● The Investor’s Paradox: The Power of Simplicity in a World of Overwhelming Choice
By Brian Portnoy
Summary via publisher, Palgrave Macmillan
Investors are in a jam. A troubled global economy, unpredictable markets, and a bewildering number of investment choices create a dangerous landscape for individual and institutional investors alike. To meet this challenge, most of us rely on a portfolio of fund managers to take risk on our behalves. Here, investment expert Brian Portnoy delivers a powerful framework for choosing the right ones – and avoiding the losers. Portnoy reveals that the right answers are found by confronting our own subconscious biases and behavioral quirks. A paradox we all face is the natural desire for more choice in our lives, yet the more we have, the less satisfied we become – whether we’re at the grocery store, choosing doctors, or flipping through hundreds of TV channels. So, too, with investing, where there are literally tens of thousands of funds from which to choose. Hence “the investor’s paradox”: We crave abundant investment choices to conquer volatile markets, yet with greater flexibility, the more overwhelmed and less empowered we become.

● The Demographic Cliff: How to Survive and Prosper During the Great Deflation of 2014-2019
By Harry S. Dent Jr.
Review via Business Insider
Take one look at Harry Dent’s body of work and you’ll know he likes to make predictions…. What’s ahead now? A “demographic cliff,” according to Dent’s new book: Demographic Cliff: How to Survive and Prosper During the Great Deflation of 2014-2019. “At Dent Research we have a not-so-secret weapon: demographics,” Dent writes. “It is the ultimate indicator that allows you to see around corners, to predict the most fundamental economic trends not just years but decades in advance.” Dent spends the bulk of his book arguing that the demographic story has turned against the U.S. As Boomers retire, it’s not an unfamiliar argument. Dent writes that an aging U.S. will cause deflation that will weaken the economy from 2014-2019.

● In Bed with Wall Street: The Conspiracy Crippling Our Global Economy
By Larry Doyle
Review via Kirkus Review
A former Wall Street insider excoriates the current nonsystem of alleged self-regulation and weak government regulation in the finance industry. After being employed as a mortgage-backed securities trader at Bank of America, Bear Stearns and other large financial firms, Doyle became disillusioned and departed. He now runs his own investment practice and serves as something of a whistle-blower. The problems he discusses are mostly familiar to readers conversant in current American politics: the coziness of legislators and lobbyists; campaign contributions meant to sway thinking and, sometimes, votes; government regulatory agencies, such as the Securities and Exchange Commission, that seem more watchful than they are, as well as so-called self-regulatory groups within the Wall Street community that rarely protect investors from inexcusable financial losses. With great intensity, Doyle focuses on a little-known self-regulator called the Financial Industry Regulatory Authority. His deep digging into the operations of that group qualifies as investigative journalism, and the scandalous details he recounts are impressive.

● Taxifornia: Liberals’ Laboratory to Bankrupt America
By James V. Lacy
Summary via publisher, Post Hill Press
The biggest and most important state in America was once a land of opportunity in a wonderful climate. But times have surely changed. Things have never been worse for California and its citizens. California’s “one-party” domination of the liberal faction of the California Democratic Party and their union and environmental lobby cronies have wrecked havoc on California, and all Americans are losing as a result. In Taxifornia, James V. Lacy identifies and examines the true causes of California’s decline. Californians are victims of the heaviest taxation in all of America, and those high taxes are now steadily destroying the state’s economy.

● Restoring Shared Prosperity: A Policy Agenda from Leading Keynesian Economists
Edited by Thomas I. Palley and Gustav A Horn
Summary via ThomasPalley.com
Edited by Thomas I. Palley and Gustav A. Horn. The economic recovery in the US since the Great Recession has remained sub-par and beset by persistent fear it might weaken again. Even if that is avoided, the most likely outcome is continued weak growth, accompanied by high unemployment and historically high levels of income inequality. In Europe, the recovery from the Great Recession has been even worse, with the euro zone beset by an unresolved euro crisis that has already contributed to a double-dip recession in the region. This book offers an alternative agenda for shared prosperity to that on offer from mainstream economists. The thinking is rooted in the Keynesian analytic tradition, which has been substantially vindicated by events. However, pure Keynesian macroeconomic analysis is supplemented by a focus on the institutions and policy interventions needed for an economy to generate productive full employment with contained income inequality. Such a perspective can be termed “structural Keynesianism”. These are critical times and the public deserves an open debate that does not arbitrarily or ideologically lock out alternative perspectives and policy ideas. The book contains a collection of essays that offer a credible policy program for shared prosperity, rooted in a clear narrative that cuts through the economic confusions that currently bedevil debate.

● The Upside of Down: Why the Rise of the Rest is Good for the West
By Charles Kenny
Review via Publishers Weekly
China is poised to overtake the U.S. as the world’s largest economy within the next 15 years, but, according to former World Bank economist Kenny, there is a silver lining for the U.S. economy. Kenny continues in the optimistic vein of his first book, Getting Better, as he explains why America losing its status as the unchallenged global superpower doesn’t have to mean declining living standards for its citizens. “America is a country made great by the founding principles of broad-based democracy, education, civil rights, and openness embodied in [the Constitution]”—qualities that Americans should be glad to see spread throughout the world. As developing countries grow richer and more educated, global values will converge. Kenny decries the simplistic reasoning inherent in judging nations based solely on their GDP or military: “being biggest and among the richest hasn’t helped the United States stake a global lead on measures of the broader quality of life.”

● The Watchdog That Didn’t Bark: The Financial Crisis and the Disappearance of Investigative Journalism
By Dean Starkman
Review via The Nation Institute
Nothing defined the financial crisis of 2008 as much as the degree to which it took the public — and the press — by surprise. In a world of twenty-four-hour news coverage and financial reporting, how could so many mainstream journalists, covering something so closely, miss something so big while a few, mostly outside the mainstream, got it? More broadly, what did the business press’s failure say about contemporary journalism’s ability to explain looming systemic problems to the public? In this sweeping, incisive study, Dean Starkman answers those questions, exposing the critical shortcomings that softened coverage during the mortgage era and the years leading up to the collapse. The Watchdog That Didn’t Bark travels back to the early twentieth century to find the roots of the problem in business news’s origin as a market messaging service geared toward investors.

If you haven’t been skeptical of the noise factor in month-to-month economic numbers, today’s nonfarm payrolls report from the US Labor Department should change your worldview. Private-sector employment grew by far less than expected: +87,000 in December vs. the previous month and well below November’s hefty 226,000 advance. If we stop there the news looks troubling. But there’s no reason to stop there. In fact, economic common sense strongly suggests that we look beyond today’s discouraging monthly comparison.

As usual on these pages, I recommend watching the year-over-year trend for payrolls, along with numerous other indicators. By this standard, nothing much has changed with today’s release. Private-sector employment increased around 2% (1.96% if go to the second decimal point). That’s slightly below November’s annual 2.09% gain and so it’s fair to say that the data du jour is a touch softer. Maybe that’s a sign that economic growth won’t accelerate this year, as many analysts have been predicting. But in the grand scheme of persuasive data trends, it’s premature to say much beyond the simple fact that the labor market continues to grow at a moderate pace—a pace that continues to remain in a tight range via recent history: roughly 2%, give or take.

Meanwhile, let’s recall that looking at monthly comparisons has been wildly misleading all along. That’s nothing new, and it’s a hazard that applies across the board. This is old news, but the danger of focusing on the latest data point is forever lurking in a world where the crowd’s obsessed with each day’s data releases. Looking at the numbers without proper historical context, however, is akin to driving with your eyes closed. You may get lucky for a time, but any success is on borrowed time.

If you look at the monthly net change in payrolls in the chart above (the red line), you’ll see that the numbers have continually delivered a wide array of bullish and bearish results. By contrast, the annual rate of change in private payrolls (the black line) has been relatively steady. That’s been a sign that the labor market has continued to heal, albeit with some bumps along the way.

We’re all susceptible to reinventing our economic outlook whenever a widely followed economic report delivers a surprise. Sometimes an attitude adjustment is warranted, but that’s a rare event. In fact, it’s almost never productive to rethink our macro projections (assuming that they’re reasonable) merely because one number shocks the crowd. And, yes, the rule applies even for the all-important employment report from the government.

The solution, of course, is to routinely review a broad set of indicators in search of reasonably reliable estimates of how the business cycle is evolving. Payrolls are a critical input, but even this essential number shouldn’t be analyzed in a vacuum. A far better approach is to look at a broadly diversified set of numbers that collectively serve as a proxy for the broad macro trend. The US Economic Profile that’s updated regularly on these pages is one example; the Chicago Fed’s National Activity Index is another.

The good news is that the broad trend for the economy continues to look favorable, much as it has in recent history. The economy still has a lot of catching up to do when it comes to matching the numbers in the pre-2008 world. But so far the predictions that the economy is about to slide into a new recession have been wrong–an oversight that can be traced to ignoring the overall trend via the numbers. What’s more, today’s payrolls data doesn’t change that view, based on the year-over-year trend in private employment growth.

Granted, today’s weak report for job creation in December may be a warning sign. But it could just as easily turn out to be noise. On that note, keep in mind that the ADP Employment Report for December offers a considerably brighter narrative for last month’s labor market news.

One of these data sets is misleading us. All will be clear when the revised numbers are published down the road. Meantime, let’s remember that the key lesson over the last several years with matters of estimating the business cycle has been a tendency in some corners to read too much into one or two numbers. That’s a good way to attract a lot of readers when it comes to writing headlines or getting invited to TV shows, but it’s a deeply flawed way to analyze the economy. The track record on this front speaks for itself.

Yes, we’d all like to know how the economy will fare in, say, six months. But such privileged information is beyond the grasp of mortal minds in real time. The next-best thing is looking to a broad set of data on a regular basis for perspective. The economic outlook is constantly in flux, based on the implied future according to the existing data set. Most of the time, however, the shifting projections are minimal, and for the moment that still applies. When there’s a substantial change, for better or worse, you’ll read about it here. Meantime, beware of the usual pitfalls in the game of trying to squeeze blood out of a statistical stone.

The stock market is up sharply and inflation expectations have been stable in the low-2% range. That’s just what the monetary doctors have been prescribing and the markets have complied. This is the sweet spot that looked unlikely last spring. But a lot can happen when a determined central bank sticks to its plan for quantitative easing. The year ahead, however, will bring a new set of challenges. The first question: How long should the Fed let inflation expectations rise?

Consider how the numbers stack up so far. The S&P 500 has been rising persistently for the past year. Inflation expectations have only recently turned higher, although it’s still early to worry about pricing pressures. For now, higher is still better. Indeed, the Fed’s preferred measure of inflation—core personal consumption expenditures—is rising at a muted 1.1% year-over-year rate through November, or well below the Fed’s 2% target. But the market’s outlook for inflation (the yield spread between nominal 10-year Note and its inflation-indexed counterpart) is increasing again, touching 2.31% in yesterday’s trading. That’s still within the range we’ve seen in recent years and so there’s nothing particularly troubling here. But if the economy is set to accelerate in 2014 (as it seems to be), we may see inflation expectations climb higher in the weeks ahead.

At what point will rising inflation expectations become worrisome? The answer, of course, depends on a number of factors, starting with the state of the economy. Meantime, the recent high for inflation expectations is roughly 2.6%. If and when the market prices the 10-year Treasury market above that level, the calculus for deciding the implications of higher inflation will be ripe for an attitude adjustment.

Jobless claims fell last week, settling at the lowest level since late-November. The news follows yesterday’s encouraging employment report from ADP. Taken together, the data suggest that tomorrow’s December payrolls report from the Labor Department will also offer more support for thinking that economic growth is picking up.

Meantime, new filings for unemployment benefits fell 15,000 last week to a seasonally adjusted 330,000. The drop was enough to pull down the four-week moving average for claims—the first decline since the week through November 30.

A stronger signal that the labor market is healing can be seen in the year-over-year decrease in new claims, which fell nearly 12% last week vs. the year-earlier level. Here too we have the biggest retreat since late-November.

“The labor market is continuing to strengthen as we go into 2014,” says UBS economist Kevin Cummins. “We should continue to see the unemployment rate go lower.” Deciding if that’s a reasonable forecast begins by analyzing tomorrow’s payrolls report. For the moment, the outlook is bullish on this front as well.

Private nonfarm payrolls in the US are projected to increase by 219,000 (seasonally adjusted) in tomorrow’s December update from the Labor Department, according to The Capital Spectator’s average econometric point forecast. The projected gain is moderately above the previously reported increase of 196,000 for November. Meanwhile, The Capital Spectator’s average December projection exceeds a pair of consensus forecasts based on surveys of economists.

Here’s a closer look at the numbers, followed by brief definitions of the methodologies behind The Capital Spectator’s projections:

VAR-6: A vector autoregression model that analyzes six economic time series in context with private payrolls. The six additional series: ISM Manufacturing Index, industrial production, aggregate weekly hours of production and nonsupervisory employees in the private sector, the stock market (S&P 500), spot oil prices, and the Treasury yield spread (10-year less 3-month T-bill). The forecasts are run in R with the “vars” package.

ARIMA: An autoregressive integrated moving average model that analyzes the historical record of private payrolls in R via the “forecast” package.

ES: An exponential smoothing model that analyzes the historical record of private payrolls in R via the “forecast” package.

R-1: A linear regression model that analyzes the historical record of ADP private payrolls in context with the Labor Department’s estimate of US private payrolls. The historical relationship between the variables is applied to the more recently updated ADP data to project the government’s estimate of private payrolls. The computations are run in R.

TRI: A model that’s based on combining forecasts with a technique known as triangular distributions. The forecast combinations include the following projections: Econoday.com’s consensus forecast data and the four predictions generated by the models noted above, i.e., VAR-6, ARIMA, ES, and R-1. The forecasts are run in R with the “triangle” package. For more information about TRI, see this post.

Dynamic Asset Allocation Strategies Based on Unexpected Volatility
Valeriy Zakamulin (University of Agder) | Nov 2013
In this paper we document that at the aggregate stock market level the unexpected volatility is negatively related to expected future returns and positively related to future volatility. We demonstrate how the predictive ability of unexpected volatility can be utilized in dynamic asset allocation strategies that deliver a substantial improvement in risk-adjusted performance as compared to traditional buy-and-hold strategies. In addition, we demonstrate that active strategies based on unexpected volatility outperform the popular active strategy with volatility target mechanism and have the edge over the widely reputed market timing strategy with 10-month simple moving average rule.

The Global Multi-Asset Market Portfolio 1959-2012
Ronald Q. Doeswijk, et al. (Robeco) | Nov 2013
The global multi-asset market portfolio contains important information for strategic asset-allocation purposes. First, it shows the relative value of all asset classes according to the global financial investment community, which one could interpret as a natural benchmark for financial investors. Second, this portfolio may also serve as the starting point for investors who use a framework in the spirit of Black and Litterman (1992), or for investors who follow adaptive asset-allocation policies as advocated by Sharpe (2010). We estimate the invested global market portfolio for the period 1990-2012 by estimating the market capitalization for the eight asset classes: equities, private equity, real estate, high-yield bonds, emerging-market debt, investment-grade credits, government bonds and inflation-linked bonds. For the main asset categories – equities, real estate, non-government bonds and government bonds – we extend the period to 1959-2012. We provide these annual historical estimates in tabular form so that practitioners and academics can easily use these historical data going forward. Next, we compare the asset allocations of institutional global investors to the market portfolio. To our knowledge, we are the first to document the global multi-asset market portfolio at these levels of detail for such a long period of time.

Dynamic Risk Allocation with Carry, Value and Momentum
Boris Gnedenko and Igor Yelnik (ADG Capital Mgt) | Nov 2013
According to recent research, diversification across risk factors (or investment styles) proves to be more efficient than traditional asset class diversification. In this paper, we take the next step and show that it is economically worthwhile to combine risk factors in a dynamic manner, in a process that we call Dynamic Risk Allocation (DRA). Building a DRA portfolio by means of several unconventional heuristics adds robustness and intuition to the whole portfolio construction process. Our main finding is that risk factor allocation largely replaces traditional global equity and bond market premiums as well as allocation to hedge funds (in expected utility maximization sense). Hence we question the economic validity of the alpha-beta separation paradigm that currently prevails in the industry. Adopting existing optimal rebalancing techniques, we show that our results are robust to transaction costs. Our empirical analysis is made for a global portfolio of 3 well-known risk factors: momentum, value and carry. To minimize data mining effects, each risk factor is broadly diversified across 4 global asset classes and taken both in cross-sectional and time series contexts. We test our approach using 38 years of daily historical prices in a broad set of futures contracts and major FX rates.

Investing in Systematic Factor Premiums
Kees C. G. Koedijk (Tilburg University), et al. | Aug 2013
Investments in certain segments of the market realize better returns over longer periods than those in other segments. Leading academic studies from the eighties onwards demonstrate, for instance, that value, momentum, smallcap and low-volatility stocks systematically generate higher risk-adjusted returns.
Investments in these segments or factors are also known as anomalies, as these factors cannot be explained by classic investment theories. However, allocation by institutional investors to strategies that explicitly capitalize on the benefits of these factors is now supported by academic research.
This report takes as its starting point the study by researchers Ang, Goetzmann and Schaefer (2009) for the Norwegian Government Pension Fund, the first such study to explicitly recommend factor investing. This pension fund is one of the largest active investors in the world. When in 2008 ten years’ worth of cumulative outperformance was wiped out, the fund launched an investigation to evaluate the effects of active management. The researchers concluded that the exposure to factor premiums clearly accounted for the fund’s results. Their conclusion is clear: factor investing must be part of the strategic asset allocation of institutional investors. However, this begs further questions:
•what is the added value of factor investing?
•what are these underlying factors?
•how can a pension fund best put together a portfolio?
This report provides answers to these questions. As a follow-up to the study by Ang, Goetzmann and Schaefer, it is very topical, as many institutional investors are investigating the opportunities provided by factor investing.

The pace of job creation in the private sector accelerated last month, according to the ADP National Employment Report. The 238,000 increase (seasonally adjusted) at 2013’s close marks the third monthly rise above 200,000 and the biggest advance in more than a year. The upbeat news implies that Friday’s payrolls release from the US Labor Department for December will also compare favorably with recent history.

“The job market ended 2013 on a high note,” says Mark Zandi, chief economist of Moody’s Analytics, which produces the employment report with ADP. “Job growth meaningfully accelerated and is now over 200,000 per month. Job gains are broad-based across industries, most notably in construction and manufacturing. It appears that businesses are growing more confident and increasing their hiring.”

The next question: Are businesses also less inclined to lay off workers? Tomorrow’s weekly update on initial jobless claims will offer a clue. There’s some mildly bullish movement on this front lately, with new filings for unemployment benefits dropping in each of the past two weekly reports. If tomorrow’s news extends the trend, the case for optimism will strengthen further as we await Friday’s news from Washington.

But let’s not go overboard. Although today’s ADP data is encouraging, monthly estimates are noisy. Take another look at the chart above for the year-over-year comparisons (the red circles). ADP tells us that private payrolls increased 1.9% in December vs. the year-earlier level. That’s roughly in line with the pace we’ve seen in last year’s third quarter. Yes, it’s also a bit faster than the annual rate that prevailed earlier in 2013. But for the moment, it’s fair to say that employment is still growing at a pace that’s only slightly better than we’ve seen lately.

If we’re finally at the stage where growth is set to pick up, we’ll see more evidence in the hard data in the weeks to come. For now, that’s still wishful thinking, albeit with a bit more confidence for assuming that the long-awaited payday has finally arrived.

Morningstar’s Samuel Lee warns “that REITs’ diversification powers are down and so are their expected returns.” Maybe, but it’s premature to dismiss the asset class, particularly as part of a broadly diversified asset allocation plan. Nothing is static in the financial markets and so today’s profile of risk and return is sure to change.

The challenge with REITs is the simple fact that these securities—a liquid proxy for tapping real estate—have had a strong run. Lee sums up this stellar history and considers the investment implications going forward:

A common argument for REITs is a simple appeal to long-run historical returns. From the beginning of 1972, when the FTSE NAREIT US Equity REIT Index data begin, to the end of August 2013, REITs returned 11.9% annualized. U.S. stocks returned about 10.3% annualized over the same period. REITs earned high returns because their yields were high. It bears repeating that almost all of the real return REITs have produced can be attributed to dividends. Price appreciation only kept up with inflation. Something about REITs changed in the early 2000s. My theory is that for most of their existence REITs were a small, illiquid asset class, neglected by the mainstream and known largely to a small set of venturesome investors. The asset class gained mainstream acceptance as the real estate bubble inflated. Even though the bubble eventually popped, REITs were established as a major asset class, easily accessible, and now ensconced in the big, conventional market indexes like the S&P 500. The abundant accessibility and liquidity surrounding REITs seem to have permanently altered their risk-return characteristics.

A simple way to test this is to see how REIT’s comovement to the market has changed over time. I calculated a rolling three-year market beta, controlling for REITs’ exposure to size, value, momentum, and interest-rate risks, to better isolate pure market exposure. The change is striking: REITs went from an average market beta of 0.5 to over 1 in the early 2000s and have stayed there since. Over this period, REITs went from small-cap, deep-value stocks to larger-cap, growthier stocks.

But let’s review some basic numbers for another perspective. It’s no surprise that REITs have had a rough time lately. The shares, after all, are interest-rate sensitive investments. Accordingly, recent history hasn’t been particularly supportive for REITs, thanks to the beginning of the end of the Federal Reserve’s bond-buying program–a change in the monetary weather that has pushed interest rates up recently, albeit gently so far. The benchmark 10-year Treasury yield has climbed to around 3%, up from 1.7% last spring. The headwind with rates has weighed on REITs, with the MSCI REIT Index advancing a sluggish 2.5% in 2013, or far below the nearly 34% total return for stocks (Russell 3000).

But disappointing performance is the foundation for better days ahead when it comes to asset classes. At some point down the road, REIT yields will be higher and the prospective outlook for the securities will look brighter. Lee’s cautious outlook for the sector shouldn’t be dismissed, but let’s not throw the baby out with the bathwater by thinking that today’s analysis is written in stone.

The same applies for correlations. Consider the history of rolling three-year return correlations for REITs and US stocks (S&P 500), based on one-year returns via average monthly prices. As you can see in the chart below, the high correlation period between REITs and stocks of recent years has fallen sharply over the past year. This is a byproduct of the performance divergence between the two asset classes of late. But unless you think that stocks are REITs are now destined to move in lockstep from now on, the diversification potential for the two asset classes is still intriguing.

True, it’s tempting to write off REITs in favor of stocks given last year’s performance history. But the outsized gains for equities won’t last forever—ditto for the comparatively depressed returns for REITs. Risk and return are forever changing for each asset class, and not necessarily on an identical time schedule, as 2013’s performance record reminds.

The bigger problem is that correlations generally, across all asset classes, are likely to rise in the years ahead. The danger for investors, as William Bernstein explains in his recent e-book, is that we’re all “Skating Where the Puck Was.” Ours is a world where tapping into a broad set of formerly obscure asset classes and trading strategies is now easy and inexpensive (think ETFs). The average investor can build and manage multi-asset class portfolios to a degree that was once the exclusive province of institutions. As a result, the low-hanging fruit of low correlations has probably been picked.

The bottom line: risk premiums will be lower, in part because correlations will be higher. As such, you’ll have to work harder (and smarter) to keep portfolio returns from sliding at a given risk level. That’s hardly a reason to abandon asset allocation–doing so may end up making your job that much harder. But it’s a reminder that we’ll have to do better job in managing the mix. Rebalancing, in other words, is destined to become even more important as a factor in the investment solution in the years ahead. Most investors will still need a wide spectrum of asset classes to achieve respectable results, but it’s not going to get any easier to turn water into wine.

In the coming weeks you’ll see a new forecasting methodology rolled into the economic previews that routinely appear on The Capital Spectator (see here, for instance). As a brief introduction, let’s consider a real world example by crunching the numbers for tomorrow’s estimate of US private payrolls from ADP.

The new model is based on combining forecasts with a technique known as triangular distributions, which require only three inputs: minimal, maximal and “most likely” or modal values (HT: Michael Helbraun at Revolution Analytics). Here’s how Wikipedia explains the rationale for using this technique for estimating future values of a time series:

The triangular distribution is typically used as a subjective description of a population for which there is only limited sample data, and especially in cases where the relationship between variables is known but data is scarce (possibly because of the high cost of collection). It is based on a knowledge of the minimum and maximum and an “inspired guess” as to the modal value. For these reasons, the triangle distribution has been called a “lack of knowledge” distribution.

As for combining forecasts, the inspiration flows from a long line of research that tells us that aggregating predictions tends to be more reliable than the individual estimates. This is old news (the formal research on the topic dates to at least 1969 by way of the widely cited Bates and Granger paper), but it’s no less relevant in the 21st century in the perennial job of managing uncertainty. As Allan Timmermann noted in a 2005 study: “Forecast combinations have frequently been found in empirical studies to produce better forecasts on average than methods based on the ex-ante best individual forecasting model.”

Adding triangular distributions (TDs) to the mix offers a bit more control in managing the uncertainty that infects predictions. Because TDs draw on a different set of assumptions and techniques vs. the other models used on these pages, the average forecast is, in theory, slightly more robust in a statistical sense.

The approach here starts with using Econoday.com’s consensus forecast for the data set under scrutiny (in this case tomorrow’s ADP report) as the proxy for the “most likely” value. The minimal and maximal values are represented by the outer extremes of each survey’s forecasts. For today’s ADP projection, the Econoday/TD-based estimate is combined with three additional forecasts via econometric techniques that are standard tools in the economic previews on these pages: an autoregressive integrated moving average (ARIMA) model, an exponential smoothing model, and a vector autoregression model. In each case, the point forecast is used to represent the “most likely” value, with the upper and lower numbers for the 95% confidence intervals representing the minimal and maximal values.

With the data sets in hand, we then run a Monte Carlo simulation on the combined forecasts and generate 1 million data points on each forecast series to estimate a triangular distribution. (The econometric engine here is the “triangle” package that’s run in R.) Finally, we take random samples from each of the four simulated data sets and use the expected value with the highest frequency as our prediction.

The results are show below, with the triangular distribution forecast indicated by “TRI”. Taking the average of all four estimates tells us that tomorrow’s December ADP employment report is projected to show a 208,000 gain for private payrolls over the previous month. That’s a bit less than November’s 215,000 rise, but moderately higher than we’ve seen in recent history. Meanwhile, the Capital Spectator’s average 208,000 projection is in the middle of a trio of consensus forecasts for December via surveys of economists:

VAR-6: A vector autoregression model that analyzes six economic time series in context with the ADP private payroll employment. The six additional series: the ISM Manufacturing Index, industrial production, index of weekly hours worked, US stock market (S&P 500), spot oil prices, and the Treasury yield spread (10 year Note less 3-month T-bill). The forecasts are run in R with the “vars” package.

ARIMA: An autoregressive integrated moving average model that analyzes the historical record of the ADP private payroll employment in R via the “forecast” package.

ES: An exponential smoothing model that analyzes the historical record of the ADP private payroll employment data in R via the “forecast” package.

What are the primary challenges for designing and managing portfolio strategies in the new year? The same ones that bedeviled us in 2013. At the top of the list is a tendency to overlook the portfolio and instead focus on the individual pieces that collectively add up to “the strategy.”

Markowitz long ago told us that “Portfolio Selection” is the holy grail of investing decisions. But you can’t embrace this strategic advice unless you spend time thinking of your various investments as one aggregated fund. That’s harder than it sounds, in part because the world is constantly inviting you to focus on the parts rather than the whole. It’s unsurprising that portfolio perspective is forever in short supply in the wider world of analysis and news. Asset allocation design and management scream out for customized solutions. In other words, you’ll have to do most of the work yourself in the critical cause of analyzing and monitoring your investments from a top-down portfolio perspective.

Where to begin? The first order of business is generating returns for your portfolio. The good news is that there’s no shortage of tools (often at no charge) on the web. Morningstar’s Portfolio Manager is one example (click on the “Portfolio” tab at the top of Morningstar.com). Regularly monitoring how your portfolio ebbs and flows in absolute and relative terms is essential. Every portfolio has a unique risk and return profile. The question, of course, is whether the profile that defines your portfolio is appropriate for you?

Developing confidence that you’re on the right track is a process, and one that takes time and regular monitoring. No wonder that many investors choose to hire a financial advisor. If you’re inclined to do it yourself, you’ll need a plan to stay on the straight and narrow. Portfolio analysis can easily turn into a black hole if you’re not careful. Creating a blur of data in the 21st century is about as difficult as walking, which is why prioritizing your analytical path is so critical.

The foundation is calculating returns, of course. For most investors, simply reviewing how the portfolio has performed can be an eye-opening experience. Assuming your strategy is reasonably diversified, there’s a good chance that the monthly results will be closely correlated with a naïve portfolio of betas, such as the Global Market Index. The priority is deciding how, or if, to adjust your portfolio’s risk and return profile through time. Again, this isn’t a one-time decision; it’s a process, and one that can only be intelligently managed by routinely dissecting the portfolio.

In other words, you need good data on how the portfolio is evolving. Consider a simple 60%/40% US stock/bond portfolio. Let’s say that we created this portfolio with a pair of ETFs (SPY and AGG), with an inception date of Dec. 31, 2003. You might think that’s there’s not much to consider with such a basic investment strategy. In fact, there are multiple perspectives to review in the search of valuable context. For instance, here’s how the rolling 1-year correlations between the two ETFs compare for the past decade, based on 90-day windows for trailing 12-month returns (plotted daily):

If you owned this portfolio, what might the correlation history imply about adjusting the mix? Do high correlations between the assets offer more opportunity for portfolio changes? Or should we emphasize low correlations as the basis for timely adjustments? Is a 90-day look-back window superior to a longer period? Should we analyze return correlations based on one-year returns—or three- or five-year returns?

Before you go off the deep end on any analytical pursuit, keep in mind that the key decisions for any portfolio can be reduced to: 1) choosing the initial asset allocation; and 2) deciding how and when (or if?) to rebalance. Analytics should be designed and implemented with the goal of providing useful information to help us make better choices about these two aspects of portfolio management. Everything else is usually noise.

As challenging as this task is, it’s a lot tougher if you’re easily distracted by the media’s focus on the parts rather than the whole. It’s tempting to think that the financial story du jour is the most important piece of intelligence for managing your investments. But ask yourself some simple but important questions: How much do you know about your portfolio’s risk/return profile? How does it compare with a passive benchmark for a comparable strategy? What does the risk/return profile imply about how, or if, you should change your asset allocation and/or rebalancing strategy?

The more you think about these critical questions (and how to come up with intelligent answers), the more you’re likely to recognize that most of what’s served up by the usual suspects in the media is irrelevant for building and managing investment portfolios that will help us achieve our financial goals with minimal risk. In this respect, the new year promises to be more or less unchanged from 2013.