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Our Investing Biases Are Particularly Dangerous Because They Are Time-Based Rather Than Phenomenon-Based

By Rob Bennett I read an article this week that explored the differences between how we have responded as a society to the pushes for limits on smoking and on guns. The push for limits on smoking has been highly successful. The push for limits on guns has not been terribly successful. Why? The article argued that the difference is that smoking is not an ideological or cultural issue; neither conservatives nor liberals see efforts to limit smoking as an attack on their world view. It’s different with guns. Most cities are heavily liberal and most rural areas are heavily conservative. As a result, there are strong ideological and cultural differences between those who own guns and those who do not. Those who have never been around guns have a hard time understanding why anyone would feel a need to own one. But those who have been around guns all their lives cannot understand why those favoring limits on ownership are so troubled by guns. So efforts to change the law in this area produce intense conflicts; the harder one side pushes for limits, the harder the other side opposes those limits and gridlock results. “Bias” is not one thing. There are many varieties of biases, some more problematic than others. In fact, an argument can be made that some biases are good. As a general rule, it is a bad thing to be biased because to possess a bias is to respond unthinkingly to a phenomenon. But acting on the basis of a bias speeds up one’s reaction time and that is not such a bad thing in some cases. I have a strong bias against disco. I have probably missed out on some disco songs from which I would have derived a pleasurable listening experience. But there aren’t many disco songs that fall into that category. And my bias helped me avoid a lot of painful listening experiences too. The biases that many of us hold about investing issues are extremely damaging, in my view. Most biases are phenomenon-based. We favor certain types of food over others. Or we favor certain ways of thinking about issues over others. Or we favor certain ways of doing things over others. These biases can hold us back. But the good thing about phenomenon-based biases is that we can limit the power of the bias by deliberately exposing ourselves to the opposite sort of phenomenon from time to time to check whether the bias is supported by the realities. Liberals are biased against conservative ideas and conservatives are biased against liberal ideas. Is that really such a bad thing? If we reconsidered our philosophical orientation each time a new issue was presented to us for our assessment, it would take much longer for us to figure out where we stand on issues. The reality is that once a person has thought about a few issues hard enough to know where his bias lies, he can save time when assessing new issues by jumping to a quick conclusion that his position will be ideologically consistent with his earlier positions. Being biased is a time-saver. But there are dangers, of course. There are always those few issues regarding which a liberal adopts the conservative take and those few issues regarding which a conservative adopts the liberal take. Those exceptions can achieve great significance over time. If you follow the story of how a liberal becomes a conservative over a number of years or of how a conservative becomes a liberal over a number of years, you will see that it is usually one important exception to a general bias that starts the ball rolling in a new direction. I often seek out views different than my own just to shake up my preconceptions a bit. It’s very very hard to do that in the investing realm. The most important investing biases are time-based rather than phenomenon-based. That means that for long periods of time certain ideas are forgotten by almost the entire population. To tap into the other side of the story, the investor would have to study historical data from a time period many years removed from the current time period. Who does that? Shiller showed that valuations affect long-term returns. What he really was doing when he did that was showing that the stock market is not efficient, that mis-pricing on either the high or low side is a significant reality rather than the illusion that Buy-and-Holders believe it to be. Even during the most out-of-control bull market, there are a small number of people questioning whether the insane prices achieved are real and lasting. But the percentage of the population holding that view can be very small indeed. The percentage of the population that is conservative rather than liberal doesn’t vary dramatically from time to time. The percentage of the population that believes that stocks are the perfect investment choice is dramatically higher when prices are high than it is when prices are low. For a good number of years following the great crash of 1929, investors didn’t expect to see any capital appreciation at all on their stocks. The conventional wisdom of the time was that stocks were worth buying only for their dividends; those that didn’t pay high dividends were not worth owning. In the late 1990s, dividends fell to tiny levels. The very thing that made stocks dangerous (their high price) changed the conventional wisdom on stock ownership to reflect a bias that stocks are always worth owning. Stocks for the Long Run was a popular book in the 1990s. It would not have sold many copies in the 1930s. The book reports on data, facts, objective stuff. The message of the data should not change from times like the 1930s to times like the 1990s. But the ways in which we arrange the data and interpret the data changes when we go from bull markets to bear markets. People will be looking at the same data that was employed in Stocks for the Long Run to sell stocks to make the case against stocks when we are on the other side of the next stock crash. Our stock biases hurt us. But they are hard to see through because just about everyone is on one side of the table for a long stretch of time and then just about everyone is on the other side of the table for the next long stretch of time. Bull markets turn us all into bulls and bear markets turn us all into bears. Investing biases come to be so widely shared for long stretches of time that it is hard for any of us to keep their other point of view even remotely in mind. Disclosure: None

Reducing Portfolio Risk With Help From Momentum Model

Reduce portfolio risk by activating momentum model. Reduce portfolio risk based on security volatility. Reduce portfolio risk through the use of stop-loss orders. Controlling portfolio risk is every bit as important as seeking portfolio return, particularly when markets are high and volatile. The following analysis takes readers through a process of controlling portfolio risk with help from a tranche momentum spreadsheet. Main Menu: We begin with the following Main Menu where the basic assumptions are laid out by the portfolio manager. In the following example we are using twelve (12) ETFs plus SHY as the cutoff security. Hence the name, Baker’s Dozen. Many of the ETFs carry low correlations with each other, an important factor to consider when identifying securities to populate a momentum oriented portfolio. In the follow screen-shot we set the number of offset portfolios to 8 and the period between offsets to two (2). What this means is that the securities are ranked multiple times (8) on different dates (separated by 2 days) based on two different look-back periods plus volatility. Using these three metrics, the ETFs are ranked each review period. My preference is to review a portfolio every 33 days so the review is rotated throughout the month. Not only are the ETFs ranked based on current data, but they are ranked two, four, six, eight, and etc. days ago so we know what the rankings looked like up to sixteen (8 x 2) days ago. The look-back periods are 60 and 100 trading days. A 20% weight is assigned to the volatility as we are looking for securities with low volatility. Only two securities are selected for each offset portfolio. This becomes more apparent in the second screen-shot so move down to that slide. (click to enlarge) Tranche Recommendations: Here we have what is called the Tranche Momentum model worksheet. This is the first of three risk reducing mechanisms. The tranche model is designed to reduce the “luck-of-trading-day” as this is a problem inherent in all back-tests as well as real portfolio management. Instead of splitting the portfolio into 50% VNQ and 50% MTUM , as the current offset recommends, we note that offset 3 recommended divisions between VNQ and TLT . Offset portfolio #5 recommended 50% allocation to SHY and 50% to VNQ. Using eight (8) portfolio offsets ends up dividing the portfolio into four securities where the percentages are based on the number of times the ETF shows up in one of the eight rankings. The worksheet permits as many as 12 portfolio offsets, but I tend to favor using eight. The following worksheet ranks the ETFs using both absolute and relative momentum principles. Readers will note that the current portfolio holds 200 shares in VTI, but the tranche momentum model recommends none as VTI is under-performing SHY, our “circuit breaker ETF.” Momentum becomes one of our risk reducing mechanisms as under-performing securities are screened out of the active portfolio. (click to enlarge) Risk Reduction Recommendations: The following worksheet combines recommendations from the above tranche data and adds a volatility factor to come up with a list of recommended ETFs. In the following slide the Maximum Trade Position Risk percentage is set to 2.0% so the total portfolio is not exposed to more than a 6% draw-down until the next review period. The still leaves individual ETFs at unacceptable risk levels which we control in the final screen-shot. Before moving to the final slide, look at the individual recommendations. Shares held in VTI and PCY are sold out of the portfolio as VTI is under-performing SHY and PCY has not shown up as a recommended ETF in any of the last 8 offset portfolios. The recommendations are to hold the following four ETFs. 75 shares of SHY – round up from 74. 300 shares of VNQ – rounded to the nearest 100 shares. 100 shares of TLT – rounded to the nearest 100 shares. 350 shares of MTUM – rounded to nearest 50 shares. (click to enlarge) Manual Risk Reduction Recommendations: For the final risk reduction activity the recommendations from the above worksheet are followed which still leaves a few ETF exposed to excess risk. The final step is to place stop-loss or Trailing Stop Loss Orders (TSLOs) on VNQ and MTUM. VTI is either sold at market or a 6% TSLO is used. While the current portfolio holds $8,000 in cash, the recommendation is to increase it to $32,500. Note that the current portfolio carries a risk of 4.8%, but if the suggested adjustments are made, the risk drops to 3.4%. (click to enlarge) With the aid of the tranche momentum spreadsheet we limit portfolio risk through absolute and relative momentum principles as these keep us out of deep bear markets. Further portfolio risk is controlled by placing stop-loss orders as a way of clamping down on excess draw-downs. Granted, these procedures work when we have an orderly market. Guarding against “flash crashes” is an entirely separate problem.

3 Emerging Market ETFs With Q4 Gains

Wrong were those investors who thought emerging markets would perform miserably in the fourth quarter due to the looming Fed tightening. The gradual waning of cheap dollar inflows post lift-off, the resultant rise in the greenback, sluggish emerging currencies, high inflation issues, political disorder and the commodity market rout were deemed to dull the appeal of emerging markets. The theory wasn’t completely baseless. The broader emerging market ETF iShares MSCI Emerging Markets (NYSEARCA: EEM ) has lost 17% so far this year and over 3.3% so far this quarter (as of December 18, 2015). But not all emerging market equities and the related ETFs have been vulnerable. At least, Q4 performance of a few emerging market ETFs has been noteworthy. Investors should note that the S&P 500-based SPDR S&P 500 Trust ETF (NYSEARCA: SPY ) has added over 1.8% so far this quarter (as of December 18, 2015) and the world ETF iShares MSCI ACWI (All Country World Index) Index (ACWI ) is up 0.3%. A couple of emerging market ETFs have managed to climb and two funds even impressed with their double-digit returns in the quarter-to-date period (as of December 18, 2015). Interestingly, these top performers are spread across various sectors or countries and could be better plays in the current market. This suggests that there have been winners in every corner of the space, even amid a sluggish overall trend. Below, we highlight three top-performing emerging market ETFs in the quarter-to-date frame. Emerging Markets Internet & E-Commerce ETF (NYSEARCA: EMQQ ) – Up 23.3% The Internet and e-commerce industry is developing fast with the increased use of social networking sites and online trading as well as the growing adoption of smartphones and other mobile Internet devices. So, this product has more to do with technological expansion in the emerging markets rather than reflecting the slowing potential of those economies. In fact, EMQQ can succeed on the back of a fast-expanding middle-class population of emerging nations. This $12-million ETF considers companies from Asia, Latin America, Africa and Eastern Europe. Country-wise, China takes the highest allocation in the fund. Alibaba (NYSE: BABA ), Baidu (NASDAQ: BIDU ) and Baozun (NASDAQ: BZUN ) are the top three holdings of the fund. EMQQ charges 86 bps in fees and is up 23.5% so far in the fourth quarter (as of December 18, 2015). First Trust ISE Chindia ETF ( FNI ) This fund follows the ISE Chindia Index, which measures the performance of the liquid firms domiciled either in China or in India. Notably, even after the upheaval in August, Chinese stocks are among the top and stellar performing securities in the emerging market pack. As far as India is concerned, it is one of the most stable emerging markets at the current level in terms of economic growth and corporate profitability. It has accumulated nearly $230 million in its asset base. The product puts nearly 50% of its assets in the top 10 holdings, with JD.com (NASDAQ: JD ), Tata Motors (NYSE: TTM ) and NetEase (NASDAQ: NTES ) being the top three firms. From a sector look, more than 35% of the assets are allocated to information technology while about one-third goes to consumer discretionary. FNI charges 60 bps in fees per year from investors and has returned 12.63% so far in the quarter. The product looks to track 50 emerging market-based depositary receipts. The fund invests about 45% of assets in China while Taiwan, Brazil and India get the next three positions with 14%, 12.5% and 10.4% weight, respectively. The fund charges 30 bps in fees. Sector-wise, the fund is heavy on information technology (38.68%) while telecom (16.75%), financials (14.7%) and energy (10.2%) get double-digit exposures. Alibaba (11.5%) and Taiwan Semiconductor (NYSE: TSM ) (10.6%) are the top two stocks of the fund. ADRE is up 5.6% in the quarter-to-date frame and has a Zacks ETF Rank #3 (Hold) with a Medium risk outlook. Original