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Buy The Fourth Quarter Of The Third Year Of The Presidential Cycle

The best time to buy the Presidential Election Cycle is from September of the second year to April of the third year. Nevertheless, the fourth quarter of the third year is strong, particularly after a weak third quarter. In the past, it was better to buy near the end of October than at the end of September. How does the fourth quarter do in the third year of the Presidential election cycle? ‘Everyone knows’ that the third year of the Presidential cycle is incredibly reliable, and has returns that far exceed the other three years. Even Grantham has touted it, which I thought must be tongue-in-cheek, because he is a macro-guy. So I decided to go back and check, and found his letter written at the end of the third quarter in 2014 for GMO. It turns out he was quite serious. Regular readers know the score: +2.5% a month for the seven months from October 1 to April 30, in year three on average since 1932 (a total of +17%). This is now the 21st cycle. The odds of drawing 20 random 7-month returns this strong are just over 1 in 200 according to our 10 million trials. But 17 of the actual 20 historical experiences were up, and the worst of the 3 downs was only -6.4%, so the odds of this consistency plus the high return would be much smaller. The remaining 5 months of the Presidential year have a good but not remarkable record, over .75% per month, but the killer here is that the remaining 36 months since 1932 averaged a measly +0.2% a month!” Reference to the remaining 5 months means that Grantham views the third year of the Presidential cycle as running from September to September. More importantly, we have missed the key months from September 30 to April 30. From 2014 to 2015, that time span had the S&P 500 rising by 11.39%, which is not too shabby given what the market has done since. Yahoo Finance only had S&P 500 data as far back as 1950. So my analysis is for the 16 third years since then (see the table below). We have completed 17 years from his September to April time frame, however, and I calculated an average 19.72% return for those time periods, with a median return of 19.49%. There was only one decline of -.76% in 1978-79. But dividends have not been included. So every period actually had a positive total return. For the full calendar third year, the average return was 17.12%, with a median return of 18.08%. That’s very good also, but not as good, and that is a 12-month return versus Grantham’s 7-month return. For all years since 1950, the average calendar year gain was 9.18%. Therefore, the average gain in the other 3 years of the Presidential cycle works out to 5.69%. Out of the 16 third years, 15 were up, and one was unchanged (2011). With stocks down YTD, the odds would appear to be good that we will get a nice rally over the last three months. I say ‘appear to be good’, because statistically we can’t calculate the odds. This is a small sample. It is not a random sample. And there is no solid theory to support why the pattern of the recent past should hold in the future. Let’s see how the last three months of the third year have done since 1950. From 9/30 to the end of the year, the average gain in the S&P has been 3.04%, with a median return of 4.39%. The mean is lower because of the skew created by 1987. Third Year Pres. Cycle %ch. Oct. 31 to end of yr % ch. Sept. prev. yr to April 3rd yr % ch. Full 3rd year % ch. April to Sept. 3rd yr % ch. 9/30 to end of 3rd yr % ch. Sept. low to end 3rd yr % ch. Oct. low to end 3rd yr % ch. Sept. 30 to Oct. low % ch. Sept. low to Oct. low 1951 3.62 15.32 16.35 3.7 2.19 2.19 4.9 -2.58 -2.58 1955 7.42 17.49 26.40 15.04 4.14 6.74 11.47 -6.57 -4.25 1959 4.12 15.04 8.48 -1.23 5.29 8.61 6.95 -1.55 1.56 1963 1.36 24.04 18.89 2.72 4.63 4.63 4.38 .24 .24 1967 3.40 22.79 20.09 2.87 -.25 2.98 3.4 -3.53 -.41 1971 8.34 23.31 10.79 -5.4 3.81 4.58 8.85 -4.63 -3.92 1975 1.29 37.39 31.55 -3.93 7.54 9.86 8.75 -1.12 1.02 1979 5.91 -.76 12.2 7.43 -1.35 1.35 7.84 -8.53 -6.02 1983 .84 36.55 17.27 1.00 -.69 .43 .95 -1.63 -.52 1987 -1.87 24.66 2.03 11.61 -23.2 -20.4 9.89 -30.1 -27.6 1991 6.28 22.64 26.31 3.31 7.56 8.73 10.69 -2.83 -1.77 1995 5.92 11.24 34.11 13.54 5.39 8.28 6.65 -1.18 1.53 1999 7.8 31.28 19.53 -3.93 14.54 15.84 17.78 -2.75 -1.65 2003 5.83 12.47 26.38 8.62 11.64 11.64 9.20 2.23 2.23 2007 -5.23 10.97 3.53 2.99 -3.82 1.15 -2.15 -1.71 3.37 2011 0.34 19.49 -.003 -17.0 11.15 11.34 14.41 -2.5 -2.69 2015 11.39 -7.93 Mean 3.46 19.72 17.12 1.96 3.04 4.87 7.75 -4.32 -2.59 Med. 3.87 19.49 18.08 2.87 4.39 5.68 8.30 -2.67 -1.09 (The median date of the September low is the 21st. The median date for the October low is the 17th.) The average fourth quarter gain for all years since 1950 is 4.06% with a median of 4.92%. So the third year of the Presidential cycle has a lower average using both measures. The much lower mean is probably because of 1987, but clearly the fourth quarter of the third year is actually not as good as other years. There were 5 down quarters out of 16. They were 1967, 1979, 1983, 1987 and 2007. But all 5 years that declined from April to September 30 (1959, 1971, 1975, 1999, and 2011) had good gains in the fourth quarter . This augurs well for 2015, but 5 out of 5 does not mean we have to get 6 out of 6. The average gain for the two months following October 31 was 3.46% with a median of 3.87%. I don’t know what the comparable percentages are for all years. Two years had declines – 1987 and 2007. So the return is better for the last two months than the last three months. This should not be a surprise. I compared the October lows with the September lows, and found that on average (in the third year), the October low was 2.59% lower than the September low (see the table). October had a lower low in 10 out of 16 years. If you can identify the October low, then the average gain from there to the end of the year was 7.75% with a median of 8.30%. 2007 was the only down year with a loss of -2.15%. Locating the vicinity of the October low is not as stupid as it sounds. The median low date was October 17th. Unfortunately, the 1987 crash was on the 17th, 18th and 19th with the huge losses on the 19th (I remember it well. I was 100% invested and canoeing a river in Missouri.). Eight of the 16 lows were on the 19th or later. Three of the lows were on the second to last or last day. So if you buy at the close on the third to last day, you should be able to beat that average return dated from the end of October. The last two days in October are pretty good on average. I will buy stocks when Financial Select Sector SPDR ETF (NYSEARCA: XLF ) hits a twenty-day high (adjusted for dividend payments). The levels are posted in my Instablog. I actually buy small caps when XLF hits a twenty-day high. I compared the Russell 2000’s performance in the fourth quarter of the third year with the S&P 500 since 1987, and found that on average the S&P did slightly better. The R2000 is more volatile. In strong fourth quarters, it beat the S&P. In weak fourth quarters, it underperformed badly; e.g. 1987. I’m pretty optimistic about the last two months of the year. There is a strong possibility that October will be bad, because of all the negative macro- indicators. Risky high-yield investments like MLPs, mREITs, and junk bonds have been hammered. Sentiment is very negative as indicated by Investors Intelligence, Hulbert’s sentiment measures, Rydex, and Citigroup’s Euphoria/Panic model. I think sentiment follows the market. If October brings further drops in stock prices, then these measures will become even more negative, but that will set us up for a bigger bounce into the end of the year.

Tech Service Stocks On The Rise; New Market Leaders?

Information technology stocks are displaying clear strength through the market correction. The group’s price-weighted IBD subgroup index is trading above its 50- and 200-day moving averages and outperforming the Nasdaq, which has slumped back well below those lines. IBD’s Computer-Tech Services group is home to 13 stocks with 90 or better Composite Ratings (each priced at least 10 a share). Five hold a highest-possible 99 score. Among those with a

Low Volatility Portfolio Optimization Works Where Momentum Strategies Fail

Summary Momentum strategies have worked exceedingly well since 2008. It takes some effort to find a diversified portfolio for which momentum strategies fail. Adaptive asset allocation based on portfolio optimization with high volatility target also fails when momentum strategies fail. Adaptive asset allocation based on portfolio optimization with low volatility target performs well even when momentum strategies fail. Momentum strategies are very popular and are readily available at no cost on the internet. In fact, it takes some effort to find a well diversified portfolio of equities and bonds that would have failed. I used the “dual momentum” and the “relative strength” timing models on the portfoliovisualizer.com site and run a sequence of simulation on some ETF portfolios that included stocks, bonds, real estate and commodities. The portfolio I selected for the study is made up of six ETFs and it performed poorly for the momentum strategy with any look back period. As a benchmark we analyze the performance of the portfolio with equal weight targets, rebalanced when the allocation of any asset deviates by more than 20% from the target weight. That portfolio was subjected to 21 rebalancings within the time interval of the study from January 2007 to September 2015. In this article I compare the momentum strategy with the adaptive allocation strategies I described in many previously published articles. We investigate two versions of the strategy: a return maximization with a low volatility target, and another with a high volatility target. The version with low volatility target was subjected to 105 reallocations of the assets, virtually almost every month. The version with high volatility target was subjected to only 52 reallocations because it was allocated, on average, about two months to the same asset. Here is the list of securities used to build the portfolio: SPDR S&P 500 Trust ETF (NYSEARCA: SPY ) iShares U.S. Real Estate ETF (NYSEARCA: IYR ) SPDR Gold Trust ETF (NYSEARCA: GLD ) T he United States Oil ETF, LP (NYSEARCA: USO ) iShares 1-3 Year Treasury Bond ETF (NYSEARCA: SHY ) iShares 20+ Year Treasury Bond ETF (NYSEARCA: TLT ) The data for the study were downloaded from Yahoo Finance on the Historical Prices menu for SPY, IYR, GLD, USO, SHY and TLT. We use the daily price data adjusted for dividend payments. For the adaptive allocation strategy, the portfolio is managed as dictated by the mean-variance optimization algorithm developed on the Modern Portfolio Theory (Markowitz). The allocation is rebalanced monthly at market closing of the first trading day of the month. The optimization algorithm seeks to maximize the return under a constraint on the portfolio risk determined as the standard deviation of daily returns. In table 1 we list the total return, the compound average growth rate (CAGR%), the maximum drawdown (maxDD%), the annual volatility (VOL%), the Sharpe ratio and the Sortino ratio of the portfolios. Table 1. Performance of the portfolios from January 2007 to September 2015. TotRet% CAGR% maxDD% VOL% Sharpe Sortino Equal Weight 36.95 3.70 -35.85 10.46 0.32 0.42 AA LOW volatility 65.03 5.96 -11.05 6.02 0.99 1.32 AA HIGH volatility -4.73 -0.56 -55.18 23.19 -0.02 -0.03 The data in table 1 should be compared to the results of applying the dual momentum strategy as computed with the portfolio visualizer application. The dual momentum strategy investing monthly in the asset with the highest return over the previous 3 months had total return of -10.34%, with CAGR of -1.25%, maximum drawdown of -40.88% and volatility (St Dev) of 20.48%. There were two periods when the momentum strategy suffered huge losses; first in 2011-12 after gold topped, and the second in 2014-15 when oil prices tanked. The AA high volatility results are very similar to the dual momentum results. Most of the difference in drawdown and volatility is due to the fact that I use daily closing data while the portfolio visualizer site uses monthly data. That explains the slightly larger volatility and drawdown of the AA high volatility compared to the dual momentum. The small difference in the total return is due to a different allocation of the two strategies during a few months in 2011, as will be seen in figure 2. Of the three strategies, the AA with low volatility target performs the best both in return and risk. It produces a steady return of about 6% annually with a low volatility of only 6% and a maximum drawdown of -11%. The performance of the equal weight strategy falls in the middle; it returns on average almost 4% with low volatility of 10%, but still rather large drawdown of -36%. The equal weight strategy suffered steep losses during the 2008-09 bear market. In figures 1a and 1b we show the historical allocation of assets for the adaptive allocation strategy. (click to enlarge) Figure 1a. Historical asset allocation for the low volatility target portfolio. Source: All the charts in this article are based on calculations using the adjusted daily closing share prices of securities. As can be seen in figure 1a, the portfolio was allocated to SHY about 50% over the entire time. It was also allocated about 25% each to SPY and TLT. There were only small allocations to gold, oil and real estate. (click to enlarge) Figure 1b. Historical asset allocation for the high volatility target portfolio. Here one sees that the high volatility target portfolio was allocated alternately to one asset only, the same as in the momentum strategy. Only for a few months in 2009 was the portfolio invested in two assets simultaneously. In figure 2 we show the equity curves of the adaptive allocation portfolios. (click to enlarge) Figure 2. Equity curves for the adaptive allocation (NYSE: AA ) portfolios. We see in figure 2 that the high volatility target portfolio performed well until the fall of 2011. Since then, the equity either went down or oscillated in a range. Recently the equity fell below the initial investment. In figure 3 we show the equity curves of the low volatility and equal weight portfolio. (click to enlarge) Figure 3. Equity curves of the adaptive allocation with low volatility target and the equal weight portfolios. We see in figure 3 that the equal weight portfolio suffered large losses during the 2008-09 financial crises. It performed well between 2009 and 2012, but it fluctuates in a range since 2013. Still, overall, the equal weight portfolio performed better than the adaptive allocation or momentum strategy, as can be seen in figure 4. (click to enlarge) Figure 4. Equity curves of the adaptive allocation with high volatility target and the equal weight portfolios. Source: All the charts in this article are based on calculations using the adjusted daily closing share prices of securities. Conclusion The adaptive allocation by portfolio optimization with low volatility target performs satisfactorily during all market environments. Over a long investment horizon, it beats the equal weight as well as the momentum strategies.