Tag Archives: radio

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.

Last Week Showed Us What A Black Swan Panic Looks Like

This past week had the largest recorded percentage loss for the averages, for the first trading week of any year, going back to when George Washington was President. I have been warning about the markets being overvalued for sometime now and preparing for it for years. What triggered this sell-off was actually a Black Swan event (A Black Swan event is an event in human history that was unprecedented and unexpected at the point in time that it occurred), where North Korea supposedly exploded a Hydrogen Bomb. A Hydrogen Bomb is multiple times more powerful than the bombs that were dropped on Hiroshima and Nagasaki at the end of World War II, so North Korea has definitely gotten the world’s attention. What will happen next is anyone’s guess, but it’s not looking good based on investors’ reaction. This past Friday, we had a strong jobs report that should have made the markets go up, but instead of buying the dips, investors were selling the rips. Remember that we operate in a world and stock market where we cannot control events or how millions of investors will trade on any given day, but when the panic button was hit this week, our 79% cash position is what saved us. Why 79% in cash? Well simply because our Friedrich Algorithm is not finding much for us to buy these days. To demonstrate what I mean here are the real-time results of the Dow Jones 30 Index vs. what an investor would have seen in 2010 using the same stocks. Green means that each stock’s Wall Street Price is selling below its Main Street Price and Red means just the opposite or that the stock is overvalued according to Friedrich. Click to enlarge So with just using simple common sense and logic, one sees that in 2010 most of the list, according to Friedrich , was a bargain and in 2016, stocks in the Dow Jones Index above are way overvalued. Therefore, when most stocks in the Dow Jones 30 Index are overvalued, it does not take much for them to go down, but a Black Swan event like North Korea claiming to having set off a hydrogen bomb really panicked investors. This folks is what a Black Swan Panic looks like. (Percentage loss from December 31, 2015 close) Click to enlarge What happens next is anyone’s guess, but it seems that if one wants to practice “Capital Appreciation through Capital Preservation,” they better get busy and go through their portfolios with a fine tooth comb.

The Challenges And Pitfalls Of Measuring Factor Exposures

Factor-based investing has grown significantly in the years since Eugene Fama and Kenneth French first published (1992) their groundbreaking research on the “three-factor model” to explain the return of stocks. Now, a growing number of investors view their portfolios as “collections of various risk-factor exposures,” including risks to particular asset classes and specific “styles,” such as value, size and momentum. Investors reasonably expect to be rewarded for taking on these various types of risk. Understanding the source of returns has also made it difficult for investment managers to pass off factor-based returns as “alpha” – i.e., something that they (the manager) should be paid for having produced. But in order for investors to be sure they’re not overpaying for factor-based returns falsely portrayed as alpha, they must first be able to measure their exposures to the various risk factors – and this is trickier than one might expect. In a recent white paper from AQR , Ronen Israel and Adrienne Ross consider the challenges associated with measuring factor exposures. The authors draw a distinction between academic and practitioner models, favoring the latter for being more practical to implement. Factor Analysis When conducting factor analysis, investors should ask themselves two questions: Exactly what factors am I using? Are they the same as those I’m getting in my portfolio? The answers to those questions can significantly affect alpha and beta estimates. Factor design is also important and can lead to major discrepancies, too. When comparing alphas and betas across managers, investors should make sure they’re using factors being captured by both portfolios – otherwise, they risk overpaying for inappropriately attributed alpha. For portfolios with more than one risk factor, multivariate statistical models are most appropriate. Mr. Israel and Ms. Ross caution investors to consider t-stats – measurements of statistical significance – and not just betas, especially when comparing portfolios with different volatilities. Decomposing Returns Mr. Israel and Ms. Ross examine a hypothetical long-only stock portfolio designed to capture returns from value, momentum, and size style premia . The portfolio was designed with a 50/50 weight on value (book-to-price) and momentum (12-month trailing returns), entirely within the small-cap universe. From January 1980 through December 2014, the hypothetical portfolio would have returned an annualized 13.8% above the return on cash. Mr. Israel and Ms. Ross start with one factor – equity market risk – and build from there. First, a value factor is added (“HML”), and then momentum (“UMD”) and finally size (“SMB”). The HML, UMD, and SMB abbreviations refer to “common academic” definitions: HML (high-minus low) – Long/short value methodology; long high-value stocks/short low-value stocks; UMD (up minus down) – Long/short momentum methodology; long the stocks up the most/short the stocks down the most; and SMB (small minus big) – Long/short “size” strategy; long small stocks/short big stocks. As you can see, when only considering a single factor (“the market”) in Model 1, it appeared that the portfolio generated nearly half of its returns from manager alpha. But as more factors are accounted for, it became clear that alpha-generation was actually much smaller. As an investor, you shouldn’t have to pay active-manager fees for factor exposures presented as alpha.