-based portfolios popping up on the internet all the time now, some even labeled as “dual momentum,” that are modeled on the basis of only 10 or 15 years of ETF data. Momentum may be robust enough that future results won’t suffer much because of this. But those who think they are constructing optimal models this way are just fooling themselves. Overfitting modest amounts of data is one of the most pernicious problems in the development of investment models. Those who do this may argue that the markets change over time, so the best model parameters from years ago may not be as relevant as today’s best parameters. This may be true. However, what is also true is that today’s parameter values are also likely to be sub-optimal when moving forward in time. The following chart from my book, Dual Momentum Investing , shows what I mean: Chart courtesy of Tony Cooper The S&P 500 is highlighted in different colors for each 15 year period. You can see that the latest period, 1999-2013, looks different from the preceding period, 1984-1998. 1999-2013, in fact, looks more like the earlier 1969-1983 period. 1984-1998 is also different from its preceding period, 1969-1983 and similar to the earlier years 1954-1968. If you had used each 15-year period to develop your model, you would have had something unsuited for each of the next 15-year periods. You would likely be better off using all four periods to formulate a model rather than just the last 15-year period. The more data you use, the more likely you are to have a robust model that will hold up reasonably well in the future, even though it isn’t the best fit to any one particular period. The 12-month look back parameter we use for our GEM and ESGM dual momentum models was found to work well in 1937 by Cowles & Jones . It has been used extensively in momentum research since then and has held up well out-of-sample. But there is a lot more history than that to help give us more confidence in momentum. Let’s take a look at some of that now. We focus on stocks as our core asset since they have historically offered the highest risk premium to investors. U.S. stocks, in particular, have given investors the best long-run returns. Other assets can create a drag on long-run portfolio performance. They also lose some importance as diversifiers once you use a trend following overlay like absolute momentum to help attenuate your downside risk exposure. The longest back test on stock market momentum is by Geczy and Samonov (G&S). Their 2013 paper called ” 212 Years of Price Momentum: The World’s Longest Back Test 1801-2012 ” compared the top one-third to the bottom one-third of U.S. stocks sorted monthly by relative momentum. Over this entire sample period, the top equally weighted momentum stocks outperformed the bottom ones by 0.4% per month with a highly significant t-stat of 5.7. Prior to this study, momentum outperformance on U.S. stocks had been found significant back to 1926. G&S showed that stock momentum was also positive and statistically significant from 1801 to 1926. G&S also found that stock market momentum was remarkably consistent. In only 2 of the 21 decades from 1801 through 2012 did long-only momentum under perform buy-and- hold, and these were by just -1.2% and -0.7% annually. In all the other 19 decades, momentum outperformed buy-and-hold by an average of 3.8% annually. This year G&S came out with a new study called, ” 215 Years of Global Multi-Asset Momentum: 1800-2014: Equities, Sectors, Currencies, Bonds, Commodities, and Stocks .” Here G&S expanded their momentum study to cover six different asset classes, including bonds, stock sectors, and equity indices, which are the ones we use in our momentum models. [1] G&S demonstrated the outperformance of momentum inside and across all asset classes except commodities. Here is a chart from their paper showing the log cumulative equally weighted average of the 6 asset classes plus the cross asset momentum excess returns. The strongest momentum effect is in country equity indices, which had a long-only monthly excess return over buy-and-hold of 0.52% with a highly significant t-stat of 11.7, compared to 0.29% with a t-stat of 6.4 for individual U.S. stocks and 0.24% with a t-stat of 15.5 for all assets. G&S also show that long-only absolute (time series) momentum outperformed buy-and-hold by 0.15% per month with a t-stat of 11.2. For those who want to further their momentum education, I suggest you first read the seminal paper by Jegadeesh and Titman (1993) that started the modern momentum renaissance. Next, learn about absolute momentum from Moskowitz et al (2012) or Antonacci (2013). Then follow up with Geczy and Samonov (2015) to satisfy yourself as to the efficacy and robustness of momentum investing based on 215 years of empirical evidence. [1] Equity indexes are equally as good as individual stocks (or better, according to G&S) in capturing the momentum effect. Indexes are much easier to use and avoid the enormously high transaction costs associated with rebalancing momentum-based stock portfolios.

High Dividend ETFs Should Be A Cornerstone Of Your Portfolio

Summary Dividend income is a great way to increase cash flow while not having to liquidate positions and taking money out of the market. Historically, dividend-paying companies have outperformed non-dividend-paying companies thus this strategy may boost overall portfolio return. Companies that make up dividend-paying ETFs can vary from traditional broad market ETFs, so splitting your portfolio up can help diversify your holdings. For a majority of investors, regular “stock picking” is not of interest to them. The shear amount of work and patience involved in the process tends to push the masses towards passive management, where the debate between mutual funds and ETFs begins. Without a doubt, there has been a lot of movement into ETFs due to their lower-fee structure as well as their overall net-of-fees performance compared to mutual funds. Over the past few months, I’ve begun to do a deeper dive on the value of high dividend yield ETFs to see if they are truly worth the hype. Many of the most successful investors preach the importance of investing in companies that pay steady dividends. It’s easy to understand the appeal of such companies; the ability to return cash to shareholders shows that the company is, usually, being managed well and investors can generally expect a stream of cash to supplement any unrealized gains in their shares. I’m of the personal opinion that, to some degree, companies pay dividends to keep shareholders from selling their shares. In other words, giving investors a bit of cash to pad their pockets may deviate them from selling their position when they are in need of cash – something that tends to occur increasingly during economic downturns. Of course, these dividends come at the cost of less capital appreciation, but many investors like the little bonus they see in their investment account. If you are someone who believes you should just spend the interest, not the principal, then high-dividend ETFs should peak your interest. In addition, corporations have been distributing record amounts of dividends back to shareholders recently, showcasing a need for investors to broaden their exposure. Concept of High-Dividend ETFs As the name implies, these types of funds typically offer higher payout yields compared to the average ETF. They tend to come in all shapes and sizes, so it’s important to understand that many of these funds are outside of your investment objectives. For example, most individual investors would not have the risk tolerance for the UBS ETRACS Monthly Pay 2X Leveraged Mortgage REIT ETN (NYSEARCA: MORL ), which has a current dividend yield of over 30% and is considered one of the highest yielding ETFs out there unless they were looking for specific exposure the real estate market. There are, however, some more general high-yield ETFs that are of interest to someone looking to mix up their investments which maintaining their overall asset allocation. For example, someone may want to have 50% of their funds invested in U.S. Equities. Instead of having that portion of your portfolio in something like the PowerShares QQQ Trust ETF (NASDAQ: QQQ ), you could split up your U.S. Equity exposure and invest some of that money in a higher yielding ETF like the Global X Super Dividend U.S. ETF (NYSEARCA: DIV ). Of course, the industry makeup of these two funds are vastly different – QQQ is highly focused in the technology sector while DIV has more exposure to Utilities and Real Estate, but this could actually prove to help with overall diversification for your investment in a particular economy like U.S. Equities. One of the reasons I have tried to increase my exposure to dividend-paying stocks and ETFs is because, according to a study by BlackRock, they have outperformed non-dividend paying companies over the long-term. As you will see below, this is the case both in bull markets and bear markets. Risks One negative view has surfaced regarding dividend ETFs recently. An article on Bloomberg showcased that for the first time ever, dividend ETFs are projected to have an outflow of capital for the year. Although there are many reasons for this phenomenon, including investors choosing to change their investment mix to other markets that may not be as much dividend-paying as growth-oriented, it is a trend that needs to be watched to ensure there isn’t significant downward pressure on the actual price of these ETFs. As always, it is important when using ETFs in your portfolio to review and understand the underlying investments (i.e. companies) that are held in the portfolio. As long as dividend-paying companies continue to perform well and corporations continue to pay and grow their dividend, there shouldn’t be any significant risk to these funds. Portfolio Strategy For an investor looking to produce some extra income, and potentially even diversify their portfolio more, high-yielding ETFs are a great product to help you achieve this goal. What I would recommend, especially at the beginning, would be to structure your portfolio in a way that only half of a given asset allocation is invested in a high-yield ETF to begin with. Similar concept to my example above, let’s say you currently have a portfolio of 30% Fixed Income, 40% U.S. Equities, and 30% International Equities. I would recommend keeping 15% of your Fixed Income investments the same and the other 15% I would find a high-yield bond ETF to keep the same exposure to fixed income, but with more income; keep in mind that, especially true with fixed income, higher yield is typically higher risk investments. Similarly, I would take 20% of your U.S. Equity allocation and invest in a high-yield U.S. Equity ETF, like DIV I mentioned above. Finally, I would take 15% of the funds I have in International Equities and find a similar type of non-North American ETF that offers a high-yield. One such example would be the FlexShares International Quality Dividend Index ETF (NYSEARCA: IQDF ). Something like the Arrow Dow Jones Global Yield ETF (NYSEARCA: GYLD ) may work as well, keeping in mind that as a “Global” ETF it would still have exposure to the U.S. market so you need to be careful to ensure your overall portfolio allocations are still intact. As always, if this type of investment is of interest to you I highly recommend speaking to a licensed financial professional to see which funds match your overall risk and return objectives.