A New Gilded Age

The period of the late 19th century, following the end of the civil war and before the arrival of WWI is now regarded as the Gilded Age, a period in American history where fortunes of previously unheard of levels were now being made by the likes of John Rockefeller in oil, Andrew Carnegie in steel and the railroad "robber barons" Vanderbilt and Stanford, as they became known.  The Gilded Age, its growing industry and unprecedented wealth soon attracted numerous European immigrants, with the greatest wave of these reaching American shores by the early 20th century.  Amidst all these riches, however, Wikipedia cites the Gilded Age to be one of equally, or perhaps unequally, great poverty in America with the average income of most families below $380 per year.

Social scientists and economists are now drawing parallels of the vast and growing income inequality of America today to that of the late 19th century.  Some have called it the second Gilded Age.  Without question income and wealth inequality is on the rise.  This fact was most recently pointed out in a number of articles that site the fact that just under 20% of the total income in America in 2012 went to the top 1%.  This is the highest share since 1928.  The top 10% earned a whopping 48% of total earnings last year. Perhaps more staggering, since the beginning of the economic recovery in 2009, 95% of income gains have gone to the top 1% of the population.  

At the same time, median household income has fallen for the fifth straight year for a cumulative loss of 8.7% (versus 2007).  Median family income is now the lowest, on an inflation adjusted basis, since 1995.   

Some have blamed Obama, some the culmination of prior administration policies.  Truth be known, Congress has very little direct control over income (other than by confiscating it through taxes) and the White House, even less.  Nonetheless, the evidence of inequality is irrefutable.  So if not administrative policy, what has changed over the past five years that might contribute to such growing income inequality? Monetary policy.

Since the onset of the Great Recession, the Fed has relied on two tools of monetary policy.  Short term interest rates, anchored by the Fed Funds Rate and Discount Rate, were dropped abruptly by the Fed in 2008.  Next Chairman Bernanke deployed a series of unconventional policy tools, known as QE or Quantitative Easing, the purchase of US Treasury and Mortgage Securities by the Federal Reserve.

Today, the Fed's balance sheet has swelled to $3.5 trillion of these securities, with new purchases added at the rate of $85 billion per month.  The recent talk of "tapering" is designed to gauge the level of reduction in the rate of monthly purchases, a phenomena the capital markets regard as tightening of policy.

Aside from the dubious benefits or market-related effects of QE, several things are clear as it regards our topic of income inequality.  First, the Fed's zero discount rate policy or ZIRP has simultaneously dropped the cost of funds of banks on deposits to zero (or near zero) while also lowering the rate of return to investors on bank deposits to this same rate. Economists have called this "greatest transfer of wealth from savers to the banking system in US history".  The banks have been substantially recapitalized through this process, at the expense of savers.  

Bank stocks have rallied, with the Fed shoveling cheap cash their way, which the banks have been all to eager to invest in the rising stock market.  It then should come as no surprise to anyone that the dominant US banking capital of New York has the worst inequality (or highest GINI coefficient) of any state in the country.  The top 1% of New Yokers earn 35% of total income.  The bottom 50%, just 9%.

Second, because the rate of interest on savings deposits, short term US treasury bonds and other fixed income securities, has been less than the rate of inflation, economists have referred to this negative real interest rate phenomenon as "financial repression".  Financial repression favors the issuers of debt, like the US government, and punishes investors in that debt who see their rate of interest turn negative when adjusted for inflation.

Lastly, QE was designed in part to lower mortgage rates in the economy, but also to promote a wealth effect by boosting the value of financial assets, principally stocks.  Given that stocks are disproportionately held by the wealthy, however, these capital gains have been disproportionately owned by the wealthy.  Now, also by surprise, we find growing income inequality among this very same group.

So let's recap what Fed policy has promoted over the past five years.  First, it has favored banks through low interest rates, at the expense of savers.  Second, it has favored the wealthy, as disproportionately large owners of stocks, from those of lesser means.  Lastly, it has punished retirees, young families, non-profit organizations and others seeking short term savings investments through low rates of return.  While we grouse about growing income inequality, the fact remains that the Fed today, more than that of the current or past administration is the party most directly responsible for this outcome.

Earnings, Multiples and the Stuff We Overlook at Market Tops

The new record highs for the S&P and DJIA indices has spurred a great deal of discussion about what's driving the market to new highs.  The doubters look to the Fed and the Bernanke Put as setting a floor under equity prices.  The bulls look to relentless corporate earnings and fattening balance sheets to justify valuations.

Thus as it's always been, a battle of bulls and bears providing equilibrium to the markets.  With the macro data once again turning negative (for the fourth straight spring cycle), Asia weakening and Europe part in recession and part in full-scale depression, we are compelled to dive deeper into the puzzle of US corporate earnings for answers.  After all, when the US economy stalled in 2010 as stocks steadily advanced, the bulls educated us all on the percentage of US multi-national sales that came from a then healthy EU and a roaring Germany.  When these markets faltered, they redirected us to Asia.  These same voices are now advising we look inward to corporate balance sheets.

And, no doubt, balance sheets are in great shape (at least for large companies) thanks to the Fed greatly reducing refinancing costs and boosting stock prices.  But balance sheets bolster corporate solvency and credit-worthiness not earnings.  It's peculiar testimony to the uncertain operating environment corporations face that their highest and best use for cash is to recycle it to shareholders. The accelerated use of share repurchases may be driving stock prices higher, but shrinking the balance sheet is not a formula for organic growth in any textbook we've seen. 

Earnings, however, are different.  They are clearly a sign of growth, purpose and value, even if you overlook the lack of top line revenue from which they derive.  But earnings growth is also slowing and valuation metrics are increasingly stretched.

Goldman's Chief Equity Strategist David Kostin today released an uber-bullish call on US equities.  The essence of his model is that a forecast 2013 earnings growth rate of 11.3%, accompanied by further multiple expansion, must lead to higher share prices.  Well, that's simply arithmetic, isn't it?  Assuming you accept the assumptions.

Kostin cites the current market p/e multiple as a forward multiple of foretasted earnings, as is increasingly the common practice, rather than price over trailing twelve month earnings (TTM) as until fairly recently was accepted market convention.  This is undeniably what tends to happen at market tops when valuations get stretched:  people create new and improved metrics.  Those of us who lived through the tech boom/bust of the late 1990s, early 2000s will recall how during the boom cycle p/e ratios were upgraded to p/e/g ratios, arguing that p/e ratios must be adjusted by the growth rate of earnings to properly value modern high tech companies against stodgy old makers of laundry soap and toothpaste.  No one seemed to mention p/e/g ratios beyond 2001.

Kostin references the current S&P market p/e as 13.3 on its way to 15 by 2013 and 16 by 2015.  Hmm.  Well, these are first and foremost forwardly looking p/e ratios, because the TTM on the S&P as of today is a hearty 19.2 (for the Russell 2000, by the way, it's a juicy 35.66).  But this phenomena of overstating future earnings, only to downwardly revise throughout the year, has been the pattern of the last two years.  So now Kostin advises that we approach 2013 with a forecast for earnings that may be 10% too high (relative to what we may actually realize) and then slap a greater multiple on those earnings that will undoubtedly bring the p/e TTM measure into nose bleed territory.

Add to this the practice of borrowing to fund share repurchases and corporations easily meet higher EPS targets not through growth in operating earnings, but through a lower share base.  As the p/e is commonly calculated by simply taking the price per share and dividing by earnings per share, an inflated EPS (as a result of share repurchase) will artificially lower the p/e.  These are the kind of things we overlook when trying to convince ourselves that we're operating within reason at market tops.

X-12-ARIMA

We've written several posts on the subject of the seasonal adjustment of data from Commerce, BLS and US Census departments.  The beast that crunches the seasonal adjustment modelling for the Census Department is simply known as "X-12-ARIMA".  The raw (unadjusted) data is pumped in and X-12-ARIMA takes care of everything else.

Here's a look at how powerful the seasonal adjustment factors (SA) are with respect to time series data for the unemployment rate.  The blue line is the reported seasonally adjusted data.  The red line is unadjusted. You can see how lumpy the unadjusted data is, lending credence to the argument that the data should be reported SA, in order present a more cogent picture of employment trends, through seasonal periods.

While the smoothing perhaps presents a clearer picture of trend, at the same time the SA data does not reflect at any point in time the true unemployment rate (unless the two curves periodically intersect).   For instance, for January 2013 when the unemployment rate was reported as 7.6%, the actual rate (number of persons reported unemployed/labor force) was approximately 8.5%.  No big deal.  The two trends are moving lower.  But what about other time series data?

The retail sales data on an unadjusted basis shows a similar lumpy pattern (red line).  The pattern here is again smoothed by the seasonal adjustment factors.  But with retail sales, the distortions caused by adjusting data may be more meaningful.  We all know retail sales climb during the holiday season at year end. We also know that sales tend to fall off in January once the bills arrive.

But what is interesting are the portions of the graph below the SA line and above the unadjusted line.  What these gaps represent are periods where the seasonal adjustment overstates true retail sales activity in the economy.  

While the SA factors tend to mute the explosive growth in retail sales during the holiday period at year end, these factors then consistently overstate economic growth during periods of greatest sales declines.  This is equally true with employment, which also peaks in Q4 and troughs in Q2.

As can be seen from the graphs above, the most significant impact of both of these adjustments occurs during the first half of each year (Q1 for retail sales and Q1-Q2 for employment).  Since financial markets trade off the headline numbers for both which, again, overstate activity during Q1-Q2 of each year, this consideration might explain the pattern of the stellar performance of stocks during the first quarter and the consistent "spring swoon" that we've seen for each of the past three years.