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4 Tactical/Momentum ETFs: A Disappointing 1-Year Anniversary

Summary Four ETFs, introduced late last year, have the ability to switch between stocks and bonds, on a tactical/momentum basis. How did these four funds fare during the August correction? Since inception, only one of the four ETFs has outperformed the global market portfolio. Introduction In a Nov. 2014 article entitled ” Comparing 4 Tactical/Momentum ETFs “, I introduced four newly-debuted tactical/momentum ETFs that have, at the minimum, the ability to switch between stocks and bonds depending on tactical factors such as momentum (thus equity-only momentum funds are not considered). I later provided a short update on the performance of the four ETFs in a Aug. 2015 article entitled ” An Update On 4 Tactical/Momentum ETFs “. In that article, I noted that while the four ETFs averaged only -1.19% over the preceding nine months, underperforming U.S. stocks (via the SPDR S&P 500 Trust ETF (NYSEARCA: SPY )) at +5.35%. However, that update article was published just before the S&P 500’s first 10% correction in several years. The last few months of market action has been…interesting, to say the least, and with the 1-year birthday of these four tactical/momentum ETFs having just recently elapsed, I thought that now would be a good time to review the performance and allocation of these four funds. The funds The four funds included in this analysis listed below. For more detailed information regarding these funds, please refer to my previous article . Cambria Global Momentum ETF (NYSEARCA: GMOM ). GMOM invests in the top 33% of a target universe of 50 ETFs based on measures of trailing momentum and trend. The fund rebalance monthly into ETFs with strong momentum and are in an uptrend over the medium term of approximately 12 months with systematic rules for entry and exit. Global X JPMorgan US Sector Rotator Index ETF (NYSEARCA: SCTO ). SCTO invests in a portfolio of one to five ETFs selected out of a pool of ten U.S. sector ETFs and the iShares 1-3 Year Treasury Bond ETF (NYSEARCA: SHY ). The fund rebalances monthly to invest in a maximum of 5 U.S. sectors that have demonstrated the strongest positive recent performance. If less than 5 sectors have demonstrated positive performance over this time period, the remainder will go to SHY. Global X JPMorgan Efficiente Index ETF (NYSEARCA: EFFE ). EFFE invests in any combination of 13 ETFs drawn from 5 asset classes. The fund rebalances monthly, constructing an “efficient frontier” by calculating the 6-month returns and volatilities of multiple hypothetical portfolios based on different combinations of the index component universe, then selects the combination of assets that resulted in the highest return over the 6 month observation period with an annual realized volatility of 10% or less. Arrow DWA Tactical ETF (NASDAQ: DWAT ). Implements a proprietary Relative Strength Global Macro model developed by Dorsey Wright & Associates, holding approximately 10 broad-based positions. Assets include long/short exposure to domestic, international and emerging market equities and bonds (government, corporate, agency), real estate, currencies and commodities. Details of the four funds are shown in the table below (data from Morningstar ).   GMOM SCTO EFFE DWAT Yield [ttm] 2.33% 0.50% 0.68% 0.39% Total expense ratio 0.94% 0.86% 0.86% 1.52% Management fee 0.59% 0.69% 0.69% 1.22%* Acquired expense ratio 0.35% 0.17% 0.17% 0.30% Inception Nov 4,2014 Oct 22,2014 Oct 22,2014 Oct 1,2014 Assets $25.92M $13.47M $8.11M $7.80M Avg vol. 12K 11K 12K 7.6K Annual turnover 16% 63% – 111% *Composed of management fee 1.00%, other expenses 0.22%. All four funds have low but not negligible volume, and should provide sufficient liquidity for ordinary investors. Additionally, all four funds have increased in assets since a year ago. GMOM increased slightly from $23.85M to $25.92M, while SCTO increased from $11.54 to $13.47. DWAT showed a sizable increase from $5.18M to $7.80. However, the biggest winner over the pats year appears to be EFFE, which more than tripled in size, from $2.58M to $8.11M. Performance Let’s now take a look at the performance of the four tactical/momentum ETFs in 2015, with the U.S. market (via SPY) included for comparison. GMOM Total Return Price data by YCharts The analysis of this total return price chart reveals some interesting features. Firstly, none of the tactical/momentum ETFs were able to keep pace with SPY in the first eight months of the year, i.e. before the August correction. This might not be surprising for GMOM, even EFFE and DWAT, as these draw ETFs from a wide pool of asset classes and not only U.S. equities, which has been one of the best-performing markets during this difficult year. However, the egregious performance of SCTO is concerning. The fact that SCTO underperformed SPY by the largest margin over the first eight months of 2015 is especially surprising given that its investment universe is restricted to only U.S. industry sectors and what is essentially a cash proxy! How on earth did it lag SPY by nearly 10% over the first eight months of the year if its mandate is to “invest in a maximum of 5 U.S. sectors that have demonstrated the strongest positive recent performance.” Global X provides a monthly allocation report for SCTO. We can see from the report that has had significantly allocations to SHY (i.e. cash) during the first eight months of the year, ranging from 20% in Feb. 2015 to 80% in Jul. 2015. (click to enlarge) Can we understand the reasons for SCTO’s serious underperformance compared to both SPY as well as the other three tactical/momentum ETFs? Analysis of the monthly allocations of SCTO suggests that this may have been due to the ETF being too sensitive to fluctuations in the equity markets, causing it to switch very frequently between equity and cash. For example, SPY suffered a -2.96% loss in Jan. 2015, which caused SCTO to switch to 80% equities in defensive sectors such as REITs (NYSEARCA: RWR ), consumer staples (NYSEARCA: XLP ), healthcare (NYSEARCA: XLV ) and utilities (NYSEARCA: XLU ) and 20% cash at the start of February. Of course, SPY then posted a 5.62% return in February, led by high-beta stocks, and the defensively-positioned SCTO sorely lagged during this rally. Similarly, SCTO was 100% invested in equities when SPY suffered a -2.03% loss in Jun. 2015, then switched to 80% cash for July, during which SPY reversed course to the tune of a 2.26% gain. SCTO then switched BACK to 100% equities at the start of August, just in time for the correction. Talk about bad timing! But let’s step back and analyze all four of the ETFs during this period. Responding to the correction The following chart shows the total return performance of the four tactical/momentum ETFs as well as the U.S. equity market and the U.S. bond market (NYSEARCA: AGG ) from just before the August correction to the end of the year. GMOM Total Return Price data by YCharts All four tactical/momentum ETFs dropped sharply with SPY in August as the correction hit. This is not surprising given that most of these ETFs would be expected to have a sizable allocation to U.S. equities given its status as one of the better-performing markets in early 2015. However, what happens after the correction is illuminating. At the start of September, GMOM, SCTO and EFFE decrease suddenly in volatility, suggesting that they have shifted significantly to bonds or cash. This is confirmed at least for SCTO which showed a 100% allocation cash in September. This shift therefore allowed those three funds to avoid the equity market gyrations in September. On the other hand, the performance of DWAT tracked closely with SPY, suggesting that this fund had not yet made a switch away from equity holdings. As expected, none of four ETFs were able to capture the ferocious snap-back rally exhibited by SPY in October (+8.51%). DWAT increased by around half that of SPY, while SCTO also rose slightly due to its 18.6% allocation to REITs and 21.4% allocation to utilities, however, the rest of SCTO was in cash. Rather unfortunately, all four funds appear to have switched back into an equity-heavy portfolio in November and December, just as the rally subsided and choppy market behavior resumed. This can be deduced given that all four ETFs follow the ebbs and flow of the broader market during these two months. Discussion and conclusion To say that all four tactical/momentum ETFs have disappointed in their first year of existence would be an understatement. None of the four funds were able to avoid the August correction of 2015. Three of the four funds (GMOM, SCTO and EFFE) then switched to cash or bond-heavy portfolios in September, which caused them to completely miss the stock market rebound a month later. This phenomenon was more comprehensively analyzed for GMOM in my Nov. 11 article ” GMOM: Momentum Swings From Bonds Back To Stocks “. On the other hand, based on its price action compare to SPY, DWAT appeared to remain fully invested in equities in September, but reduced its equity exposure to approximately 50% in October. As DWAT is an actively-managed ETF, it is not clear whether the delayed reduction of equity exposure involved any discretionary decisions by the portfolio manager. The next chart shows the total return performance, over the past 13 months, of the four ETFs compared to both SPY and a global market portfolio (via the Cambria Global Asset Allocation ETF (NYSEARCA: GAA )) at -1.02%, which Seeking Alpha author GestaltU has proposed is a superior benchmark for global tactical asset allocation [GTAA] strategies than the S&P500. We can see from the chart below that DWAT has had the best total return performance of -2.77% out of the four tactical/momentum ETFs during this time span, followed by GMOM at -6.87%. EFFE and SCTO had the lowest total return performances of -8.02% and -8.96%, respectively. Thus, DWAT was the only ETF to outperform the global market portfolio GAA since last November, and all four ETFs underperformed SPY. GMOM Total Return Price data by YCharts Going forward, what can we expect from these ETFs? Currently, the four ETFs show very different equity/bond distributions (data from Morningstar). SCTO has the highest equity allocation at nearly 100%, followed by DWAT at 74%. GMOM has a nearly 50:50 split of equities and bonds. EFFE is the only ETF with more bonds (60%) than stocks (40%). However, given that at least three of the four funds (all except DWAT, whose schedule is unspecified) rebalance monthly, these allocations are likely to change in January. In terms of the North American (mainly U.S.) versus international allocation of their equity portion, all except GMOM are fully domestic. GMOM contains 87% U.S. equities and 13% international equities. On a personal level, I have sold my holdings of GMOM a few months ago. I have replaced this the iShares MSCI USA Momentum Factor Index ETF (NYSEARCA: MTUM ) (as described in Left Banker’s article here ). My existing holding of the First Trust Dorsey Wright Focus 5 ETF (NASDAQ: FV ) has also done very well. Both have outperformed SPY over the past year. MTUM Total Return Price data by YCharts Note that those two ETFs are momentum-based but are not “tactical” in the sense that they cannot switch to bonds or cash, and moreover they are purely U.S. based. If the U.S. market enters a bear market, it is likely that those two funds will underperform the tactical/momentum ETFs described above. I am simply performance chasing the U.S. market here? Perhaps, but I lost patience in watching the NAV of GMOM gradually decline as it got caught between whipsaws. With my sale of GMOM, this will likely be my last article on tactical/momentum ETFs for the time being, unless their performance improves to such an extent that they warrant consideration for investment.

CEFL: A Year In Review, And A Prediction Of What’s Ahead

Summary 2015 has not been a good year for CEFL unitholders: income declined by 20% while price declined by 33%. This article presents a review of CEFL happenings in 2015, and a forecast of what’s ahead for 2016. Based on the publicly available index methodology, the CEFs to be added or removed are predicted. Introduction The ETRACS Monthly Pay 2xLeveraged Closed-End Fund ETN (NYSEARCA: CEFL ) is a 2x leveraged exchange-traded note [ETN] that tracks twice the monthly performance of the ISE High Income Index [symbol YLDA]. The YieldShares High Income ETF (NYSEARCA: YYY ) tracks the same index, but is unleveraged. CEFL is a popular investment vehicle among retail investors due to its high income (24.52% trailing twelve months yield), which is paid monthly. With 2015 nearly behind us, I thought I would review the characteristics of this year’s iteration of CEFL, and also look ahead at what might be in store for us in 2016. (Source: Main Street Investor ) 2015 portfolio YLDA holds 30 closed-end funds [CEFs], and is rebalanced annually. As I have previously discussed in my three-part “X-raying CEFL” series, this year’s iteration of CEFL (and thus also YYY) had the following characteristics: CEFL is comprised of approximately one-third equity and two-thirds debt, is effectively leveraged by 240% and has a total expense ratio of 4.92% per dollar invested in the fund (or 2.05% per dollar of assets controlled) (discussed in ” X-Raying CEFL: Leverage And Expense Ratio Statistics “). CEFL contained around two-thirds of North American (primarily U.S.) assets, with the rest being international. Moreover, the North American component of CEFL contains a higher allocation to debt vs. equity than the European component of CEFL (discussed in ” X-Raying CEFL (Part 2): Geographical Distribution “). CEFL is not very interest-rate sensitive as most of the holdings of CEFL are most-correlated with high-yield debt (discussed in ” X-Raying CEFL (Part 3): Interest Rate Sensitivity “). Actually, I might have been inaccurate in my last prediction. Over the last year, the price action of CEFL has actually moved in the same direction to interest rates, which is exactly opposite to what would be expected for a traditional bond fund. But this is not entirely surprising for CEFL, because high-yield debt usually tend to trade in tandem with equities and in the opposite direction to treasuries. Indeed, CEFL had a positive +0.71 correlation with U.S. equities (via SPDR S&P 500 ETF (NYSEARCA: SPY ) over the past year, but a negative -0.24 correlation with treasuries (via the iShares 20+ Year Treasury Bond ETF) (NYSEARCA: TLT ) (source: InvestSpy ). Thus, readers who worried that higher interest rates would lower the price of CEFL may actually have been pleasantly surprised that the opposite has held true this year. Decreasing yield Seeking Alpha author Professor Lance Brofman has done a wonderful job predicting the upcoming distributions for CEFL (see his latest article here ), while also providing expert commentary in his area of expertise. The distribution history for CEFL, which now has paid out 24 months of dividends, is presented below. Unfortunately, we see that the distributions paid out by CEFL have been in decline. In 2014, each share of CEFL paid out $4.74 of distributions, but in 2015, each share of CEFL only paid out $3.82 of distributions. This means that the distribution of CEFL has declined by 19.5% year on year. I believe that a large reason for the distribution decline can be attributed to the rebalancing debacle that occurred at the turn of this year (see below). CEFL has a current trailing twelve months yield of 24.52%. Rebalancing debacle The annual rebalancing in the index YLDA was disastrous for CEFL and YYY holders. The reasons for this have been summarized in my recent article ” Are You Ready For CEFL’s Year-End Rebalancing ?” In short, up to 10% of the net asset value of CEFL may have been lost due to traders (including, perhaps, UBS themselves) buying and selling the CEFs to be added or removed from the index ahead of the actual rebalancing date (a form of “front-running,” see this Bloomberg article for more information on this phenomenon). For further study on the rebalancing issue, consult my previous articles on this issue in the below links: Predicting the 2016 portfolio How might the portfolio of CEFL change upon the next rebalancing event, which is scheduled to occur in the next few days? As discussed in my most recent CEFL article, the index provider has decided that upcoming index will not be announced 5 days in advance. This was intended to prevent “front-running” of the index. However, with the index methodology published and available to all, I had little doubt that professional investors would be able to use the selection rules to determine which stocks would be added or removed from the index. Therefore, in an attempt to level the playing field for everyone else, I have tried to approximate the index methodology in order to predict CEFL’s portfolio for 2016. The selection methodology for the index is reproduced below (source: ISE ). 1. Restrict selection universe to closed-end funds with market cap > $500M and six month daily average volume > $1M. 2. Rank each fund by the following three criteria: i. Fund yield (descending) ii. Fund share price Premium / Discount to Net Asset Value (ascending) iii. Fund Average Daily Value (ADV) of shares traded (descending) 3. Calculate an overall rank for each fund by taking the weighted average of the three ranks with the following weightings: yield: 50%, premium/discount: 25%, average daily value: 25%. 4. Select the 30 funds with the highest overall rank. Using CEFAnalyzer , I obtained a list of the 141 CEFs with market cap > $500M. Unfortunately, I was unable to apply a volume filter because I was not sure what specific time period CEFAnalyzer reports volume data for. I then replicated the index methodology for the 141 CEFs on this list. The below table shows the top 30 CEFs for either distribution yield or discount among the CEFs with market cap > $500M. Rank Ticker Yield Rank Ticker Discount 1 GGN 17.14% 1 BCX -16.92% 2 PHK 14.58% 2 AOD -16.88% 3 KYN 14.44% 3 AWP -16.29% 4 NHF 14.23% 4 IGR -16.19% 5 HIX 13.06% 5 FAX -16.09% 6 TDF 13.03% 6 RNP -15.64% 7 IGD 12.67% 7 GLO -15.33% 8 RVT 12.40% 8 RVT -15.07% 9 CEM 11.73% 9 NFJ -15.04% 10 PTY 11.69% 10 DPG -15.04% 11 GLO 11.37% 11 UTF -14.93% 12 GAB 11.21% 12 ADX -14.92% 13 EXG 11.17% 13 TY -14.81% 14 BCX 11.12% 14 WIW -14.79% 15 CHI 11.11% 15 TDF -14.63% 16 ETJ 11.05% 16 NXJ -14.58% 17 EAD 10.94% 17 NHF -14.57% 18 DSL 10.89% 18 NIE -13.63% 19 CHY 10.89% 19 NQP -13.40% 20 PFN 10.75% 20 USA -13.32% 21 PCI 10.74% 21 FSD -13.31% 22 FEI 10.44% 22 BIT -13.04% 23 ETW 10.36% 23 GDV -12.81% 24 AWP 10.24% 24 JQC -12.70% 25 NTG 9.95% 25 CAF -12.50% 26 PCN 9.86% 26 IGD -12.41% 27 CSQ 9.81% 27 VTA -12.33% 28 PDI 9.62% 28 RQI -12.27% 29 NFJ 9.62% 29 BDJ -12.10% 30 EVV 9.59% 30 NQU -12.04% The yield ranking was then weighted by 50% while the discount ranking was weighted by 25% (the rankings are assigned to all 141 CEFs, and not only to the top 30). The ranking for volume is not shown above because I was not sure about the time period used by CEFAnalyzer to calculate volume, as alluded to earlier. However, because I did not have time to manually calculate the ADV for 141 CEFs, the CEFAnalyzer data was still used to obtain a volume ranking for the funds, which was weighted by 25%. The weighted rankings were then summed, and the top 30 CEFs with the highest overall ranking are shown below, along with their composite individual ranks. A quick check on Yahoo Finance indicated that the 3-month ADV of these 30 CEFs was above the $1M cut-off (which is actually for the 6-month ADV, but I did not calculate this). Rank Ticker Yield Discount Volume Overall 1 (NYSE: RVT ) 8 8 18 10.50 2 (NYSE: BCX ) 14 1 25 13.50 3 (NYSEMKT: GGN ) 1 42 16 15.00 4 (NYSEMKT: GLO ) 11 7 39 17.00 5 (NYSE: NFJ ) 29 9 15 20.50 6 (NYSE: IGD ) 7 26 48 22.00 7 (NYSE: EXG ) 13 50 13 22.25 8 (NYSE: PCI ) 21 39 11 23.00 9 (NYSE: HIX ) 5 79 12 25.25 10 (NYSEMKT: EVV ) 30 35 17 28.00 11 (NYSE: DPG ) 33 10 38 28.50 12 (NYSE: AOD ) 44 2 24 28.50 13 (NYSE: NHF ) 4 17 96 30.25 14 (NYSE: DSL ) 18 77 8 30.25 15 (NYSE: CEM ) 9 100 5 30.75 16 (NASDAQ: CSQ ) 27 52 19 31.25 17 (NYSE: KYN ) 3 119 2 31.75 18 (NASDAQ: CHI ) 15 96 1 31.75 19 (NYSE: TDF ) 6 15 104 32.75 20 (NYSE: AWP ) 24 3 83 33.50 21 (NYSE: USA ) 31 20 58 35.00 22 (NYSE: BGB ) 36 46 26 36.00 23 (NYSE: NTG ) 25 88 6 36.00 24 (NYSE: FEI ) 22 97 7 37.00 25 (NYSE: BIT ) 47 22 32 37.00 26 (NYSE: UTF ) 54 11 29 37.00 27 (NYSE: BOE ) 40 41 30 37.75 28 (NYSE: GHY ) 39 47 27 38.00 29 (NYSE: ETJ ) 16 56 71 39.75 30 (NYSEMKT: FAX ) 41 5 72 39.75 At this point, I would like to compare notes with reader waldschm85 : I’ve attempted to follow the index methodology and came up with the below holdings from largest to smallest as of the open. How does this compare to your list Stanford Chemist?: BCX, TDF, GGN, RVT, KYN, PCI, NFJ, NTG, IGD, NHF, EXG, CSQ, GLO, DPG, CEM, , FEI, CHY, DSL, CHI, USA, HIX, PHK, GAB, TYG, EAD, ETJ, PTY, ETW, PFN, PCN Comparison of our two lists show that we have 20 out of 30 CEFs in common, which is quite high considering that [i] we did our analyses every days apart and [ii] I used an unspecified volume figure for ADV ranking while waldschm85 may have used a more accurate method. While the weighting methodology is too complex to be reproduced here, it can be noted that last year’s rebalance produced the CEF distribution shown below. The methodology states that no CEF can comprise more than 4.25% of the index. Additionally, the top 15 largest CEFs after last year’s rebalance all had weights of above 4%. I expect the weighting distribution of the 30 CEFs after this year’s rebalance to be quite similar to the last. Additions and deletions (predicted) Here we get to the interesting part! Which funds are completely new, and which will be completely removed? Which CEFs are in both 2015 and 2016 (predicted) portfolios? The following will be performed with my list of top 30 CEFs – obviously results will differ using waldschm85’s list or that of another person’s. CEFs are presented in alphabetical order. Added CEFs: BCX, BOE, CEM, CHI, CSQ, DPG, ETJ, FEI, IGD, KYN, NFJ, NHF, NTG, PCI, RVT, TDF, USA, UTF Removed CEFs: BGY, CHW, EAD, EDD, ERC, ESD, ETY, FPF, HYT, IGD, ISD, JPC, MRC, MMT, NCV, NCZ, PCI CEFs that remain from last year: AOD, AWP, BGB, BIT, DSL, EVV, EXG, FAX, GGN, GHY, GLO, HIX. The information above shows that 18 CEFs will be added to the index and 18 will be removed. 12 CEFs will remain in the index. This is a relatively high turnover but it is not unexpected given the fact that both the distributions and premium/discount values of CEFs can vary wildly. Moreover, given that I did not calculate weightings for the 2016 portfolio, I was unable to predict which CEFs will undergo the highest increases or decreases in allocation. However, it should be stressed that the above lists are only approximate. This is because I only performed a crude replication of the index methodology (specifically, I did not use the six-month ADV for either screening or ranking), and also because of the fact that the actual selection and ranking algorithm will be performed on CEF data at year-end rather than from today. Therefore, I am hesitant to recommend the buying of the CEFs to be added and the selling of CEFs to be removed as a potential strategy to profit from the upcoming rebalance. Use the information above at your own risk. Summary 2015 has not been a good year for CEFL unitholders. First, the botched rebalancing mechanism cause permanent loss of value in the index. Second, CEFL holders received 19.5% less income in 2015 compared to last year (this may be related to the first point). Third, CEFL shifted from a 60:40 equity:bond split in 2014 to a 33:67 equity:bond split this year, just in time for the oil-induced credit contagion to wreck havoc with the high-yield debt CEFs in the index. Certainly, a -32.7% YTD price return and -18.4% YTD total return cannot be described as anything other than disappointing for CEFL unitholders. CEFL data by YCharts Will 2016 bring brighter skies for CEFL? This I cannot say for certain. However, it is interesting to note that the predicted portfolio for 2016 contains several MLP CEFs, namely KYN, CEM, NTG, and FEI, whereas this year’s index contained none. Moreover, a myriad of high-yield bond funds will remain or are newly added to the predicted 2016 portfolio. Thus, it remains likely that the fate of CEFL will remain closely tied with the fortunes of the high-yield credit market for the foreseeable future.

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