Tag Archives: management

Mean-Variance-Optimization Applied To Portfolios Using QQQ During Bear Markets

Summary Portfolios using QQQ and bond mutual funds achieved high returns with low risk from 1999 to 2015. The parameters of the mean-variance optimization (MVO) algorithm can be easily adapted to the risk tolerance of the investors. MVO strategy is very robust, and it may continue to perform well in the future. The idea of writing this article came from a comment by Varan, a frequent contributor on Seeking Alpha. Varan suggested that I investigate the performance of a portfolio using the PowerShares QQQ Trust ETF (NASDAQ: QQQ ) during the 2000 to 2003 period. Since two funds in the portfolio, the iShares 1-3 Year Treasury Bond ETF (NYSEARCA: SHY ) and the iShares 20+ Year Treasury Bond ETF (NYSEARCA: TLT ), were created in July 2002, Varan suggested that I use two mutual funds with similar holdings, the Vanguard Long Term Treasury Fund (MUTF: VUSTX ) and the Fidelity Limited Term Government Fund (MUTF: FFXSX ). In the articles on the simple ETF portfolio the simulations did not cover the 2000-03 bear market when QQQ had a maximum drawdown of -82.96%. We frequently hear investors saying that tactical asset allocation using bond funds will not work anymore because everybody expects a secular bond bear market. So, it is relevant to ask how tactical asset allocation worked using an asset that suffered a severe bear market. In that respect, QQQ is a prime example, having suffered such deep and prolonged losses during the 2000-03 bear market. It has taken twelve years for QQQ to recover and reach the level it had at its top in March 2000. The new portfolio is made up of the following four assets: SPDR S&P MidCap 400 ETF (NYSEARCA: MDY ) PowerShares QQQ Trust ETF Vanguard Long Term Treasury Fund Fidelity Limited Term Government Fund Basic information about the funds was extracted from Yahoo Finance and marketwatch.com and it is shown in table 1. Table 1. Symbol Inception Date Net Assets Yield% Category MDY 5/04/1995 14.23B 1.41% Mid-Cap Blend QQQ 3/03/1999 36.93B 0.96% Large Growth VUSTX 5/19/1986 3.27B 2.75% Long Term Treasury Bond FFXSX 11/10/1986 385M 0.68% Short Term Treasury Bond The data for the study were downloaded from Yahoo Finance on the Historical Prices menu for MDY, QQQ, VUSTX, and FFXSX. 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 (MVO) 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. The portfolios are optimized for three levels of risk: LOW, MID and HIGH. The corresponding annual volatility targets are 5%, 10% and 15% respectively. In Table 2 we show the performance of the strategy applied monthly from June 1999 to September 2015. Table 2. Performance of MVO algorithm applied monthly versus 100% in QQQ. TotRet% CAGR% VOL% maxDD% Sharpe Sortino 2015 return LOW risk 268.91 8.32 5.51 -5.51 1.51 2.19 3.60% MID risk 553.49 12.18 10.3 -10.55 1.18 1.68 3.10% HIGH risk 824.31 14.59 15.11 -16.12 0.97 1.36 -0.24% QQQ 124.69 5.08 29.43 -82.96 0.17 0.23 -1.22% In table 2 we see that all MVO portfolios had stellar performance over the 16 years of this study, even though QQQ had a very rocky ride. Also, notice that the realized volatilities of the MVO strategies are well correlated with the maximum drawdown and the realized annual returns. The 2015 returns column reports the results during 2015 to the end of September. It shows that all MVO strategies performed better than QQQ. The LOW and MID risk portfolios achieved a positive return of over 3% while QQQ lost 1.33%. The HIGH risk portfolio lost a minute 0.24%. The equity curves for all portfolios are shown in Figure 1. (click to enlarge) Figure 1. Equity curves of the portfolios with MVO monthly optimization versus QQQ over the whole time interval from June 1999 to September 2015. Source: All charts in this article are based on calculations using the adjusted daily closing share prices of securities. In figure 2 we show the equity curves during a shorter period that includes the 2000-03 bear market, specifically, we show the June 1999 to December 2003 interval. During the first nine months there was a steep increase in QQQ price followed by a three year bear market. We also included a nine month period of recovery. (click to enlarge) Figure 2. Equity curves of the portfolios with MVO monthly optimization versus QQQ over the time interval from June 1999 to December 2003. We see that all MVO portfolios increased at a slow pace during the bear market. The HIGH risk portfolio was basically flat from March 2000 to March 2003, while the LOW and MID risk portfolios achieved small but steady gains. The details of their performance are given in table 3. Table 3. Returns of QQQ and MVO portfolios during the 2000-03 bear market. Time Interval QQQ LOW_Risk MID_Risk HIGH_Risk 6/10/1999-3/28/2000 123.95% 13.32% 23.93% 53.15% 3/29/2000-3/11/2003 -79.79% 26.21% 22.72% 6.65% 3/12/2003-12/31/2003 53.25% 10.59% 17.86% 34.09% In figure 3 we show the equity curves of the MVO portfolio during the 2008-09 bear market. We included nine months of recovery from April to December 2009. (click to enlarge) Figure 3. Equity curves of the portfolios with MVO monthly optimization versus QQQ over the time interval from October 2007 to December 2009. In figure 3 we see that QQQ suffered a large loss from October 2007 to March 2009. During the same interval, the HIGH risk portfolio lost 8.40%, the MID portfolio was flat, and the LOW risk portfolio gained 7.29%. The exact numbers are given in table 4. Table 4. Returns of QQQ and MVO portfolios during the 2008-09 bear market. Time Interval QQQ LOW_Risk MID_Risk HIGH_Risk 10/1/2007-3/09/2009 -50.27 7.29 0.37 -8.40 3/10/2009-12/31/2009 78.72 8.71 19.35 42.54 Finally, in figure 4 we show the equity curves from September 2014 to September 2015. (click to enlarge) Figure 4. Equity curves of the portfolios with MVO monthly optimization versus QQQ over the time interval from September 2014 to September 2015. In figure 4 we see that QQQ as well as all the MVO portfolios were very volatile, but their equity was bound in a narrow range. Still, the LOW and MID risk portfolios outperformed by realizing modest gains. The exact gains and losses are given in table 5. Table 5. Returns of QQQ and MVO portfolios during the latest one year and the first nine months of 2015. Time Interval QQQ LOW_Risk MID_Risk HIGH_Risk 9/30/2014-9/30/2015 3.63 8.18 9.17 3.58 12/31/2014-9/30/2015 -1.22 3.60 3.10 -0.24 To give the reader more insight into how the MVO strategy succeeds in making gains even when an asset of the portfolio suffers extremely large losses, we present in the following three figures the monthly allocations during the period from June 1999 to December 2003. We decided to display the allocations over a short time interval in order to get graphs that are easy to read. (click to enlarge) Figure 5. Monthly allocations of the portfolios LOW risk strategy over the 2000-03 bear market. In figure 5 we see that the LOW risk strategy allocated, on average, over 60% of the money to the short term bond fund. QQQ was not allocated any funds between March 2000 and November 2002. The long term bond fund was allocated substantial funds during the bear market. (click to enlarge) Figure 6. Monthly allocations of the portfolios MID risk strategy over the 2000-03 bear market. The MID risk strategy allocated more funds to the long term bond fund than to the short term during the bear market. Again, QQQ was allocated the smallest amount of funds during the bear market. (click to enlarge) Figure 7. Monthly allocations of the portfolios HIGH risk strategy over the 2000-03 bear market. The HIGH risk portfolio allocated very little money to the short term bonds. During the bear market most money went alternately to the long term bonds and the mid cap MDY. QQQ was still not allocated any significant funds from April 2000 to November 2002. In table 6 we show the October 2015 allocations for all the strategies. Table 6. Current allocations for October 2015. MDY QQQ FFXSX VUSTX LOW risk 0% 0% 70% 30% MID risk 0% 0% 31% 69% HIGH risk 0% 0% 0% 100% Conclusion The Mean-Variance Optimization strategy applied to a well-constructed portfolio of stocks and bonds performs quite satisfactorily during deep bear markets. It also offers a very simple mechanism of adaptation to the risk tolerance of the investors by trading off risk and returns. The illustrations of this article give us confidence that MVO strategy is very robust, and it may continue to perform well in the future. Additional disclosure: The article was written for educational purposes and should not be considered as specific investment advice.

Valuation Dashboard: Industrials – October 2015

Summary 4 key fundamental factors are reported across industries in the GICS Industrial sector. They can be used to assess the valuation status of an industry relative to its historical average. They can also be used as a reference for picking quality stocks at a reasonable value. This article is part of a series giving a valuation dashboard by sector of companies in the S&P 500 index (NYSEARCA: SPY ). The idea is to follow up a certain number of fundamental factors for every sector and to compare them to historical averages. This article is going down at industry level in the GICS classification. It covers Industrials. The choice of the fundamental ratios used in this study has been justified here and here . You can find in this article numbers that may be useful in a top-down approach. There is no analysis of individual stocks. A link to a list of individual stocks to consider is provided at the end. Methodology Four industry factors calculated by portfolio123 are extracted from the database: Price/Earnings (P/E), Price to sales (P/S), Price to free cash flow (P/FCF), Return on Equity (ROE). They are compared with their own historical averages “Avg”. The difference is measured in percentage and named with a prefix “D” before the factor’s name (for example D-P/E for the price/earnings ratio). The industry factors are proprietary data from the platform. The calculation aims at eliminating extreme values and size biases, which is necessary when going out of a large cap universe. These factors are not representative of capital-weighted indices. They are useful as reference values for picking stocks in an industry, much less for ETF investors. Industry valuation table on 10/29/2015 The next table reports the 4 industry factors. For each factor, the next “Avg” column gives its average between January 1999 and October 2015, taken as an arbitrary reference of fair valuation. The next “D-xxx” column is the difference between the historical average and the current value, in percentage. So there are 3 columns relative to P/E, and also 3 for each ratio. P/E Avg D- P/E P/S Avg D- P/S P/FCF Avg D- P/FCF ROE Avg D-ROE Aerospace&Defense 19.64 18.02 -8.99% 1.15 1.02 -12.75% 22.45 21.28 -5.50% 7.31 9 -18.78% Building Products 27.98 20.14 -38.93% 1.3 0.64 -103.13% 37.43 22.38 -67.25% 11.96 6.07 97.03% Construction&Engineering 19.89 18.3 -8.69% 0.4 0.48 16.67% 15.75 19.81 20.49% 3.86 5.98 -35.45% Elec.Equipment 23.15 18.31 -26.43% 1.53 1.64 6.71% 25.31 21.88 -15.68% -7.83 -3.3 -137.27% Ind. Conglomerates 36.65 20.45 -79.22% 2.44 1.3 -87.69% 29.48 29.98 1.67% 2.59 12.12 -78.63% Machinery 17.84 18.25 2.25% 1.04 0.9 -15.56% 25.17 21.81 -15.41% 10.34 8.72 18.58% Trading Companies&Distri 17 17.14 0.82% 0.58 0.7 17.14% 14.47 25 42.12% 8.39 8.61 -2.56% Commercial Services&Supplies 22.99 20.86 -10.21% 1.22 1.03 -18.45% 26.12 19.84 -31.65% 3.9 3.99 -2.26% Professional Services* 21.91 24.04 8.86% 1.5 1.22 -22.95% 19.53 17.43 -12.05% 6.51 3.09 110.68% AirFreight&Logistics 23.45 21.06 -11.35% 0.65 0.57 -14.04% 23.49 32.87 28.54% 12.44 11.12 11.87% Airlines 12.02 15.18 20.82% 0.96 0.41 -134.15% 24.97 12.37 -101.86% 25.32 3 744.00% Marine** 16.02 14.04 -14.10% 1.13 1.41 19.86% N/A N/A N/A -13.74 6.05 -327.11% Road&Rail 16.71 19.17 12.83% 1.15 0.86 -33.72% 28.74 36.17 20.54% 16.39 9.43 73.81% Transport Infrastructure 7.7 23.6 67.37% 1.07 1.19 10.08% 5.48 20.8 73.65% 16.51 -3.22 612.73% *Professional Services: Avg since 2008 **P/FCF currently outlier for Marine Valuation The following charts give an idea of the current status of industries relative to their historical average. In all cases, the higher the better. Price/Earnings: Price/Sales: Price/Free Cash Flow: Quality Relative Momentum The next chart compares the price action of the SPDR Select Sector ETF (NYSEARCA: XLI ) with SPY. (click to enlarge) Conclusion Industrials have underperformed the broad market in the last 6 months. At the industry level, Transport Infrastucture, Road & Rail are the only 2 industries with at least 2 of 3 valuation ratios pointing to underpricing, and a quality level above their respective historical averages. However, there may be quality stocks at a reasonable price in any industry. To check them out, you can compare individual fundamental factors to the industry factors provided in the table. As an example, a list of stocks in Industrials beating their industry factors is provided on this page . If you want to stay informed of my updates on this topic and other articles, click the “Follow” tab at the top of this article. You can choose the “real-time” option if you want to be instantly notified.

6 Weekly Sentiment Charts – Is The Blood Still Running Deep Enough?

Summary Two months ago, my sentiment charts were screaming BUY. I added to many positions. About a month ago, some of my sentiment indicators reached lows not seen in a year or longer. The time to buy stocks is when there is “blood in the streets” when others are fearful and selling. Sentiment has recovered quickly. After making his fortune buying during the panic that after Napoleon’s Battle of Waterloo, 18th century British nobleman and member of the Rothschild banking family, Baron Nathan Rothschild, is often credited for telling his clients that “The time to buy is when there’s blood in the streets.” (See ” When There’s Blood In The Streets “) I’ve explained in past articles such as ” SPY 8% Off Record High While WLI Rises To 6-Week High ” why I like SPY as an investment for the long-term. I use fundamentals to pick individual stocks and SPY for my portfolio, but I seldom buy as they are making new 52-week highs. I try to buy when they are on sale and when the blood is running in the streets. Every week I review my sentiment charts of the weekly data. In this article, I compare the sentiment levels from various surveys in my table to get an idea of overall investor sentiment. (click to enlarge) Note: “Blood Level” of 1 means the data is in the lower 20% of the graph while a reading of 5 is for the data in the upper 20% of graph. To get better prices, I start with my list of “Explore Portfolio” stock picks then wait for market pullbacks and extreme negative sentiment levels to buy if they haven’t quite reached the “low ball” prices I set ahead of time to buy during market panics and other periods of market inefficiency. Said another way, I like to take profits as markets make new highs then buy back shares when my sentiment charts loudly shout at once “Buy” as most investors are afraid and selling. Two months ago when the S&P500 made its low for the year, most of my sentiment indicators were at screaming buy levels not seen since the 21% bear market correction in 2011. While recovering, most of the sentiment indicators I track are still improving and have yet to reach extreme levels. Some, like the ten day moving average of the put to call ratio shown below have fallen enough to suggest we are again due for a market pullback, so I’ve taken profits in my stocks to have funds to buy any major pullbacks. If you have other favorite sentiment indicators you want tracked in my table, then let me know in the comments and I will consider adding them to future articles. What follows are the charts and brief explanations for the measures of sentiment I follow, in no particular order of importance. Chart 1: Put-to-Call Ratio – 10 day moving average chart courtesy of Stockcharts.com (click to enlarge) Chart 2: AAII American Association of Individual Investors Sentiment Survey Numbers posted weekly here on Seeking Alpha From AAII Sentiment Indicator , “The sentiment survey, taken once a week on the AAII website, measures the percentage of individual investors who take the survey who are bullish, neutral and bearish.” (click to enlarge) Chart 3: II: Investor’s Intelligence Survey From Investors’ Intelligence Sentiment Indicator : The “Investors Intelligence Survey” or IIS questions stock-market newsletter writers once a week to see if they were bullish or bearish on the stock markets in the near-term. Newsletter writers have a large following as a group and are thus considered “market experts.” Investor’s Intelligence web site (click to enlarge) Chart 4: Ticker Sense Blogger Sentiment vs. S&P500 From Ticker Sense Blogger Sentiment Poll : “The Ticker Sense Blogger Sentiment Poll is a survey of the web’s most prominent investment bloggers, asking “What is your outlook on the S&P 500 for the next 30 days?” Conducted on a weekly basis, the poll is sent to participants each Thursday, and the results are released on Ticker Sense each Monday. The goal of this poll is to gain a consensus view on the market from the top investment bloggers — a community that continues to grow as a valued source of investment insight. © Copyright 2015 Ticker Sense Blogger Sentiment Poll.” (click to enlarge) Chart 5: NAAIM Exposure Index From NAAIM Exposure Index – National Association of Active Investment Managers, “The NAAIM Exposure Index represents the average exposure to US Equity markets reported by our members.” Screenshot source Chart 6: CNN Money Fear & Greed Index The CNN Money Fear & Greed Index is derived from seven indicators explained here Screenshot source Notes I trade SPY around a core position in my newsletter’s ” Explore Portfolio ” and with my personal account. With dividends reinvested, my explore portfolio holds 137.202 shares of SPY with a “break-even” price of $99.33. I also have the index fund version of SPY in both my newsletter’s “core” portfolios. SPY is the exchange traded fund for the S&P 500 Index. VTI is Vanguard’s “Total Stock Market” exchange traded fund. If you want to invest in a single fund, that is my first choice over SPY. I recommend SPY and several others in my core portfolios for more opportunities to rebalance. VOO is Vanguard’s new exchange traded fund that tracks the S&P 500 Index. It is a lower cost alternative to SPY. I own and write about SPY, as I have many years of data for it, but VOO could do slightly better than SPY over time because it has a lower expense ratio. Disclosure : I am long SPY and own the traditional index fund versions of VTI and VOO bought long ago in various taxable and tax deferred accounts. I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article.