Tag Archives: earnings

2014 Review: Economy Edition (02/04/2015)

U.S. GDP numbers were released last Friday, which means we can now finish 2014 year-in-review series by taking a look at major economic indicators. The employment situation continued to improve in 2014 (Exhibit 1).  The U.S. economy added 3.47 million new jobs (289,000 a month), which was a 60% improvement over 2013.  Total employment increased 2.5% which was quite a bit better than the 0.6% population growth.  The unemployment rate ended the year at 5.4%, which is in the range of what Fed currently considers full employment (see footnote).  U-6 rate is a broader measure defined as “Total unemployed, plus all marginally attached workers plus total employed part time for economic reasons”.

Exhibit 1 – Employment

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GDP growth remained in the 2% range last year (Exhibit 2).  Auto sales jumped 8.9% to over 17 million a year (the highest level since 2005).  Inflation, as measured by Consumer Price Index, actually decreased and came in at 0.8%.  This level is well below the Fed’s 2% target and something to keep an eye on as everyone is trying to figure out when it will start raising rates.

Exhibit 2 – Growth & Inflation

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Public debt increased to $17.8 trillion during the year (Exhibit 3).  On the positive side, a combination of tax hikes and spending cuts led to a big drop in budget deficit.  It declined by 30% to $483 billion or (only) 2.8% of GDP.  Despite the “Taper” , Federal Reserve still expanded its balance sheet to $4.2 trillion.  Average monthly increase was about $40 billion (down from $90 billion in 2013).  It should actually decline this year as securities mature and there are no fresh purchases. We’ll do a separate post analyzing Fed balance sheet in more details.

Exhibit 3 – Debt & Deficit

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Note: Public debt figure as of Q3:2014

S&P 500 earnings growth decelerated to 7.9% (Exhibit 4).  P/E multiple expanded 3.3% to 17.8x.  10-Year Treasury rate declined to 2.2%, which was totally unexpected as most market pundits predicted a big jump in rates for 2014. 30-Year Fixed Mortgage rates also declined ending the year just under 4%.

Exhibit 4 – Earnings & Rates

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Note: Q4:2014 S&P 500 earnings are consensus estimates as of Jan 22, 2015

Finally, the housing sector continued improving (Exhibit 5).  Both units and prices increased again in 2014, albeit at a much slower pace than in recent years.

Exhibit 5 – Housing

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Note: S&P Case-Shiller 20-City Home Price Index as of Nov 2014

Footnote

Federal Reserve currently considers full employment to be in the 5.2% – 5.5% range.

Committee participants’ estimates of the longer-run normal rate of unemployment had a central tendency of 5.2  to 5.5 percent.

http://www.federalreserve.gov/faqs/money_12848.htm

More traditionally “Full Employment” in the U.S. means 4%.

The United States is, as a statutory matter, committed to full employment (defined as 3% unemployment for persons aged 20 and older, 4% for persons aged 16 and over); the government is empowered to effect this goal. The relevant legislation is the Employment Act (1946), initially the “Full Employment Act,” later amended in the Full Employment and Balanced Growth Act (1978).

http://en.wikipedia.org/wiki/Full_employment

Earnings & Returns: A Longer Look (03/31/14)

In the last post I explained how stock returns can be explained by earnings growth and changes in P/E multiples. Josh Brown had a similar post on his Reformed Broker blog (“Earnings drive the market” LOL) that got me thinking about other ways to show this relationship. Frankly, after trying dozens of approaches (index, smooth, correlation, R-square, inflation-adjustment, etc.) I couldn’t really come up with anything very compelling. So what follows is my best attempt to take a look at the long-term relationship between earnings, prices and P/Es.

For this analysis I’m using Online Data by Robert Shiller. The data goes back to 1871 and was used in his seminal book “Irrational Exuberance”. Exhibit 1 shows S&P Price, Earning and P/E ratios on the logarithmic scale. They are indexed starting with 100 in 1871. There appears to be relatively clear relationship between price (blue line) and earnings (red line). While P/E multiple has been slightly trending up, it generally oscillates back and forth based on the investor sentiment (Exhibit 2). Please note that these P/E ratios are based on reported earnings and will be different from my last post which used operating earnings data from S&P.

Exhibit 1 – S&P Price, Earning and P/E Ratios

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Source: Online Data by Robert Shiller; PlanByNumbers

Exhibit 2 – S&P P/E Ratio with Its Trendline

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Source: Online Data by Robert Shiller; PlanByNumbers

Another approach would be to look at the rolling 10-year correlation of S&P Returns with Earnings Growth and Annual % Changes in P/E Ratios. This allows us to step back from the exact relationship for each year as described in Reformed Broker post and analyze it in 10-year increments. The calculation is a little convoluted so I’ll attempt to explain it in Exhibit 3. Columns 2-4 have the raw metrics, I then calculated the Year-Over-Year % Change for each of them in columns 5-7. The final calculation is the correlation over the last 10 years between 5 and 6 resulting in column 8; then between 5 and 7 for column 9. A quick refresher: a positive correlation means the two variables move together, while a negative correlation indicates that they move in the opposite directions. A higher correlation number (positive or negative) means that the relationship is stronger.
Exhibit 3 – Calculating Rolling Correlation Example

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The resulting chart is shown in Exhibit 4 with blue line representing column 8 and red line showing column 9. As is often the case with correlations, it changes over time going from positive to negative relationship several times over the 150 years. Interestingly, from 1975 to 2000 earnings had negative correlation with prices while P/E was strongly positive. This would suggest that the index was driven by expanding P/E ratios while earnings growth wasn’t as important. Than in 2000 there was an abrupt change in regime and earnings became an important driver of index performance. At the same time, P/E ratio correlation plummeted and turned strongly negative. This situation peaked in 2008 and has been slowly abating since.

Exhibit 4 – Rolling 10-Year Correlation of S&P Returns with Earnings Growth and Changes in P/E Ratios

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Source: Online Data by Robert Shiller; PlanByNumbers

Conclusions
I realize that this analysis was not highly scientific, but the general takeaway is that: Yes, over time there is a positive relationship between earnings growth and index returns.  Market’s mood does change and sometimes it cares more about P/E multiples while others are driven by earnings growth.

Simple Math of Earnings, Multiples & Returns

Professional investors like to throw around fancy words to make themselves feel smart and important.  One of those terms is “Multiple Expansion”.  I figured that 2013 was a great example to illustrate what they mean.

P/E ratio or Price-to-Earnings multiple is probably the most common way to value a stock or a market index.  Let’s take a look at Exhibit 1 and decompose what happened in 2013.  Bear with me here,  The S&P 500 Index closed the year at 1,848.36 up from 1,426.19, so return was (1,848.36 ÷ 1,426.19) – 1 = 29.6%Note: This is the price return which excludes the 2.5% dividend yield.  Operating Earnings grew from 96.82 to 107.07 last year, a jump of 10.6%P/E multiple at the end of 2013 was 1,848.36 ÷ 107.07 = 17.3 times.  This means that the investors were willing to pay 17.3 dollars for each dollar of earnings.  This number went up from 14.7x a year ago – BINGO! – we got MULTIPLE EXPANSION of 17.2%.  Now we can also calculate that multiple expansion accounted for about 62% of the 29.6% return for the year.

Exhibit 1 – Earnings, Multiples and Return Breakdown for 2013

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Exhibit 2 shows the same data for each year since 1990. Exhibit 3 displays the middle three columns of this table in a chart format for the more visually-inclined readers.  There are some interesting takeaways from this analysis (at least interesting for a data geek like me).  Earnings tend to increase over the years while P/E multiples tend to jump around a lot.  There were only 5 years out of 24 where earnings decreased year-over-year.  Earnings are typically driven by the business cycle and economic growth, so they closely resemble GDP growth trajectory.  P/E ratios, on the other hand, are largely driven by investor sentiment which is quite fickle.  Investors might be willing to pay 25 times earnings one year and then balk at the 15x multiple the next.

Exhibit 2 – Annual Earnings, Multiples and Returns Since 1990

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Exhibit 3 – Annual Earnings, Multiples and Returns Since 1990 – Chart Illustration

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Now that we understand how multiples and earnings growth work together to produce price appreciation, we’ll take a look at the longer history of those metrics in a future post.

Data Note

Standard & Poor’s provides free regularly-updated spreadsheets with a wealth of date about the S&P 500 index earnings, dividends, constituent companies, etc.  This detailed information is one of the reasons so many people use the index in their market analysis.   The spreadsheets can be found by going to http://us.spindices.com/indices/equity/sp-500.  Then click on ADDITIONAL INFO dropdown and select “Index Earnings” – this will open an excel spreadsheet with multiple tabs containing variety of earnings data.

2013 Review: Economy Edition

The last two weeks I reviewed investment performance in 2013 (2013 Review: Significant Events and 2013 Review: Asset Class & Sector Edition).  This last post in the annual review series will focus on economic indicators.

The employment situation continued to improve in 2013 (Exhibit 1).  The U.S. economy added 2.18 million new jobs (182,000 a month), slightly fewer than 2.19 million in 2012.  Total employment increased 1.6% which was better than 1.0% population growth.  The unemployment rate ended the year at 6.7% in December, the average of monthly rates was 7.4%.  U-6 rate is a broader measure defined as “Total unemployed, plus all marginally attached workers plus total employed part time for economic reasons”.

Exhibit 1 – Employment

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GDP growth slowed in 2013 (based on first three quarters) (Exhibit 2).  Auto sales, another metric of economic health, increased 6.8% approaching 16 million a year.  Inflation, as measured by Consumer Price Index, stayed very benign and decreased for the second year in a row.  This combination of slowing growth and mild inflation would argue for continued support from the Federal Reserve.

Exhibit 2 – Growth & Inflation

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Public debt increased to $16.7 trillion during the year (Exhibit 3).  On the positive side, a combination of tax hikes and spending cuts (sequester) led to a big drop in budget deficit.  It declined by 37% to $680 billion to about half of what it was in FY 2011.  The Federal Reserve expanded its balance sheet by over a trillion dollars to $3,759,000,000,000 (that’s a lot of zeros!).  Average monthly increase was about $90 billion, which is in-line with its official QE pace of $85 billion a month.  The purchases have been “tapered” to $75 billion beginning in January 2014.  We’ll do a separate post analyzing Fed balance sheet in more details.

Exhibit 3 – Debt & Deficit

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S&P 500 earnings growth accelerated to 10.7% (Exhibit 4).  However, most of the strong index performance in 2013 came from P/E multiple expansion, which increased by 17% to 17.2x from 14.7x.  Both 10-Year Treasury rate and 30-Year Fixed Mortgage rates increased from record-low levels.

Exhibit 4 – Earnings & Rates

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Finally, the housing sector continued its slow healing process (Exhibit 5).  While still nowhere near prior peaks, both unit numbers and prices improved again in 2013.

Exhibit 5 – Housing

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All of these data and MUCH more are available for free at http://research.stlouisfed.org/fred2/.  This is a great resource for investors and financial planners who like to do their own homework instead of relying on whatever sensationalized datapoints the media choses to focus on.

I plan on taking a closer look at many of these metrics in future posts.