Tag Archives: james-picerno

One Size Fits All… If It’s Customized

Portfolio design comes in many flavors, but so do investors. Finding a sensible balance is job one in the pursuit of prudent financial advice. Yet for some folks the idea of keeping an open mind for customizing strategy to match an investor’s goals, risk tolerance and other factors reeks of treachery. There can only be one solution for everyone – all else is deceit. Or so some would have you believe. This biased worldview comes up a lot with the discussion of buy and hold, but the one-size-fits-all argument knows no bounds. The danger is that pre-emptively deciding how to manage assets for all investors is the equivalent of diagnosing illness and recommending treatment before meeting with the patient. Sound financial advice requires more nuance, of course, for two primary reasons: the future’s uncertain and the human species is afflicted with behavioral biases. In other words, a given investment strategy can be appropriate – or not – for different individuals. Consider the concept of buy and hold. By some accounts, it’s all you need to know. Stick your money in, say, the stock market and let the magic of time do the heavy lifting. Sensible? Perhaps. But it may be hazardous. The determining factor is the particulars of the investor for whom the advice is dispensed. Buy and hold – perhaps by focusing heavily if not exclusively on stocks via a handful of equity funds – may be eminently appropriate for a 25-year-old with a budding career, a saver’s mentality, and the behavioral discipline to focus on the long-run future. The same solution can be toxic, however for anyone with a time horizon of 10 years or less. Even for someone who’ll be investing for much longer, may run into trouble with buy and hold if he has a tendency to over-react to short-term events. In that case, buy and hold can be wildly inappropriate for an investor without the discipline to look through market crashes and bear markets. Ah, but that’s where a good financial advisor can help by keeping the client on the straight and narrow: Ignore the short-term volatility and stay focused on the long term. Fair enough, but it doesn’t always work. Some investors will bail at exactly the wrong time no matter how much hand-holding they receive. Deciding who’s vulnerable on that score can be tricky, but not impossible. Perhaps, then, a portfolio strategy with less risk – asset allocation – or the capacity to de-risk at times – some form of tactical – is more appropriate for certain individuals. The flip side of this equation is no less relevant. Forcing every client into a tactical asset allocation strategy simply because that’s your specialty (and/or it pays better for the advisor) is also misguided. Higher trading costs, taxable consequences and the inevitability of timing mistakes can and probably will take a bit out of total return over the long haul relative to buy and hold. The “price” of tactical can still be worthwhile for some folks, if the portfolio has a tamer risk profile. The point is that there’s no way to decide what’s appropriate without first understanding the client. Granted, a 25-year-old investor is more likely to benefit from buy and hold vs. a newly retired 65-year-old client. But there are exceptions and it’s essential to identify where those exceptions arise. The good news is that there’s an appropriate strategy for every client. The great strides in financial research and portfolio design capabilities via computers over the last several decades provide the raw material for building and maintaining portfolios that are suitable for any given client. Buy and hold may still be appropriate, but maybe not. The greatest strategy in the world is worthless if a client jump ships mid-way through the process. As such, the goal for managing money on behalf of individuals isn’t about identifying the strategy with the highest expected return or even the strongest risk-adjusted performance. Rather, the objective is to build a portfolio that’s likely to work for the client. That may or may not lead to a buy-and-hold strategy – or some variation thereof. Such talk is heresy in some corners. But matching portfolio design and management particulars to each client’s time horizon, goals, etc. – and behavioral traits – is the worst way to manage money… except when compared with the alternatives.

Tactical Models Under Pressure As U.S. Stocks Rebound

The US stock market may be on the verge of decisively throwing off its bear-market shackles and making fools of analysts (including yours truly) who’ve been issuing cautious commentary in recent months. It’s also been clear for more than a month that a previously issued markets-based warning on US business-cycle risk has been wrong, at least so far. As yesterday’s broad-minded review of economic indicators relates, the US economy wasn’t in recession in March, based on data published to date. In the wake of the equity market’s rally in recent weeks, the call that stocks were at risk of a bear market may be about to fade too. So it goes in the dark art/science of trying to outwit Mr. Market and look for signals in the noise. The risk of being wrong is an occupational hazard for anyone who practices investing with something other than a buy-and-hold strategy. To be fair, every model that attempts to engineer higher returns, lower risk, or some combination is subject to failure at times. It’s the nature of the beast – no one can outfox the crowd all of the time. Even if you’re right 70% of the time, being wrong in real time outweighs previous successes on an emotional level. The question now is whether we’re on the cusp of one of those times when model failure is about to spill out across the market landscape from a broad US equity perspective? The S&P 500 ticked higher again yesterday, posting another new year-to-date peak. Measured from the previous trough in early February, the index has climbed roughly 15%. In just over a month’s time, the mood has shifted from deep pessimism to exuberance. In the days ahead, analysts and investors will be under pressure to decide if the current exuberance is irrational or warranted. Click to enlarge For some perspective on where we’ve been, recall that the current phase of volatility began last August, when China unveiled a surprise currency devaluation and global markets swooned in response. Bear-market signals from various models followed, including a popular tactical model that seeks to filter out noise by focusing on monthly data-current month-end price relative to a 10-month moving average-for monitoring market trends ( “A Quantitative Approach to Tactical Asset Allocation” ). But this model, like so many others, has been whipsawed in recent months via the monthly readings for the S&P. In the current climate, the bear-market signals have recently given way to bull readings… again. (For charts tracking various ETFs in context with this model’s signals, see Meb Faber’s updates here. ) The Capital Spectator is fair game for criticism as well in the wake of recent market volatility. For instance, an econometric application based on a Hidden Markov model that’s been discussed on these pages continues to signal that the US stock market remains in a bear market. This model has been consistently profiling a negative regime for equities since last fall, but that won’t mean much if it turns out to be wrong. For investors who favor a buy-and-hold strategy, a hefty dose of vindication may be near. If you want to know why most efforts to generate superior risk-adjusted returns through time via various flavors of tactical asset allocation usually come to naught, recent action in the US equity market offers a real-time education. But let’s not put a fork in the tactical models just yet. Even if the past six months have been an extended head fake for bearish signals, there’s still prudent reasons for embracing some degree of tactical models. Expecting superior results at all times, alas, is expecting too much. But that’s a subject for another day. Meantime, back on the front lines of market action, the S&P has retraced all its losses-twice-since last August’s slide. In addition, the case for seeing an imminent recession for the US is still MIA, based on numbers in hand. But there have been worrisome signs of macro weakness in some corners of the economy-last week’s March numbers for industrial production and retail sales are the latest examples. Meantime, next week’s first-quarter GDP report is expected to deliver a tepid 0.3% rise, based on the Atlanta Fed’s Apr. 19 nowcast. Are these reports the raw material for a resumption of the bull market that was rudely interrupted last August? We’ll have the answer shortly, perhaps within a few weeks. If the US stock market runs decisively higher from current levels, the sound you hear of crashing will be disgruntled tactical asset allocators throwing their models out the window. But that’s not yet fate. There’s a severe round of comeuppance lurking around the next bend. The only mystery is where the axe will fall. Some of us think we already know the answer, but perhaps it’s time to roll out Robert Goldman’s famous phrase: “Nobody knows anything.” Actually, let’s rephrase that for use with market analytics: Nobody knows anything… in real time.

Estimating Return-Shortfall Risk For Portfolios

Failure isn’t an option, but it happens. Modeling the possibility that a portfolio strategy will stumble isn’t exactly cheery work, but it’s a productive and necessary exercise for stress testing what the future can do to the best-laid plans for investing. The good news is that there’s a rainbow of options for estimating the potential for trouble. But it’s usually best to start with a basic framework before venturing into more exotic realms. A solid way to begin is by calculating the probability that a portfolio’s return will fall short of a particular benchmark or return. Larry Swedroe, Director of Research for the BAM Alliance, last month wrote about the probability of underperformance from the perspective of four factor premiums. The technique is to assume a normal distribution of returns and model the outcome under a variety of scenarios. Normal distributions are problematic, of course, due to fat-tail risk. But as Swedroe correctly points out, a normal distribution is “reasonable for multi-annual returns data because annual returns data is approximately normally distributed for diversified portfolio.” The details for the number crunching are straightforward. Several years ago The Calculating Investor outlined the procedure with an Excel spreadsheet. Let’s expand the concept a bit by applying the normal distribution function in R via the pnorm() command. Assume we’ve designed a portfolio with a 10-year time horizon and expected annualized volatility (standard deviation) of 15%. Holding those variables constant, here’s the probability of generating a below-zero return over that span based on a range of expected returns for the portfolio: Not surprisingly, the risk of suffering a negative result is substantial if we’re assuming a low return. A 1% annualized return carries a 40%-plus risk a sub-zero performance over a 10-year stretch. But as expected return rises, the risk of below-zero performance falls. As the portfolio’s projected return approaches 10%, the risk of losing money fades to a virtually nil possibility, given the assumptions about volatility and time horizon. For another perspective, let’s vary the time horizon while holding the expected return and volatility constant by assuming the portfolio will earn 5% annualized with 15% standard deviation. As the next chart below shows, running the numbers through a normal distribution model tells us that the risk of sub-zero performance is considerable at short time horizons. Starting at around 15 years, shortfall-return risk falls below a 10% probability. In other words, the longer the time horizon, the lower the probability of losing money. Finally, let’s model various levels of expected volatility while holding constant the time horizon (10 years) and projected return (5%). The third chart below quantifies what intuition implies: higher portfolio volatility increases the probability of suffering a loss. There are many variations on the simple examples above. For example, we can easily model the risk of falling short of the risk-free rate, an inflation-adjusted benchmark, or any other yardstick that’s considered relevant. We can also crunch the data by factoring in a fat-tails assumption for added reality. Ultimately, the goal is to design a modeling framework that’s customized for a specific portfolio. The point is that a basic quantitative application is useful for deciding how a given portfolio might fare under extreme conditions. For instance, the procedure outlined above may reveal that a given set of assumptions is highly sensitive to small changes – a sensitivity that may not be obvious without a formal modeling effort. In that case, it may be time to go back to the drawing board for designing an asset allocation. After all, the price tag is always lower for discovering problems in the design stage as opposed to finding enlightenment when real money is at stake. The future’s still uncertain, of course, but the first priority for the art/science of risk modeling is about minimizing the potential for surprises. Our capacity for insight is limited and so deploying diagnostic tests about what could happen fall well short of providing definitive clarity for the morrow. Estimating shortfall risk is no panacea, but it’s still useful. In fact, the only thing that’s worse than running this modeling procedure is not doing it at all.