Step 1 (Page 138): As summarized in the following figure, “capital providers can compare each country on the basis of the drivers of its EE’s capital receptiveness.”

Step 2 (Page 140): “…capital providers should conduct industry analysis–focusing on the size, growth rate, and profitability for the average participant of a variety of industries in the country. Moreover, capital providers should think about how the determinants of industry profitability are likely to evolve in the future.”

Step 3 (Page 142): “Having chosen which country and industries in which to invest, capital providers need [to identify] a series of candidate companies…whose managers are skilled enough to take their capital and use it to enhance the value of the company.”

Step 4 (Page 143): “The case studies we explored suggest that success flows from investing in a company that passes the following tests:

• It’s growing and generating cash;
• It targets a large and growing base of customers;
• Its CEO is skilled at identifying new opportunities and exploiting them well…”

Step 5 (Page 144): “…a big part of making an investment pay off is betting on an entrepreneur with whom the capital provider is comfortable.”

Step 6 (Page 146): “…seek the exit option that yields the highest after-tax return.”

In summary, Capital Rising offers case study-derived guidance on exploiting global opportunities for investing in individual companies.

The book is aimed at private equity investors, but the guidance on picking global investment opportunities may have value for individual investors, advisors and funds.

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The following chart presents the raw annual (January through December) returns for the S&P 500 index by year for 1950-2009, with shape/color coding to designate the four years of the presidential term cycle. There are 15-16 observations for each cycle year. Visual inspection suggests that years 3 and 4 may be better than years 1 and 2, and that years 1 and 2 are more variable than years 3 and 4. The two best and two of the three worst years all come from year 2. Year 3 has no negative returns, and only three year 4 observations are negative.

To generalize, we compute average returns and standard deviations of returns by year and overall.

The next chart shows the average annual (January through December) return for the S&P 500 index for 1950-2009 for each year of the presidential term cycle and for all 60 full years in the sample. The small squares mark the averages, and the variability ranges span one standard deviation above and below average. The statistics confirm that years 3 is especially attractive. However, after 2008, year 4 no longer clearly beats years 1 and 2, though it is somewhat less volatile. The subsample size of 15 for each cycle year is very small, as is therefore confidence in the results. In other words, a few very contrary future observations could change the statistics substantially.

Are there any interesting quarterly patterns within the annual statistics?

The next chart decomposes S&P 500 index returns by “political quarter” for January 1950 through April 2010. Political quarters derive from the typical election breakpoint of early November, with political quarters therefore offset from calendar quarters by one month. The chart shows that the best political quarter overall is Nov-Jan (consistent with much other calendar effects research), with an average return of 4.2% across all 60 years. The worst political quarter is Aug-Oct, with an average return of -0.1% across all 60 years.

The strongest returns across the presidential term cycle come from Nov-Jan in year 2 through Feb-Apr in year 3; in fact, these are the only two quarters for which the average return is larger than the standard deviation of returns. Note that year 2 is a congressional election year, so November of year 2 brings some level of affirmation or repudiation to the President’s party. Standard deviations for these political cycle quarters range from 3.6% to 9.1%, with Aug-Oct most volatile and Feb-Apr least volatile. Again, subsamples by political quarter are very small, so confidence in these results is very low.

How do stock returns behave immediately around elections?

The next chart is a close-up of average daily S&P 500 index returns from 21 trading days before through 21 trading days after U.S. national elections for the total sample across 1950-2008 and several subsamples. Results for the total sample (light green line) include variability ranges spanning one standard deviation above and below average. The most consistent feature is a tendency to rally from about one week before election through one day after election, perhaps expressing investor relief that the campaign is winding down and/or reduced uncertainty in which party will prevail. As usual for daily return analysis, variability tends to swamp anomaly.

The average daily return for all days in this interval is 0.10%, about three times the average return for all days since the beginning of 1950.

What is the cumulative effect of these daily returns?

The final chart is a close-up of average cumulative S&P 500 index returns from 21 trading days before through 21 trading days after U.S. national elections for the total sample across 1950-2008 and several subsamples. The roughly 2% rally from one week before election through one day after election is consistent. The average return for all two-month periods during 1950-2008 is about 1.3%.

In summary, there appear to be both long-term and short-term connections between the U.S. national election cycle and stock market performance, with presidential term year 3 (1) the best (worst) and a tendency for a brief election-time rally.

However, the subsamples for presidential term year analysis are very small, so confidence in related tendencies is very low.

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“In general, are you satisfied or dissatisfied with the way things are going in the United States at this time?”

Using Gallup polling results from PollingReport.com and contemporaneous S&P 500 Index and 12-month trailing S&P 500 operating P/E data for January 1998 through May 2010 (roughly monthly, but with some polling gaps), we find that:

The following chart shows the relationship between the S&P 500 index and the level of public sociopolitical satisfaction since 1998, encompassing part of the Clinton administration, all of the Bush administration and part of the Obama administration. The horizontal access for the polling schedule is irregular because of variations in polling frequency. It appears that there may be some relationship between the two series, but they trend in opposite directions during much of 2003 through 2007.

The large jumps at the beginning of 2009 are at least partly reflective of a large gap in polling activity during January through July of 2009.

Does sociopolitical satisfaction relate better to a valuation measure such as P/E, arguably a measure of investor risk aversion?

The next chart shows the relationship between the S&P 500 12-month trailing operating P/E and the level of public sociopolitical satisfaction over the period 1998 to the present. Again, the horizontal access for the polling schedule is irregular because of variations in polling frequency. There appears to be a relationship between these two series in terms of both broad trends and finer features. The Pearson correlation between them is 0.71.

Again, the large jumps at the beginning of 2009 are at least partly reflective of a large gap in polling activity during January through July of 2009.

For a different perspective, we use a scatter plot.

The following two scatter plots relate S&P 500 12-month trailing operating P/E to sociopolitical satisfaction over the entire sample period at the polling frequency (approximately monthly) and at an annual frequency (using polls nearest beginnings of years). The relationship between overall stock market valuation and broad public sentiment appears significant and perhaps non-linear.

At the polling frequency, an exponential (linear) best-fit curve yields an R-squared statistic of 0.53 (0.50), indicating that variation in sociopolitical satisfaction explains about half the variation in P/E.

At an annual frequency, the R-squared statistic is 0.50 for an exponential fit (but sample size is just 13).

Does one of these two series lead the other? When we offset the two series and calculate correlations for various lead-lag relationships, there is no indication that either series leads the other. For a closer look at potential exploitability, we relate change in satisfaction from poll to poll to the S&P 500 Index return from the latter poll to the next poll.

The following scatter plot relates the change in sociopolitical satisfaction from the last survey to a given survey to the change in the S&P 500 Index from the given survey to the next survey over the entire sample period. This test roughly (ignoring polling gaps) relates the change in satisfaction last month to the change in the S&P 500 Index next month.  The R-squared statistic is 0.00, indicating no relationship between change in satisfaction from poll to poll and near-term (approximately next month) stock market movement. Whatever trading behavior keeps sociopolitical satisfaction and S&P 500 12-month trailing operating P/E in step, it appears to be coincident (efficient) rather than predictive at the measured frequency.

Note that sample size is very small with respect to the numbers of annual earnings cycles and four-year political cycles.

In summary, evidence suggests that public sociopolitical satisfaction and stock market valuation move substantially in step, with no way to exploit the broad sentiment measure in stock market trading.

There may be an incentive for politicians to act in ways that create investment bubbles (elevated P/E) in the course of maximizing public sentiment.

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The following chart plots the average cumulative return of the S&P 500 index across the four years of the presidential term (Y1-Y4) for the entire sample period based on monthly data (M12=December). The return profile indicates that all of Y1 and most of Y2 tend to be approximately flat. Strong gains follow from late in Y2 to two-thirds through Y3. Moderate gains ensue through the end of the term.

For comparison, the chart includes a plot of the cumulative return of the S&P 500 index for the 2009-2012 presidential term through April 2010. This current term is atypical.

Is the average behavior persistent over time?

The next chart shows the average cumulative return of the S&P 500 Index across the four years of the presidential term for two equal subperiods (seven terms each), 1953-1980 and 1981-2008, again based on monthly data. The shapes of the two profiles appear similar enough to warrant cautious belief in some persistence.

In summary, evidence from a small number of presidential terms supports some cautious belief in three typical phases of the U.S. stock market: (1) flat at the beginning; (2) strongly advancing in middle; and, (3) moderately advancing at the end.

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Stock Trader’s Almanac covers a lot of ground. The description indicates that it includes a lot of reference data and that there is very likely overlap in anomaly coverage with CXOadvisory.com, especially in categories such as “Calendar Effects,” “Political Indicators” and Technical Trading.”

You can use these three categories (and key word searches) to identify similar analyses and thereby get a second opinion on specific anomalies.

Some general risks of data-intensive asset return analyses are:

1. Data snooping (mining) bias, both direct and indirect, which tends to incorporate luck into inference.
2. Assumption of normality of asset return distributions, which tends to overvalue statistical metrics (like mean, standard deviation and significance test outputs).
3. Look-ahead bias, which assumes knowledge not available in real time.

Realistic accounting for trading frictions and use of capital are other risks. For further discussion, see “The Demon’s Drudgery”.

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The following chart plots for comparison SEC outlays and the S&P 500 Index for federal fiscal years since 1990 for SEC outlays and since 1986 for the stock index. SEC outlays for fiscal years 2009 and 2010 are estimates. Visual inspection offers a hint that SEC spending tends to ramp up after bear markets and moderate after bull markets.

For a closer look, we relate annual changes in SEC outlays and S&P 500 Index annual returns for various lead-lag scenarios.

The next chart plots Pearson correlations between the change in SEC outlays and the return on the S&P 500 Index by fiscal year based on the above data for lead-lag relationships ranging from SEC outlays lag stock returns by four years to SEC outlays lead stock returns by four years. Results suggest a negative correlation between stock returns and spending changes one to three years later. In other words, when the stock market declines (advances), SEC spending tends to rise (decline or flatten) over the following one to three years. There is no convincing relationship between SEC spending changes and same-year or future-year stock returns.

How convincing is the two-year lag scenario?

The following scatter plot depicts the relationship between S&P 500 Index returns and SEC spending changes two years later based on the above data. The plot is somewhat well-organized but the sample is small. The R-squared statistic for the relationship is 0.15, indicating that the stock market return for a given year explains 15% of the change in SEC fiscal year spending two years hence.

In summary, very limited evidence suggests that regulatory activity reacts to stock market returns with a lag of one to three years and has little or no effect on future stock market returns.

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Over the entire sample period, the U.S. Senate is in session 4,742 trading days and out of session 3,193 trading days. Activity varies with the two-year election cycle and with unusual events.

The following chart summarizes average daily open-to-open S&P 500 index returns when the U.S. Senate is in session and out of session for various conditions over the entire sample period. The number in parentheses after each condition is the number of years during which the condition exists. Some subsamples are small. “All Days” represents average daily returns weighting the return for each day equally. “All Years” represents the average of the average daily returns calculated by calendar year. Results for Democratic (D) and Republican (R) control of the Senate and the Presidency are on an “All Years” basis. Results broadly suggest that:

• Whether weighted by day or calculated and weighted by year, there is practically no difference in average daily returns while the Senate is in session and out of session.
• Differences for in-session/out-of-session average daily returns are modest for the small subsamples defined by party control of the Senate and Presidency.

The standard deviation of daily returns over the entire sample period, on an “All Days” basis, is 1.10% (1.12%) while the Senate is in (out of) session, indicating no material difference in return volatility.

In summary, in contrast with cited research, limited tests do not support a belief that the stock market reliably generates higher and less volatile returns when the U.S. Senate is not in session.

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The following chart summarizes average annual returns for various combination of party in power over the entire 60-year sample period. The number in parentheses after each combination is the number of years during which the combination exists. Some subsamples are very small. Results broadly suggest that stocks do better when Democrats (Republicans) control the Presidency (Congress).

The various combinations suggest that Republican control of the Senate may be most decisively favorable for stocks, with:

• Average performance strong when Republicans control the Senate; and,
• Outperformance (underperformance) under Democratic (Republican) presidents associated with Republican (Democratic) control of the Senate.

The current combination of Democratic control of both the Presidency and Congress relates historically to average stock market performance.

In summary, limited evidence suggests that Republican control of the Senate is most favorable for stocks, and that the current Democratic control of the Presidency and Congress relates to average stock market performance.

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We exclude from the sample the inauguration of 1933. The New York Stock Exchange shut down for several days after inauguration day in 1933 because of the mandatory national bank holiday initiated immediately after that date.

The following chart shows the average daily DJIA returns from five trading days before (-5) through five trading days after (+5) inauguration days for 1929-2005, with one standard deviation variability ranges. Day 0 is inauguration day. When inauguration day is on a Saturday, we include no returns for that day. The mean daily return for all trading days in the sample is 0.02%. Results on average suggest abnormal strength three to four days before inauguration day and weakness from two days before through inauguration day. Volatility on average tends to be high the day before inauguration and low on inauguration day. As usual for daily data, noise generally dominates signal. Sample size is small.

Does persistence in the pattern over time support belief in it?

The next chart compares the average daily DJIA returns from five trading days before through five trading days after inauguration days over the first (before 1970) and second (after 1970) halves of the sample period. This chart has no error bars and uses a finer vertical scale than the preceding chart. The patterns for the subperiods are similar, supporting belief in the reliability of the abnormal returns.

Does the party assuming power matter?

The final chart compares the average daily DJIA returns from five trading days before through five trading days after inauguration days for inaugurations of Democratic (D) and Republican (R) Presidents. Patterns suggest stronger investor reactions to Republicans taking power than Democrats.

In summary, best guess is the U.S. stock market will show first relative strength and then relative weakness in the week preceding presidential inauguration days (less so for Democrats). The week after inauguration tends to be quiet.

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• There is a significant relationship between aggregate U.S. stock market P/E and presidential approval rating. The R-squared statistic for a simple regression indicates that changes in P/E explain about 14% of the variation in presidential approval.
• A one-point increase in P/E relates to a contemporaneous (long-term) increase in presidential approval rating of 0.62% (1.9%).
• P/E has greater explanatory power for presidential approval rating than post-election honeymoon, scandals, military activity, unemployment and inflation.

This connection between aggregate valuation and political approval perhaps implicitly quantifies an incentive for politicians to foster equity investment bubbles, with the hope that no bubble bursts during their incumbencies. In other words, the direction of causality is not obvious.

In summary, stock market P/E and presidential approval rating are significantly intertwined.

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