Tag Archives: vfinx

On Stock Crashes, Hindsight, And Anger

“You will not be punished for your anger, you will be punished by your anger.” – Buddha US stocks just had their worst start to a year in history last week going all the way back to 1928. The bears have come roaring back. As investors and traders frantically deal with clients and their own personal emotions, let’s put things into factual perspective. Those that have seen my presentations, met with me or read our writings for years are aware that we quantitatively test indicators and create strategies we believe have merit. Backtesting, like any form of analysis, does not guarantee exceptional performance in the future, but it can at least provide information on anomalies or market patterns which have persisted over time. So here’s a very simple and powerful backtested strategy. The 200 day moving average is popular as an indicator used to buy or sell stocks. Let’s make a simple rule. If a stock fund is trading above its 40 week (200 day) moving average, buy that stock fund. If below, then instead, buy Treasuries as your “risk-off” trade. For the below backtest, I used the Vanguard 500 Index (MUTF: VFINX ) as the stock proxy, and the Vanguard Long-Term Treasury Fund (MUTF: VUSTX ). Want to get more creative? Use the same rule, but leverage up the stock fund VFINX by an extra 30% to juice returns. Yellow uses that leverage, blue is the rotational risk-on/off strategy using Treasuries, orange is VFINX, and gray is VUSTX. VFINX mimics the S&P 500 SPDRs ETF (NYSEARCA: SPY ), while VUSTX mimics the iShares Treasury 20+ Year ETF (NYSEARCA: TLT ). Click to enlarge Looks pretty good right? Most of the time you’re trending higher, though it is worth noting that there are several flat volatile years where those who invest over a 1, 2, 3, or 4-year period are frustrated by a lack of returns and volatility/whipsaws. However, over longer periods of time and full cycles, both the unleveraged rotational strategy between stocks and Treasuries, and the leveraged one not only outperform on an absolute basis in terms of pure performance, but also do so on a risk-adjusted basis. The blue version’s cumulative return is 1,545% going back to 1986, versus the stock fund itself at 1,274%. That’s 1.2x better. The leveraged version which magnifies by 30%? Cumulative return is 2,756%, resulting in 2.16x stronger performance against VFINX as a buy and hold investment. Note that the extra 30% leverage over time significantly magnifies results. Compounding leverage can be a wonderful thing when it works over time if you have a strategy for it. It is worth noting that the 40 week MA’s strength is more about avoiding big declines rather than participating in big upside, though that upside exposure is most conducive towards using leverage. Also worth noting that if you added other momentum areas, notably emerging markets, the return path gets even more extreme. Great! Let’s buy into that. Now instead of the chart above, you’re living performance day to day. Feel good about the rotational strategy? Well, let’s now dig a little deeper. Let’s look at the worst weeks in the strategy. Any of these percentage declines feel familiar? Date Stocks/Bonds Leveraged Stocks/Bonds 4/10/2000 -10.52% -13.67% 10/12/1987 -9.21% -11.97% 9/8/1986 -7.84% -10.19% 12/8/1986 -7.66% -9.96% 8/1/2011 -7.15% -9.30% 10/9/1989 -6.91% -8.98% 10/11/1999 -6.63% -8.62% 5/3/2010 -6.35% -8.26% 8/17/2015 -5.71% -7.43% 1/24/2000 -5.62% -7.30% 10/5/1987 -5.07% -6.59% 8/24/1998 -4.97% -6.46% 7/23/2007 -4.89% -6.35% 1/5/1998 -4.83% -6.28% Herein lies the point of this backtest and any strategy employed. It is completely and utterly impossible to avoid weeks like what happened at the start of 2016 from impacting your portfolio and creating an emotional response. In the rotational strategy outlined here, there are numerous large declines throughout history. In each and every single one of these weeks, the response by investors and traders is the same. “What the hell is going on?” “I can’t invest in this!” “You should have sold out!” “Sell! SELL!!!!!” “Change the strategy!” Nothing can get everything right. Even if you used stop orders or decided to trade out of one of these big declines mid-week, historically it doesn’t do anything to mitigate those losses, even though one believes deeply that it would. The anger that comes from weeks like last week is no different than the emotional response that comes from other worst weeks in history in any strategy. Hindsight creates anger, but that doesn’t mean that anger is right. In the 2014 Dow Award paper on Beta Rotation, (click here to download) for example, we show that 80% of the time Utilities outperform the broader stock market before an extreme VIX spike. That means it also missed it 20% of the time, just like any indicator used to mitigate risk over much longer periods. That in no way shape or form invalidates the indicator. My point here is that sometimes these things happen. Long duration Treasuries did not confirm ahead of time that last week would happen based on indicators we have tested. Following the decline last week, things do look ugly. The decline could get worse, or the market could V, or W, or L, or do anything it wants to do. Hindsight is the only way we will know after such a major break. One cannot control for the madness of sudden markets. All any of us can do is prevent that madness from impacting rational decision making. The year is not written yet. There will come a major up move in reflation trades which would undo in the blink of an eye any damage done to portfolios so far in 2016. In the meantime, don’t let hindsight make you angry, because every strategy will have weeks in the dataset as violent as the one just experienced. That does not mean one should abandon the approach. It is easy to forget one simple rule when it comes to investing passively or using active management. The best time to buy in is after a significant drawdown. After drawdowns is the time to rationally examine data, rather than make rash decisions failing to understand that sometimes, you just can’t avoid bad luck. This writing is for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation regarding any securities transaction, or as an offer to provide advisory or other services by Pension Partners, LLC in any jurisdiction in which such offer, solicitation, purchase or sale would be unlawful under the securities laws of such jurisdiction. The information contained in this writing should not be construed as financial or investment advice on any subject matter. Pension Partners, LLC expressly disclaims all liability in respect to actions taken based on any or all of the information on this writing.

Dynamic Asset Allocation

Identifying the right asset classes and proportions to diversify is difficult for an investor. The scientific methods for diversification, namely Markowitz’s Mean Variance Optimization have not been practically applicable. Investing in all asset classes evenly at all times will reduce risk but lower returns too. A diversification strategy that reduces exposure to asset classes trending down long term has historically outperformed the stock market both in terms of overall return and volatility. Diversification is widely accepted as the most important aspect in building a portfolio. For investors looking to accomplish their long term financial goals, diversification helps reduce risks and volatility as market and economy go through various expansion, contraction cycles. However the specifications on how much to diversify and in what asset classes are often vague and left to the judgment of an individual investor. There aren’t many established or prevalent public tools that would take investor characteristics as an input (for example risk tolerance, time horizon etc.) and output a recommended model portfolio. A recommended portfolio that provides a list of specific asset classes (mutual funds, ETFs or stocks) and propose percentage weights for investor to review and consider as a starting point. Further, the primary goal for diversification is looked at as risk minimization or reduced volatility in your portfolio. That comes at a cost since lower risk leads to lower return. Could diversification lead to lower risk and yet outperform the market in terms of returns? This article proposes a diversification strategy that has historically outperformed the market, with lower drawdowns and can be used by investors to build a long term asset allocation strategy. Background: Let’s start with understanding the history and state of financial theory on diversification. Harry Markowitz’s Mean Variance Optimization (MVO) method developed in 1952 forms the core backbone of financial theory on diversification. The core insight of Markowitz’s work was that by combining assets that are negatively correlated (i.e. they typically move in different directions) one can reduce the overall volatility of a portfolio without impacting the expected return. Markowitz provided a mathematical algorithm that can use this insight to generate the ideal portfolio (named as Markowitz Efficient Portfolio ) with lowest risk/volatility possible. This was a powerful algorithm and Markowitz rightfully won a Nobel Prize in 1990 for it. Unfortunately even though this was a powerful algorithm, it has not turned out to be practically applicable (Reference papers: 1 , 2 , 3 ). It entails complicated mathematics sensitive to minor changes in the input and requires accurate future forecast on potential assets. Historical returns are very poor forecasts. Variations of Markowitz’s algorithm like Black Litterman model have been proposed to overcome these limitations, however even these require sophisticated inputs (like asset market weightings, volatilities and correlations) that may not be easy to provide for by an average investor. Diversification Strategy Options: To build a model that is simple to understand, compute and specific in terms of output recommendations, we start with Markowitz’s key insight: incorporate assets that are negatively correlated in a portfolio. However correlation between two assets can change over time and rather quickly so we don’t want to assume future correlation will be same as past. Instead we incorporate asset classes that have the potential to have negative future correlation. Thus we include assets in the portfolio that are fundamentally or significantly different from each other. To illustrate this with an example, let’s start with Stocks, Gold and Bonds as three available asset classes that are fairly different from each other. Let’s pick a mutual fund or index from each of these to start with diversification in asset class itself and not be exposed to individual stock risk. I picked the Vanguard 500 Index Fund (MUTF: VFINX ), the V anguard Long Term Investment Grade Fund (MUTF: VWESX ) and the Franklin Gold and Precious Metals Fund (MUTF: FKRCX ) to represent stocks, bond and gold in this test portfolio. We could have picked ETFs like the SPDR S&P 500 Trust ETF ( SPY), the SPDR Gold Trust ETF ( GLD) and the i Shares 20+ Year Treasury Bond ETF ( TLT) but those have historical data only since 2002. Using VFINX, VWESX and FKRCX as proxies for stock, bond and gold allowed me to back test on historical data going all the way back to 1985 from Yahoo Finance. The simplest diversification without making any future assumptions on expected returns would be to allocate equal one third percentage to each asset class. How would this constant equally diversified portfolio would have worked as compared to staying 100% invested in stocks? Overall, stocks would have generated better returns but they’d have also seen larger volatility as seen in the higher drawdown in table below. The graph below shows how the two portfolios would have grown and the table shows annualized return and drawdown numbers for the duration. (click to enlarge) (click to enlarge) Looking at the above numbers, a simple strategy of equal breakdown across multiple asset classes provided a good start for reasonable growth and yet lower drawdowns. However, could we have generated better returns than being in stocks alone? We can take advantage of being in an asset class rather than an individual stock. Individual stocks can go through wild up and down swings, but asset classes do show longer bull – bear trend. For example, the graph below shows that “Gold – Precious metal equities” have been a 4 year long bear market since 2011. Similarly U.S. stocks went through 2-3 year bear market in 2000 and 2008. (click to enlarge) One improvement that we can make in our diversification strategy is to exclude any asset class that is in its longer term bear market and equally invest in all other asset classes. An asset class can be marked in bear market if its 52 week return is less than -2%. We could use any other indicator too like simple moving average or 52 week minima drop. They will all work. The important thing is to classify it as a bear and exit or reduce your sizing in that asset class. Any heuristic that improves the accuracy of classifying an asset class is in bear market will improve the strategy further. In our proposed dynamic allocation strategy we simply reduce allocation to zero on an asset class which has lost more than 2% over the last one year. All other assets are held in equal proportions to make up 100% of portfolio and balanced weekly. For simplicity we have assumed balancing weekly has zero costs, in reality transaction costs may necessitate balancing over a longer time period like 1 or 3 months. Back testing this strategy on historical data since 1984 returns an annual return of 11.87% with an average drawdown of 3.73%. The worst case drop from a 52 week high was 31.35%. So an outperformance both in terms of returns as well as lower volatility. (click to enlarge) (click to enlarge) Conclusion: Investors who manage their portfolio on their own, can use the learning above to build their own long term portfolio management strategy. They can extend the above proposed strategy to cover a comprehensive set of asset classes to include all major sectors like real estate, commodities etc. as well as international economies. Including more asset classes should help reduce risk but too many asset classes will decrease the overall return. Investors can try a variations where instead of equal allocation across all asset classes, sectors that are booming have higher weighted allocation versus sectors that are underperforming. Catching a long term bull market in an asset class and over indexing on those asset classes is likely to help improve returns. They can adjust the maximum level of weighting in a single asset class based on their risk tolerance to limit over exposure in a single asset class. Investor can thus build their own diversified portfolio, test its historical performance on returns and drawdown and thus be equipped to make smarter investing decisions for the long term. Disclaimer: The author does not have any holdings in the mutual funds (VFINX, VWESX and FKRCX) used to test described diversification strategy. These funds have been used only for illustrative purpose and the author is not making any recommendations to buy them. We use a proprietary asset allocation technique across global stocks, bonds, commodities, commodities stocks, mutual funds, ETFs and other investment options in our portfolio.

Investment Activity And The Illusion Of Control In Exchange For Low Real Returns

Study after study shows that more investment activity is correlated only with higher fees and lower real, real returns. Activity is the illusion of control in exchange for lower real, real returns. You don’t want to be irrationally long term, which usually results in huge amounts of short-term permanent loss risk. But you also don’t want to be so short term that you take no risk. The best way to reduce taxes and fees in your portfolio is to take a long-term perspective. Again, a multi-year or cyclical time frame blends perfectly with maximizing your real, real returns. I take a cyclical view on things. This means I can sometimes go years without making big changes in my views or portfolio. This is a very intentional construct, and I think it’s one that most people should adhere to. After all, you don’t want to be irrationally long term , which usually results in huge amounts of short-term permanent loss risk. But you also don’t want to be so short term that you take no risk. As we find with so many things in life, moderation is the key. Hence, my cyclical or multi-year perspective on things. Resolving this temporal problem isn’t the only reason for this, though. We know that taxes and fees are two of the most important frictions in a portfolio. And the best way to reduce taxes and fees is to take a long-term perspective. Again, a multi-year or cyclical time frame blends perfectly with maximizing your real, real returns . Of course, this is easier said than done. We live in a world dominated by “What have you done for me lately” narratives. And worse, we are confronted with our own biases that make us feel comfortable when we’re doing something. After all, letting your portfolio float in the wind feels very uncontrolled, and oftentimes, uncomfortable. Activity is the way in which we try to “control” the markets. Of course, you can’t control the decisions of other market participants. And study after study shows that more activity is correlated only with higher fees and lower real, real returns. Yet, the allure of greater control pulls us in. Activity is the illusion of control in exchange for lower real, real returns. Luckily, there is a happy medium here. There is no need to be irrationally long term or short term. But it takes a great amount of discipline to reject the illusion that activity creates control. For most, that illusion (and the sales pitch of “market-beating returns” that often goes with it) is too enticing to reject.