Tag Archives: ideas

Testing Asset Allocation Results With Random Market Selection

Skill is a slippery concept in finance, courtesy of the shady influence of chance in asset pricing. It’s also an awkward topic in just about every corner of money management because discussing it in detail invariably raises serious doubts about our ability to engineer investment results that are satisfactory much less stellar. But ignored or not, randomness is a factor and perhaps a far more powerful one than generally assumed. In recent posts I’ve explored several facets of how random market behavior can influence portfolio results. In the first installment on the topic we focused on random rebalancing dates. Then we moved on to the results via randomly changing asset weights in asset allocation. Let’s push this testing a step further and build portfolios by randomly selecting asset classes. As before, I’ll use the same 11-fund portfolio that’s globally diversified across key asset classes with a starting date of Dec. 31, 2003. The benchmark strategy is rebalancing the portfolio at the end of each year back to the initial weights, as defined in the table below. Let’s call this our “reasonable” attempt at building an informed asset allocation strategy. For comparison with the element of chance in market pricing, this time the test consists of randomly selecting combinations of asset classes with equal weighting that rebalances the mix back to equal weights every Dec. 31. Note that there are 11 funds in the table above. To test for randomness I’ll use R’s number-crunching prowess to select 1,000 different asset allocation mixes. For instance, one randomly selected portfolio may hold US stocks, US REITs, and commodities and ignore everything else. Another portfolio may hold everything with the lone exception of US junk bonds. (For those who’re interested in the details, I’m selecting time series data via the sample() command with no replacement.) All random portfolios are created as equal weight strategies (if there’s more than one fund) using a start date of Dec. 31, 2003, with results running through yesterday’s close (Apr. 6). The chart below compares the benchmark portfolio (red line) with 1,000 random portfolios as defined above. As you can see, there’s a wide range of outcomes relative to the benchmark portfolio, which increased from 100 to roughly 211 over the test period–i.e., the portfolio more than doubled. By contrast, the best-performing random portfolio surged to more than 300 while the worst performer collapsed to just under 50. Most of the random portfolios, however, dispensed moderately superior or inferior results relative to the benchmark. Let’s review the same data from another perspective by comparing the ending value of the benchmark portfolio (red line) for the sample period with the distribution of ending values for the 1,000 randomly generated strategies (black line). Note that the median outcome for the random portfolios is also included in the chart below (blue line). This is only a toy example, of course, but the results imply that we should be cautious in assigning skill as a key factor for the results of the benchmark portfolio. Dumb luck seems to have played a role too. But let’s not beat ourselves up too much. We can almost certainly avoid the fate of the worst performer among the random strategies by holding a broad set of asset classes. The probability is quite low that everything will fail at the same time, although the events of 2008 pushed that notion to the limit and left more than a few investors with doubts. In any case, the main takeaway is that randomness in market behavior is a factor, and perhaps a dominant one, when it comes to risk and return in the context of portfolio design. That doesn’t mean we should throw up our hands and assume that we have no control over investment outcomes. Rather, the lesson is that a fair amount of what appears to be skill may be something else. In other words, our wetware has a tendency to be confused by randomness–a confusion that we’re all too often eager to facilitate, perhaps unconsciously. Chance can’t be engineered out of the investing process, at least not entirely, but that’s only a minor issue if we’re prepared to deal with this gremlin. The intelligent response is to understand how randomness can influence risk and return and factor that aspect of market behavior into asset allocation analysis and design. Yes, many are fooled by randomness, but that doesn’t have to be every investor’s fate.

Policy Divergence And Investor Implications

By Mark Harrison, CFA The world’s central banks and treasuries are no longer simply balancing the levers of growth and inflation through a succession of cycles with varying degrees of poise. Karin Kimbrough, a macro-economist at Bank of America Merrill Lynch, explores a world where all the old symmetries of monetary and fiscal policy have evaporated – that era might as well be 100 years ago. Instead, according to Kimbrough, who spoke at the CFA Institute Fixed-Income Managed Conference in Boston, in this new era, central banks are far from scoring top grades. In the United States, the US Federal Reserve’s quantitative easing (QE) trade is beginning to unwind, but QE policies are still underway in other developed economies. There is monetary and fiscal policy divergence, which, together with demographic distinctions among advanced economies, has important implications for interest rates and fixed-income markets. In this question & answer session following Kimbrough’s presentation, concerns are raised about this policy divergence, difficulties controlling inflation, portfolio risk concentrations from investor yield-chasing, and perceived foreign threats. A full version of this presentation is also available in the CFA Institute Conference Proceedings Quarterly . Audience member: What do you think about the concept of good versus bad inflation? Karin Kimbrough: I personally do not ascribe too much value to the good versus bad inflation concept. I think that the good versus bad inflation argument really just reflects where we see growth and demand more tightly. We are making more advances on the consumer side. Growth is looking okay, and services are definitely stronger than manufacturing, so we are seeing more inflation in services. Any sort of price pressure from abroad is just completely disinflationary given the strengthened dollar and the many downward pressures abroad in commodities and import prices. You mention that fiscal policy is not living up to its end of the bargain. What are some policies that you would espouse to help bridge the gap? I am a Keynesian at heart, in the sense that Keynesian is shorthand for correcting deficient demand. I believe that, in the presence of a deep lack of aggregate demand, the government should step in and support it. So, as a Keynesian economist, I would have supported some kind of new deal deploying people who are still unemployed to work on a major infrastructure project. It might be a redo of some of our major highways or getting high-speed internet into more rural areas – some long-term infrastructure investment that would actually pay off in dividends in the long term for the United States in terms of productivity, either through transportation or communication. So, I would have liked to have seen highway bills and infrastructure bills or, as a New Yorker, another tunnel between New Jersey and New York. All of that got delayed because it was deemed too expensive, but I cannot think of a better time to do it than when rates are low and there is a lot of labor to deploy. Yes, it is expensive, but it also puts people back to work. When people are back at work, they are paying taxes, paying their mortgage, and shopping, and businesses make plans and invest. When you grade inflation a D+, you grade it against the Fed’s target of 2%. How do we know 2% is the right number? If 2% is not the right number, what might the right number be, and how would it affect your grade for inflation? I graded it based on the test, and the test was 2%. Should the test be different – say, 1.5%? Maybe. I think of it this way: 2% provides a nice, comfortable margin such that the Fed is not setting a target that is so low that it is constantly flirting with deflation, which is generally a nightmare for central banks. No central bank wants to be constantly resorting to QE and asset purchases. The Fed wants to be able to toggle the pace of our economy using rates, which is hard to do when everything is sitting so close to the zero lower bound. A 1% inflation target would mean that, over the medium term, actual inflation is oscillating at a very low level, which is problematic. The Fed is trying to set inflation expectations that give the central bank ample room to respond without constantly facing a threat of either destabilizing high inflation or managing problematic deflation. No one is quantitatively arguing the Fed get to 2% more robustly than historical behavior. If 2% is just a random number and is not achievable, does it force the Fed to implement policies that might create other risks, such as the systemic risks that come out of an attempt to create something that is not possible? Central banks look at a variety of measures when deciding on policy, and of course, not all measures point in the same direction. For example, the personal consumption expenditure (PCE) index presents a more negative case right now compared with consumer price index (CPI) inflation, which is sitting at 1.7-1.8% – a lot closer to 2%. It depends on how something is measured, and of course, governments and central banks are guilty of occasionally changing their standards. They will give good reasons for changes – for example, they might say, “We think we need to reweight medical costs or housing costs differently” – and they will come up with a different measure of inflation. If 2% is indeed unachievable and we are constantly trying to drive ourselves there, then perhaps we are doing it at the expense of inflating asset price bubbles by keeping rates unusually low. I think about it from a central bank perspective: There are risks of financial instability resulting from inflated asset prices. These risks are worsened when leverage is added into the mix. Right now, I do not think we are at a particularly over-levered position relative to a decade ago. So, the Fed might be willing to tolerate some degree of overvaluation in certain markets, because the leverage does not look like it is there. That said, if leverage were building up, I would be a lot more worried about trying to achieve an unachievable target of 2%. Disclaimer: Please note that the content of this site should not be construed as investment advice, nor do the opinions expressed necessarily reflect the views of CFA Institute.

Low Volatility ETFs Still In Play

Yellen’s dovish comments may be fueling a market rally at the end of one of the most volatile quarters in years, but these haven’t buried low volatility ETFs. The new-found optimism on Yellen’s hint of a ‘cautious’ rate hike trail perked up investors sentiments lately, helping U.S. bourses to score gains for two back-to-back days (as of March 30, 2016) (read: ETF Winners & Losers Following Yellen Comments ). With this, the broader market pared some of the prior losses. Among the top ETFs, investors have now seen the SPDR S&P 500 Trust ETF (NYSEARCA: SPY ) gain about 1.1%, the SPDR Dow Jones Industrial Average ETF (NYSEARCA: DIA ) gain about 1.7% but the PowerShares QQQ Trust ETF (NASDAQ: QQQ ) move down by about 2.2% year to date (as of March 30, 2016). However, the recent gains do not ensure that the market is free from risks. The Fed chair repeatedly pointed to global growth concerns and deflationary fears as downside risks to the interest rate policy. With the oil refusing to stabilize, ‘more oil producers facing delisting ‘ and growth issues still present abroad, a bear market and the consequent volatility may come down the pike anytime. Also, the U.S. earnings picture is still in shambles. Earnings estimates for the first quarter of 2016 are projected to decline 1.4%. Ruling out the impact from the energy sector, the picture looks slightly better at 1.3% decline. Even investors seem to have little faith in the Yellen rally as they are following low-volatility ETFs despite the enthusiasm in the market. The tendency can be validated by the all-time highs hit by the below-mentioned low-volatility ETFs on March 30. U.S. stocks may not be expensive, but they are not cheap either at the current level. There is also widespread fear among investors about how long this rally will hold. In such volatile times, it’s prudent for investors to follow a proper trading strategy which ensures risk-on sentiments along with stability. With that in mind, we highlight four low volatility ETFs, each of which hit all-time highs lately and could be in focus in the days to come. PowerShares S&P 500 ex-Rate Sensitive Low Volatility Portfolio (NYSEARCA: XRLV ) XRLV looks at 100 S&P 500 components that exhibit both low volatility and low interest rate risk. This strategy excludes stocks that perform miserably in a rising rate environment, with a tilt toward financials (23.31%), industrials (23.06%), healthcare (19.23%) and consumer staples (14.52%). The $126.2-million fund charges just 25 basis points a year in fees. However, the product is not a great choice for dividend yield. The fund yields about 1.46% annually and added 1.2% in the last five trading days (as of March 30, 2016). PowerShares S&P 500 Low Volatility Portfolio ETF (NYSEARCA: SPLV ) This $6.62 billion low volatility ETF consists of the 100 stocks from the S&P 500 Index with the lowest realized volatility over the last one year. The fund is heavy on consumer staples (22.6%), financials (21.4%), industrials (16.2%), utilities (14.1%) and healthcare (13.5%). The fund charges 25 bps in fees. SPLV advanced over 1.4% in the last five trading days (as of March 30, 2016) and yields about 2.16% annually. It has a Zacks ETF Rank #2 (Buy) with a Medium risk outlook. iShares MSCI USA Minimum Volatility ETF (NYSEARCA: USMV ) The fund measures the performance of equity securities in the top 85% by market capitalization of U.S. equities that have lower absolute volatility. The fund has garnered an asset base of $11.2 billion. This fund is home to 168 securities in total and assigns double-digit allocation to the financials (20.49%), healthcare (19.22%), information technology (15.23%) and consumer staples (14.94%) sectors. The fund also has an edge over its peers when it comes to expenses as it charges a fee of just 15 basis points annually while it yields about 2.02%. The fund delivered a return of about 1.5% in the last five trading days (as of March 30, 2016). The fund has a Zacks ETF Rank #2 with a Medium risk outlook. PowerShares S&P MidCap Low Volatility ETF (NYSEARCA: XMLV ) This overlooked ETF looks to follow the S&P MidCap 400 Low Volatility Index. The product invests about $316.5 million in assets in 78 stocks. From a sector look, financials takes half of the portfolio followed by about 11.84% of assets invested in materials, 11.59% in industrials and 10.67% in utilities. The portfolio has minimal company-specific concentration risk with no product accounting for more than 1.69%. The product charges about 25 bps in fees. It was up 1.8% in the last five trading days (as of March 30, 2016). Original Post