Republicans Versus Democrats on Debt and Budget Elements

Since the "Great Recession" is over, Presidential campaigns are looming and Obama has submitted his final Budget, I have decided to update some past postings in which I have rated the Presidential terms (and Parties) since Kennedy.  As before, I have downloaded all the historical spending and forecasts from the 2016 Budget package.  These can be found on the Office of Management and Budget website.

Looking at the actual budget incremental numbers by year would clearly bias conclusions for the most recent Presidential terms since our economy is steadily growing.  Therefore, all of my analysis is based on the growth rate of budget numbers expressed as percent growth compounded annually for each Presidential term.  There are an equal number of terms for both Republicans and Democrats in my analysis with each party also having one 4 year term.  In addition, Kennedy/Johnson were combined into one 8 year term as was Nixon/Ford.  Since Obama has submitted the 2016 Budget, the 2015 budget is half over, so that estimate should be fairly close and I am using the 2016 estimate to round out his 8 year term.

Below is the graph for Obama's 8 year term and this same technique was used for all Presidential terms on each budget category.

The total Budget Outlays are graphed above, beginning in 1961 through 2020, with the last 6 years being budget estimates.  The statistics are calculated, in this example, from 2009 thru 2016 which will be Obama's 8 years.  You will notice some red boundaries on the graph, for this period, which reflect the bounds of expected variation in the annual Outlays.  This would let you know if there was "unusual" year among his 8.  Centered under the graph, is a table of statistics, including the Compound Annual Growth Rate (CAGR) of 1.5% which is just above the blue highlighted entry.  Therefore, during Obama's 8 years (1 1/2 are forecasted), Government spending has grown 1.5% each year, compounded.  You should be able to d-click on this graph to see a larger version.

This type of analysis was done for every Presidential Term on the following Budget categories:

• Real GDP
• Nominal GDP
• Individual Income Tax Receipts
• Payroll Tax Receipts (Social Security and Medicare)
• Corporate Tax Receipts
• Total Receipts
• Total Outlays
• Annual Deficit
• Supplemental Spending (off budget - calculated by Total Debt - sum of deficits)
• Total Debt at the end of the year.

I also calculated, for evaluation, three additional categories of Budget line growth minus GDP growth.  This would show if a budget item is growing faster (positive number) or slower (negative number) than the economy:

• Total Receipt growth minus GDP growth
• Total Outlay growth minus GDP growth
• Total Debt growth minus GDP growth 

The table below summarizes these findings.

As I have done in the past, to evaluate each Term relative to the others, I have defined "Best" as Receipts with the highest growth rates and Outlays, Deficits, Supplementals and Debt with the lowest growth rates.  In short anything that makes the Debt fall.  In the table above, magenta color reflects the "Best" performance in each budget category.  Likewise, the orange color represents the "Worst" performance.  Any underlined number was found to be statistically different from all other results.  At the bottom of the table is the average growth for all Democratic terms and the average growth for all the Republican terms.

Below are my highlights from the table:

1. Real GDP, which represents economic growth, grew 1% better for Dems.  Kennedy/Johnson had the highest growth at 5.4%
2. Individual and Corporate Tax receipt growth was higher for Dems (6.1% and 2.1% respectively).  Could it be that HIGHER taxes yield HIGER economic growth???  Notice for Bush 1, who had the lowest growth rate in both Individual and Corporate Taxes, he also had the lowest Real GDP!
3. Total Receipt growth was 3.6% higher for Dems which is likely due to both higher tax rates and higher economic growth.
4. Total Outlay growth was 1%  for Republicans.  I thought they valued SMALLER government.
5. Supplemental Spending was 20 times higher for Republicans!!  Reagan had the highest rate at 46.6%.
6. Total Federal Debt grew 5% faster under Republicans!  Reagan had the highest Debt growth at 15.1% likely caused by the high Supplemental.  The only other term with double digit growth was Bush 1 at 11.8% when his Outlay growth was twice the Receipt growth.

Finally, to determine the Best and Worst Presidential Terms, I evaluated each of Budget Categories I listed earlier, eliminating GDP but keeping Real GDP.  I also included the 3 additional calculated categories bulleted above.

I assigned 2 points for the best growth rate in each category and 1 point for second best.  (There was a tie in one category).  Likewise I assigned -2 points for the worst growth rate and -1 point for second worst.  Finally I added up the scores for each Presidential Term.  Here are the results:

I was very surprised by these results as they are different than in my earlier post!  So I tried a different approach using only Real GDP and my 3 calculated categories which should have negated any inflation issues.  To my surprise again, the results were very much the same.  Carter dropped to 4th (inflation issues) and Reagan switched with Bush 1 for last place!

I know there are many ways to evaluate a Presidency, with historians having a strong say.  However, as a data monger, I am glad that I have some data analysis approaches to use as I begin to think about the upcoming election cycle and all the rhetoric.  Good luck to us all!

Climate Change Modeling Has Some Problems. History Might Help!

On September 19, Steven E. Koonin wrote an article for the Wall Street Journal titled "Climate Science Is Not Settled" .  Although there are many interesting observations in the article, what caught my attention was the topic of Climate Models. http://online.wsj.com/articles/climate-science-is-not-settled-1411143565?mod=WSJ_hp_RightTopStories   The basic message is that all of the modeling that has been done cannot accurately predict our current climate results.  Here are a couple of quotes from the article:

"As a result, the models give widely varying descriptions of the climate's inner workings. Since they disagree so markedly, no more than one of them can be right."

"Although the Earth's average surface temperature rose sharply by 0.9 degree Fahrenheit during the last quarter of the 20th century, it has increased much more slowly for the past 16 years, even as the human contribution to atmospheric carbon dioxide has risen by some 25%. This surprising fact demonstrates directly that natural influences and variability are powerful enough to counteract the present warming influence exerted by human activity."


Models are ways of predicting a future result which in this case is the Average Annual Temperature of Earth.  For most of us and in our experience, the range of highest to lowest temperatures seems to occur within a year, which captures all of the seasons.  So no surprise, that modelers are focused on the Average Annual Temperature and all the "influencers" that occur within a year.  By studying many years, say 200 or so, you could get a good idea if this Annual Average Temperature predicted by the models, is accurate.  (Useful worldwide temperatures date back only to 1880, hence the 200 year history.)

If you wanted to predict (or even measure) the Average Annual Temperature on Earth, clearly you would not gather climate data only for March and September!  You would be missing some important information.  Likewise, as we take the Average Annual Temperature and find that it is increasing recently and setting records, as compared to 1880, MIGHT WE BE MISSING SOMETHING BY NOT LOOKING AT OTHER CENTURIES OR MILLENNIUMS??  Might there be other temperature cycles we need to understand before we can declare that "these are the highest temperatures we have ever seen, and, therefore, caused by human influences?"  The operative word here is "we".  Could there be some other temperature cycles beyond those that occur within a year which could be affecting the climate models?  Should we instead be looking at the Average Century Temperature for instance?

As many of my posts suggest, there is a great deal to be learned from studying the history of any problem, so I will try that approach again.  The history of worldwide climate data has all been within the last 200 years.  However, there is some data that goes back 800,000 years and this amount of history might give us some insight to the "cycles" of both temperature and the corresponding CO2 levels, which are often in the news.  This data comes from ice core samples, 2 sites in Antarctica are titled EPICA and Vostok (temperature data is listed a degrees centigrade variation from present); and one Arctic site called GISP2 in Greenland (temperature is degrees centigrade).  The process and science of these measurements can be read at  http://www.climatedata.info/Proxy/Proxy/icecores.html but I will focus on the data itself which can also be downloaded from this site.  But to be clear, this is very localized data and does not reflect the Global Temperature.  However, it is the relative changes over time at this location which can help us understand if there are any longer term "temperature cycles" that are affecting the globe such as the Milankovitch Cycles.

Here are the key points that are supported by the graphs and analysis which follow:

1. Earth is getting warmer!  It is supposed to be getting warmer since we have been in an interglacial warming period that began about 17,000 years ago.  This warming began at the end of a long glacial period and when combined with the interglacial warming period, lasts about 100,000 years.  These cycles have been repeated for at least 800,000 years.  There is a longer term temperature affect in play beyond the 4 seasons in a year.
2. If you look at the previous interglacial warming period 127,000 years ago, we have not yet reached the previous high of 4.84 degrees.  100 years ago we were 1.8 degrees hotter than we are today, because we are at "0", the base line for all these temperature measurements.  We are not yet as warm as we have been in previous temperature cycles.
3. The most recent interglacial temperature rise has taken longer than the previous two, but not yet reached their highs.  The pattern and length of our recent interglacial rise, however, looks very similar to that of 422,000 years ago.  This also holds true for CO2 levels.  Using this as the benchmark, we could have 11,000 more years of warming before hitting the historical highs.  This might yet be another even longer temperature cycle we need to understand.
4. Looking at the last 50,000 years of temperatures, which begins in the middle of the last glacial period, you see a very rapid rise in temperature about 10,000 years ago.  Since this time, the temperature has been fairly stable, until the last 600 years, when the temperatures dropped to a new, lower stable level.  This is the period of highest human activity.
5. Using only the last 50 or 200 years is not nearly enough data to understand the amount and sources of the earth's changing temperature.  Without this understanding, we cannot model or assess the impact of the human contribution to Global Warming.  We need utilize and model at least 422,000 years of history.  If we have not yet hit the historical highs from all these years ago, how do we conclude that humans are to blame?  Are we the cause of global warming, or rather are WE to adapt to the predictable warming yet to come as our ancient forefathers have done???

ANALYSIS AND GRAPHS

The EPICA ice is the deepest, so it yields the greatest amount of history going back 800,000 years.  Below you will find a control chart (X, MR) of the Temperature data.  The upper chart is the actual temperatures (tracked as plus/minus from the current temperature) and the lower chart is a measure of the variation in these numbers.

The first 400,000 years show a different pattern with a range of temperatures from 3.15 to -9.63 than the last 400,000 years, with a temperature range of 4.84 to -10.58.  You will also see a change in the patterns in the second half of more distinct and rapid rise in temperatures (interglacial periods) and longer lower temperatures (glacial periods).  This cycle of glacial and interglacial periods has also been increasing from 74,000 to 113,000 over past 3 cycles or about 20% increase each cycle.  The most recent cycle is not yet over and is already 130,000 years.  Also notice that the variation in the temperatures is also increasing, which for the modeler, creates issues in creating accurate forecasts.  Below you will see the similarities to the corresponding CO2 levels.  Notice however, that there is not a large increase in the CO2 variation.

Now, comparing the first 400,000 years to the more recent 400,000 years can help us determine if the change in cycle patterns and ranges results in a statistical difference for temperature or CO2.

In the last 400,000 years, temperature dropped .49 degrees or 9.7% while CO2 actually rose 0.9%.  Here is another anomaly for modeling CO2 and temperature.  The pattern of the most recent temperature rise is quite different from the previous 2 interglacial rises which are quite steep and short.  However, the interglacial rise 422,000 years ago looks very similar to the most recent rise, and needs a closer look.

The first two interglacial periods below have the same average temperature and CO2's but the second one is less than a third the length of the first, 28,600 years vs 8,000.

For the third and fourth interglacial periods, the average temperature has risen, the CO2 levels are the same, but the length of time is about the same, 8,200 years and 8,000.

Finally, comparing the most recent interglacial to the similar one 422,000 years earlier, the average temperatures and CO2 levels are the same, but has not yet reached the highest temperature or CO2's of the past.  Also, the length of the most recent period is 17,000 years.  Could we be repeating the interglacial warming pattern of 422,000 years ago?  Could it take 11,000 more years to reach our interglacial high and only then know if "we" caused it?

The next several graphs of EPICA data will focus on the last 422,000 years, which actually cover the same time frame as the Vostok data.  To show how similar both the data sources are, the next 4 control charts (X,MR) will show the EPICA and Vostok temperatures followed by the CO2 levels.

EPICA and Vostok Temperature

Patterns very similar

EPICA and Vostok CO2

Patterns very similar

The next analysis will show the changes in average temperatures over  each of these 4 glacial/interglacial cycles using EPICA data.  I have done the same analysis on the EPICA CO2 data as well as the Vostok Temperature and CO2 data.  To prevent graphic overload, I will summarize all of this in a table at the end of this section.

The cycles here are defined as the maximum interglacial temperature to the next maximum interglacial temperature.  Each figure contains the X Chart, a histogram, the F and t statistic and a summary of the two cycle's data ("before" and "after").  The blue highlight in the lower left corner signifies a statistical difference either in the averages or the variations between the two cycles.  For the first two cycles, the average temperature dropped .46 degrees or 9%.

From the second to third cycles, the average temperature dropped .48 degrees or 8.8%.

From the third cycle to the fourth cycle (most recent), the average temperature rose .6 degrees or 10%.

However, comparing the first cycle to the fourth, the average temperatures are NOT statistically different!  This coupled with the 17,000 year interglacial temperature rise pattern studied earlier, leads me to conclude that any modeling needs to consider at least 422,000 years of history to establish all of the independent variables that could impact temperature changes.  Since good climate databases go back only a couple of hundred years, this could partially explain the problem with the current climate models.

Note below that the Vostok temperature for the first and last cycles, shows a difference when the EPICA data shows the temperature to be the same.  This is further evidence that the current interglacial warming may not be over.

Looking at the most recent interglacial rise of the last 17,000 years using another ice core database might help reinforce the point of needing more history to get accurate models.  The GISP2 data includes 50,000 years of temperature data at a much more granular level, about every year.  This control chart (X, MR) shows that there has been a dramatic shift up in temperature about 10,000 years ago and has been pretty steady since.  So, we need to focus on the significant event of 10,000 years ago!  It sure looks like this is just a part of the interglacial warming that began 17,000 years ago.  But, lets look closer at these last 10,000 years to determine if the human affect of global warming can be seen.

By zooming in on these last 10,000 years, which appeared to be fairly stable, shows even more information.  In the last 900 years the temperature dropped 1.6 degrees from earliest average.  But, over the last 300 years, the temperature is rising again, but has not yet returned to the earlier highs.  So again, our issue is not what happened in the last 300 years, but what happened 10,000 years ago.  Our current models will just not help with this question!  To confirm that recent human industrialization is the cause of "global warming", we would need to see global temperatures statistically higher than we have seen in the last 422,000 years!

In conclusion, to show the human affect of global warming, we need to see CO2 and temperature levels that have not been seen in human history.  That has not yet happened!  Modeling needs to include more of our earth's history in order to forecast when, and how high, our current interglacial warming period will go.  Only then can we know if, or how much, humans have impacted this interglacial cycle.

Homicide Statistics Bias by Gun Politics

A recent email I was forwarded, contained a list of Homicides/100,000 citizens for many countries around the world.  The email subject was "Eye Opener" and began with the title "World Murder Statistics".  What followed was a list of 109 Countries, with Honduras at the top of the list with 91.6 Homicides / 100,000.  Last on this list was the USA with 4.2 Homicides / 100,000 citizens.  The email ended with this statement:

 "ALL the countries (109) above America have 100% gun bans. It might be of interest to note that SWITZERLAND (not shown on this list)has NO MURDER OCCURRENCE!However, SWITZERLAND'S law requires that EVERYONE....

1. Own a Gun
2. Maintain Marksman qualifications....regularly
3. "Carry"........a Weapon."

As has been my habit when I see a list of numbers, I first went to the source of the data to confirm what I saw in the email.  Indeed, there is data supplied by the United Nations Office on Drugs and Crime (UNODC), which is different than the email's claimed source of the World Health Organization.  The following links will take you to these data summarized in an active table, but on these sites there are links to the complete data set from the UNODC which I downloaded and found to be the same as these links.

List of countries byIntentional Homicides / 100,000 Inhabitants

List of countries by firearm-related death rate per 100,000 inhabitants 

List of countries by gun ownership rate per 100 inhabitants

I first began to understand the "109 countries above America" and what this meant.  In order to find the Honduras rate of 91.6,  the email was referencing Intentional Homicide data.  All the data in the email were correct for all countries EXCEPT America!  The email stated that the United States rate was 4.2 / 100,000 but from the UNODC data set, the United States rate was 4.8.  In addition, there were 102 countries with rates higher than the US (not 109) and, not stated in the email, 104 countries with Homicide Rates LESS than the US.

In order to understand if all 102 (109) countries with rates worse that the US indeed had "gun bans", I utilized Gun Politics  for more information.  In summary, I could not find any country with a "100% ban" on guns.  However, many of these countries do indeed have stronger gun restrictions, but in these cases there are ways to obtain and possess a gun.  But to be clear, there are an equal number of countries with Homicide Rates LESS than the US that have more restrictive gun laws than the US.  So, it appears that restrictive gun laws do not seem to predict Homicide Rates.  But to test this I did download Gun Ownership data to correlate to Homicides which I will cover later.

Now, I wanted to investigate the comments about Switzerland! The statement that they have "no murder occurrance" is not accurate.  In fact, on this same list their Homicide Rate is 0.7 with 15 countries lower than Switzerland.  And finally, gun control in Switzerland is based on a militia concept as seen from this quote from Gun Politics.

Switzerland practices universal conscription, which requires that all able-bodied male citizens keep fully automatic firearms at home in case of a call-up. Every male between the ages of 20 and 34 is considered a candidate for conscription into the military, and following a brief period of active duty will commonly be enrolled in the militia until age or an inability to serve ends his service obligation.^[76] During their enrollment in the armed forces, these men are required to keep their government-issued selective fire combat rifles and semi-automatic handguns in their homes.^[77] They are not allowed to keep ammunition for these firearms in their homes, however, and ammunition is stored at government arsenals. Up until September 2007, soldiers received 50 rounds of government-issued ammunition in a sealed box for storage at home.^[78] Swiss gun laws are considered to be restrictive.^[79] 

So this law does not apply to everyone, but only to males.  They are required to keep a gun in the home for immediate call up to the militia (after serving in the armed forces) and does not mention anything about "carrying" a gun.  The marksmanship requirement I could not find either.  However, the most interesting fact was left out of the email.  Although required to keep the firearm at home, THEY HAVE NO AMMUNITION AT HOME!  All of it is stored in government run arsenals!  No wonder the death rate is so low!  Guns at home without ammo.

Now, moving on the actual data.  First I thought it interesting that this email used Homicide Rates by all methods.  There is a database of Homicide by Firearms by the UNODC which I found and began to look at relative to guns/firearms.  This database has fewer countries participating but there are still 70.

Applying statistics to all these lists, I was first interested in statistical differences between lower and higher rates.  I evaluated this using control charts with limits based on population sigma since the data were not time ordered, just alphabetical.  First we will look at Gun Ownership per 100 Residents.

The X chart at the top, clearly shows only one outlier country which is the US!  All other countries are within the normal range of per capita ownership.  This is probably not new news to most of you.

Next I looked at Homicide by Firearm Rates for Total, Homicide and Suicide.

There are two countries outside the upper limit signifying outside the "norm" for Total Firearm Homicides.  These two countries are El Salvador and Honduras.  If we take the 95% confidence (2 sigma), Columbia, Guatemala, and Sweden could also be considered outside the norm.  Switzerland is considered to have less restrictive gun laws, but Honduras more restrictive.

For Firearm Homicide Rates, the two outliers are Guatemala and Hungary,  At 95% confidence, add El Salvador, Honduras, Japan and Sweden.  Japan is considered to have more restrictive gun laws than the US.

Finally, the Firearm Suicide Rate shows Netherlands and Zimbabwe to be uniquely high.  Netherlands is considered to be more restrictive in their gun laws.

I could not find any correlations between gun ownership per 100 inhabitants and any of the firearm rates as seen below:

As you can see, between the two graphs, there is a small white box with a -2.8 which means the correlation is non existent and all others even weaker!  The number of guns don't correlate to any of the firearm death rates so I would conclude that other factors, including culture are more important.

My takeaway from this closer look at the email and the corresponding data is the culture of guns and gun politics has very little to do with firearm homicides and suicides.  It is time for the different groups battling over gun laws to take a new direction to make their respective cases!

Apple - Stock Price Drop Justified??

Following on my post of October 26, 2012, Corporate Quarterly Earnings Report's Negative Effect on Wall Street, I began to wonder if the recent $200 drop in Apple's stock price would correlate to its actual financial performance.  To note, the price began dropping in mid-September 2012, and might be now stabilizing as of this writing.

To begin my investigation, I collected, from SEC filings, Apple quarterly Revenue and Earnings figures back to March 1993.  As you have probably gathered in my other posts or from reading the information at my website (www.sustainthegain.com), I am not a fan of using Indices of these quarterly figures relative to a previous period!  Remember, a trend of one (recent quarter compared to one previous one) is not significant!  However, to reinforce this idea, I will produce a few examples of this analysis technique and make a few comments on them.  After these examples, I will return to the more meaningful analysis technique using the actual Revenue and Earnings data.

I have displayed these indices in a control chart, in order to gain some statistical reference!  I will start with Revenue, and in particular, Index versus Previous Quarter.

The first thing to notice is that at first glance, this chart appears to be stable at an average index of 1.065.  Compounded quarterly, this is a Compound Annual Growth Rate (CAGR) of 26.6%.  More importantly, the last two quarters ending Sept and Dec of 2012 are NOT uniquely different and, therefore, don't suggest any reason for a decrease in stock price.

There are some other things to take away from this chart.  The Upper and Lower Control Limits (UCL, LCL) are 1.78 and 0.35.  This means that any single quarter's index would need to be greater than 1.78 or less than 0.35 to be "out of the ordinary"!  As I have said, most companies are spending precious time explaining indices of 1.05 or .96 when a single, unique explanation is fruitless since only the common causes are acting on the results.  Whatever explanation is offered will now falsely become part of their institutional memory.   However, there is a distinct change in the pattern of these indices beginning at the middle of the chart, which is actually, March 2004, so lets take a closer look at this change.

 Although not obvious in the first graph, there has been a statistically relevant change 3/04 when the average index rose from 1.012 to 1.124.  This is the equivalent of increasing the CAGR from 4.9% to 59.6%.  Sounds great, but still nothing showing up for the 9/2012 stock drop!  My conclusion is that there was one sustainable positive change in 3/2004, and a positive "bump" in 12/1999 which could not be sustained.  My research on Apple SEC filings turned up a major accounting change in 2004 whereby the Revenue was reported differently!  It is pretty clear that the OND quarter is the highest index each and every year, since 2004!  This is one of the rare examples of indices, in control chart form, will indeed highlight a sustainable change.  The good news is that the actual quarterly results show this change as well, so still no need to use Index versus a Previous Quarter.

Below is the same Quarterly Revenue, but displayed as Index versus Year Ago (IYA).

It is more obvious that there is a change around 3/2004, but look how messy the individual indices are!  And the OND quarter pattern change is not indicated.  Also, the width of the UCL and LCL is quite large and the average 4 quarter index is 1.44 since 3/2004.  But, the last 3 quarters are all closer to the LCL than to the average, which is a signal of a possible change.  Could this explain the stock price drop??  I doubt that anyone on Wall Street is using control charts on indices!

Would Earnings as either Index Quarter Ago or Index Year Ago, show anything more??

 IQA does not show a average index shift in 3/2004 but the high OND quarter, each year, can be seen after this date.  The average index in Earnings is .962 which means that the earnings are shrinking!  This is likely an issue with the very low index at the beginning of the chart.  After 3/2004, the average index is 1.22.  Now look at IYA to see if there is anything more insights.

In the IYA case, the rise in the average index again shows near 3/2004.  But the most striking thing on this chart is the reduced variation in the earnings index after the accounting change of 3/2004.

In summary, the use of indices turned up only one sustainable change in performance since 1993, which was the accounting change in 2004.  We also did NOT see any significant changes in 9/2012 which would explain the stock price drop.  Sooooooo, we will move on to control charts using the actual quarterly results, starting again with Revenue.

Using the actual quarterly data for Revenue, you can find 4 timeframes of stable performance:  first from 3/1993 to 12/1995 when the CAGR was 18.3%;  then 1/1996 to 9/2004 when the Revenue dropped and CAGR was -0.6%;  next from 12/2004 to 6/2010 when the Revenue rose and the CAGR was 33.3%;  finally from 9/2010 to 12/2012 when Revenue jumped and the CAGR rose slightly to 35.5%.  The increasing variation (width of the blue UCL and the yellow LCL) in 9/2004 and 12/2010 is consistent with increasing quarterly values.  

In 1/1996, Windows 95 was introduced and most likely explains the drop in Revenue 1/1996!  In the following years, Jobs became CEO, Mac OS 9 ships, G4 Cube introduced, Apple Stores Open, i-Pod ships, Mac OS X ships and i-Tunes starts late 2003.  In 2004 we have the accounting change, 17" mac display and i-pod mini.  So what was the 2004 breakthrough......you pick, but my guess is accounting!  Had the pattern of Revenue (high OND quarters), I might have said this breakthrough was  i-Tunes.   The i-phone launches in 2007, 3G in 2008, but it is the i-Pad and i-Phone 4 that both launch in 2010 which creates the jump in 9/2010 and maybe only the i-Pad.  

However, trying to explain the stock price drop is more difficult!  Wall Street does not analyze using these techniques so they were not aware of the rise in CAGR!  The last two OND quarters were at or just above the UCL, but this should have been good news!  You can see that the Spring and Summer quarters had lower revenue but not outside the LCL!  It does appear that if you averaged all 4 quarters of 2012, you would get a number that falls right on the green trend line.  I decided to check this by obtaining the annual revenue numbers since 1993 which gave me the following graph.

Since 12/2004, the annual Revenue has had stable, predictable CAGR of 41% with 2012 landing right on the trend line!  So, it seems that Revenue should not have the caused of the stock to drop.  Could it have been Earnings??  Below is a graph of actual quarterly data.

There are only two sustainable breakthroughs in Earnings when there were 3 in Revenue (4 stable timeframes), but remember that one of the Revenue breakthroughs was the Accounting change that applied only to Revenue.  The Windows 95 intro in 1/96 did yeild a couple negative earnings quarters but not a shift in the Earnings.  In 12/2000 there was large loss in Earnings after 3 years of positive growth.  This lines up quite well with when Jobs became CEO.  He likely took a big write down after which the Earnings CAGR took off at 57.9%.  Then, simultaneous with Revenue, the Earnings jumped in 9/2010 but CAGR dropped to 40.1%.  Even if Wall Street had been tracking Earnings growth with control charts, the stock price should have dropped before 2012 since the 4 quarters of 2011 would have been sufficient to get a signal of this change.  But I'm sure they were not doing such an analysis!  An argument could be made that the OND 2011 and OND 2012 for Earnings were approximately the same value, when Wall Street would have expected at least a 20% year on year increase.  This was likely the stock downfall, but it is clear from the control chart that OND 2012 is just a random, non-significant result that fell between the control limits and should have been given no special consideration.  Had the stock problem been due to the i-Phone 5 intro and the "Apple Maps" problem, this should have shown up in Revenue.

An important note about Apple Quarterly Earnings News Releases:  In my 10/26/12 article referenced at the beginning of this post, I gave many examples of companies that spent significant time trying to explain every non-significant up or down in their results using Index Year Ago as the basis.  However, Apple does NOT report this way.  Every News Release follows the exact same format: the first paragraph reports actual results of this quarter and the same quarter year ago, but they do NOT use indices; the second paragraph gives sales figures but again avoids IYA; the third paragraph reports the dividends declared; the fourth and fifth paragraphs use the phrases "We are thrilled" and "We are excited" to describe the records they have set in Sales, Revenue and Earnings.  But they never attempt to tie a particular product event directly to a change from the quarter year ago!  Way to go Apple for not poisioning their institutional memory.   Might this be a contributor to their success??

Social Security Myths Set Straight

With all the media attention on the approaching fiscal cliff and the need to reduce entitlement spending along with increasing revenue, there has been expanding information about Social Security taxes paid and benefits collected.  Most recently I received an email on Social Security titled "Federal Benefit formerly known as Social Security".  In the widely circulated message, the writer states " If you averaged $30K per year over your working life, that's close to $180,000 invested in Social Security".  In my earlier post of May 23, 2011 on the subject of Social Security and costs per person, I too discovered that an average person pays into Social Security about $198,000, so no argument about this part of the email.  

However, later in this same email the writer states: "If you calculate the future value of your monthly investment in social security ($375/month, including both your and your employer's contributions) at a meager 1% interest rate compounded monthly, after 40 years of working you'd have more than $1.3+ million dollars saved! This is your personal investment .  Upon retirement, if you took out only 3% per year, you'd receive $39,318 per year, or $3,277 per month .  That's almost three times more than today's average Social Security benefit of $1,230 per month, according to the Social Security Administration "  Note that this 3% withdrawal rate implies that the average person lives 33 years after retirement to consume the entire amount which means about 98 years old.

This email said this average person could actually have done nearly 3 times better than "investing" this money with Uncle Sam.  My "data radar" went off since this was vastly more that my previous post suggested.  So I started with the $375/month an average employer/employee paid into SS.  I can confirm that this is in the ballpark.  Next I had to look at the $375 monthly payment invested at a "meager 1% interest rate compounded monthly".  First, compounding an investment at 1% compounded monthly becomes 12.7% annual growth rate (1.01 raised to the 12th power).  I would not consider this "meager".   Also, if you used this 1% compounded monthly, your $375 monthly payment becomes $4.4 million over this 40 year work career which is very different than the $1.3 million stated in the email.

So maybe the writer meant that "meager 1%" was an annual rate but compounded monthly.  If this is the case, then the monthly compound rate would be 0.082954% (1% to the 1/12 power).  Compounding $375 per month for 40 years at this 0.082954% rate leads to a final account balance of $220,995 at retirement.  If you withdrew 3% a year from this account, that would yield $6,629 / year or $552 / month.  This is half of what this average person would get from Social Security.

So, what would the interest rate need to be so that this same $375/month would deliver $1,200/month at retirement after 40 years of employment.  Assuming the same 33 years in retirement, this would be 4% annual investment rate, but compounded monthly.  This seems about right for a conservative investment rate over 40 years.  (Please note that these calculations are all in 2012 dollars and assume that the 40 year invested amount does not continue to earn interest in the 33 years of retirement which seems to be assumption my email writer made)

So, what is my take away from this encounter??  If you get an email from anyone that has been forwarded from someone else and it has math involved, assume it is wrong until you confirm the arithmetic, including this posting!  Maybe this is also a indictment of the math education we receive in the US.

Corporate Quarterly Earnings Report's Negative Effect on Wall Street

In the last week, the Dow has suffered a 202 and a 240 point single day drop on 10/19 and 10/23 respectively.  The media coverage of these events headlined the weak Corporate Earnings as a cause of the poor performance in the Stock Market.  This rekindled my long held belief that any of these explanations do NOT have any statistical relevance to the real business results.

To check this out, I read several business sites like CNBC and Morning Star, and recorded the companies that were mentioned as explanations of these two drops in the Dow.  About 80% of the ones mentioned are also in my database of companies that I have been tracking since 1993.  So I updated results with the quarterly results announced in October for these companies to understand if any the JAS 2012 quarter were statistically relevant in comparison to previously reported quarters.

There were 15 companies in my database that were also reported in the media as contributing to drop in the Dow.  I track both Revenue and Basic Earnings per Share (EPS) for these companies which gives me 30 possible areas of concern in Corporate performance.  After analyzing all 30 areas of performance with Control Charts, only 6 of these areas showed a statistical change in the last quarter which might have negative impacts on the Dow.  However, there were also 6 which showed a positive statistical change which should have "helped" the Dow.  But key for me is that 18 of the 30 results (60%) showed NO STATISTICAL DIFFERENCE from past performance and 5 companies showed no change in either Revenue or EPS!

For example, here are a several graphs from these studies.  First, lets look at IBM which has not had a statistically important change in either Revenue or EPS over the last 11 years, including the JAS 2012 quarter!  First, the graph of Revenue:

As you can see the last red result is slightly above the average performance of 2.2% annual growth (green line), but not anything statistically different or outside the normal limits (blue and yellow).

Again, the last red result for JAS 2012 is following the previous 11 year pattern.  This result is approaching the Upper Control Limit, UCL (blue) but as the absolute numbers continue the increase, so should the RSD or Relative Standard Deviation which will increase the width of the control limits.

So what shakes up Wall Street with these results??  You will read comments that express disappointment with these JAS results that "miss" the previous forecast estimate of performance either from "analysts" or the company's previous quarter report.  IF YOUR PAST 11 YEAR PERFORMANCE HAS BEEN THIS STEADY, HOW COULD YOU MISS A FORECAST?  The answer is simple: for the past 11 years, these analysts and executives have made their success by explaining every up and down in these results with "precise" singular causes.  These may range from new marketing programs, new products, acquisitions, economic conditions, material sourcing, organization changes and the like.  The problem is that when there are only normal (common) causes effecting results, the above chart is statistically stable, and has been for the last 11 years.  The "common causes" are a complex set of activities that influence results, and randomly interact in a way to create growth and variation that stay within these statistically calculated limits.  For example, below are some quotes out of the IBM most recent quarterly report that reflect this erroneous explanation:

Third-quarter net income was $3.8 billion, flat year-to-year; or $3.9 billion, up 3 percent excluding the impact of UK pension-related charges. Operating (non-GAAP) net income was $4.2 billion compared with $4.0 billion in the third quarter of 2011, an increase of 5 percent.

Total revenues for the third quarter of 2012 of $24.7 billion were down 5 percent (down 2 percent, adjusting for currency) from the third quarter of 2011. Currency negatively impacted revenue growth by nearly $1 billion.

“In the third quarter, we continued to drive margin, profit and earnings growth through our focus on higher-value businesses, strategic growth initiatives and productivity,” said Ginni Rometty, IBM chairman, president and chief executive officer.

You will first notice the use of Index versus Year Ago which shows up as a percent increase or decrease versus the same quarter in 2011.  Interesting but useless.  These changes are just chance (common cause related) and therefore cannot be explained by a single event or project.  Said another way the "currency adjustment", "initiatives" and "productivity explanations" could rightfully be used in any quarter!  However, since they have been explaining every up or down for years, their institutional memory would suggest to them that if they are working on a similar style project in the future, that they should be expecting another 5% increase in the future.  Problem is, when the future comes, the common cause system is just as likely to cause a 3% decline which in turn produces the forecast "miss" that creates the dip in the Dow.  WOW, what a huge, non-productive routine that does nothing more create more buying/selling, increasing the variation in the stock market which in turn creates winners and losers even though nothing has really changed!!  The quarterly report should have read: "Nothing has changed and IBM continues to reliably deliver a 2.2% compound growth in Revenue which in turn is generating an 18.1% growth in EPS."  These reports should go into detailed explanations only when there is a statistically relevant change and in my database of 85 companies, these changes only occur once every 7 years on average!

Here is another example of the "blamed" companies, Amazon.  First Revenue.

As you can see, consistent 30% growth for around 9 years with only variation increasing as the Relative Standard Deviation of the actuals increases over time.  No statistically relevant changes.  Now look at EPS.

Here you an see that EPS has shown a statistical change from the historical 29.1% growth.  However, it is clear that this change did not just occur in JAS 2012, but rather OND of 2011.  Why didn't the sky start falling a year ago??  Technically, nothing in the last 3 quarters has changed since a Special Cause in OND 2011.  Rest assured that Amazon has declared what terrible things have happened to them effecting each one of the last 4 quarters rather than just the one thing that happened in OND 2011.

Finally, an example of a company whose results really should have effected the markets with statistically relevant changes in the JAS 2012 quarter, UPS.

As you can see, the JAS 2012 quarter has two results in a row closer to the LCL (yellow) than to the Average (green) which is an indication that something has statistically changed outside the common causes.  This needs an explanation.

The JAS 2012 quarter is uniquely low compared to the last 2 years performance and is of concern.  This does have a unique cause which is explained in the quarterly report as seen below:

On a reported basis, third quarter 2012 earnings per share were $0.48. In August, the company announced a decision to restructure pension liabilities for certain employees. As a result, UPS recorded an after-tax, non-cash charge of $559 million during the quarter.

How could the entire quarterly reporting process be improved for all companies in such a way as to reduce unnecessary gyrations in stock market reporting and forecasting?  Simply report only when there is a special cause in the results, which will be about once every 7 years!  Using the statistically relevant explanation, a company (or analyst) would then update their forecast and continue to use this same forecast every quarter until the next, rare special cause comes along.  But alas, this would put a significant number of analysts and executives out of work and also reduce the number of winners/losers in the stock market game, which the "winners" will surely resist!

US Finances Compared to a Middle Class Family

Now that Romney has chosen Ryan as his running mate, I expect we will begin to see a great deal more news coverage of Mr. Ryan's budget proposal and his emphasis on deficit reduction.  With this news barrage coming, I thought it could be instructive to compare how a middle class family might manage their finances and compare this to how the United States is currently managing its (our) money.

This middle class family is struggling in the current economy and it is complicated by the fact that the  aging mother has had to move to a nursing home.  This family is working to cover the nursing home expenses beyond what her social security check is worth and she does not qualify for Medicaid.  The family has after tax income of $23,020 in 2011, and expenses, including mother, of $36,030.  The family had moved into Mom's old house which is in a good neighborhood but required significant renovations before this family could move in.  So, with the renovations and Mom's 2011 nursing home support rolled into the house financing, they have a mortgage of $101,280 with a pre-renovation value of $151,080.  This yields a monthly payment to the bank of $512 which is 27% of their monthly after tax income.  Looks like a pretty manageable situation, but how long can they continue to roll each years's overspending into their mortgage??

In the table below, you can see the financial picture for 2011 and then the family's best forecast for 2021.

You will see that income is rising a bit faster than expenses since the kids will be going to school and the other parent will begin working part time, close to home.  This family has successfully been able to roll their nursing home expenses into the mortgage and the payments are now a lower percent of their income than it was in 2011.  However, you can see that the mortgage value is approaching 80% of the value of the home, and likely the ability to roll the nursing home expenses into their mortgage will become more difficult.  They need a modified plan sometime soon after 2021.......but that is 10 years away!!  Maybe Mom's situation will change by then and their budget could be balanced!  So lets stay with the plan and update it in about 4 or 5 years.

Now lets take a look and the Federal Budget and resulting debt situation.  In the table below you will see a bit more detail with the year by year situation from the 2013 Federal Budget proposal forecast to 2021.  I think you will quickly see that by taking our family's numbers above and adding 8 zeros, you will have the US numbers in the table.

Included in the Total Expenditures is the interest payment on the debt which is only 10% of what our mock family was paying on their mortgage.  I think the number to keep in mind, which is also the numbers our lenders might be looking at, is Public Debt as % GDP.  In US history, Pubic Debt as % GDP was as high as 105% in 1946, dropped to 56.5% in 1956 and remained below 60% until 2008.  For perspective, here are some other countries 2011 Public Debt as % GDP that have been in the news: Japan, 208%; Greece, 165%; Italy, 120%; Ireland, 107%; Portugal, 103%.  So we are not yet in the danger zone, but if we want to get this ratio back to our 100 year average of 46%, we either need to increase our revenue (put everyone in the household to work) or reduce expenditures (let Mom go!).  No easy choices, but to be clear, our current US revenue levels are only large enough to cover Entitlement Programs which means all the borrowed money is being used to run the Government and the Homeland Security.  Just like any family, setting priorities will be critical, but, unlike our family, these priorities will be dictated by whom the politicians view as the largest voting block.