Skill vs. Luck in Wall Street

Investors can track, to a hundredth of a percentage point, how individual Wall Street analyst’s stock picks perform. But they are largely powerless in determining the degree to which an analyst’s results are a function of skill—and how much they are attributable to plain luck. If we accept that a big part of investing success is luck, not skill, then investors should go with index funds in lieu of following the advise of Wall Street analysts.[1] However, this argument is in contrast to the investment community and financial press’ interest in the selection of “All Star” analysts, and their reliance on analyst rankings. The assumption is that information provided by analysts has value for investment decisions (e.g. The Wall Street Journal, “Best on the Street: Analyst Survey”; “Institutional Investor Analyst Survey”).

iStock_000006439326XSmall Stock chart sizedThis value may arise from analyst earnings forecasts, which are a major input into analyst stock recommendations.[2] [3] Loh and Mian find that investors who follow the investment advice (i.e., buy/sell recommendations) of the most accurate earnings forecasters earn abnormal returns.[4] This contemporaneous relation is motivated by theories of fundamental analysis (e.g., Ohlson[5])—if the analyst’s estimates of future earnings (inputs into a valuation model) are better, the analyst can potentially translate them into better target prices or stock recommendations (valuation outputs).

For the contemporaneous result to hold trivially in the predictive setting, however, forecast accuracy must be persistent—the impact of skill vs. luck.[6] In this study we seek to gain a better understanding of the persistence in the relative earnings forecast accuracy of Wall Street All Star analysts over time and whether it is related to future stock recommendation profitability. In other words, we are interested whether it is possible for individual investors to identify in advance analysts who are truly skilled and not just lucky.

A Measure Of Stock Picking Skill

To measure a stock picking skill, we develop a two-step process. We begin by calculating a measure of analyst forecast accuracy for all analyst-firm-year observations in our sample. A novel feature of our accuracy measure is that we control for forecasting complexity—the dispersion in analyst beliefs about a firm’s future earnings. We argue that if forecasting complexity was not controlled for in this manner, analysts covering firms whose earnings are more predictable would be classified as accurate, while analysts covering firms with less predictable earnings would be classified as inaccurate. In short, we would be following the recommendations of analysts covering firms with predictable earnings, rather than following the recommendations of accurate forecasters across all firms. Our measure, analyst i’s forecast accuracy of firm j’s earnings per share (EPS) for period t, Accuracyi,j,t., is defined as:

Simon formula

where Actualj,t is firm j’s actual EPS for period t, Forecasti,j,t is analyst i’s forecast of firm j’s EPS for period t, abs is the absolute value operator, and stdevj,t [Forecasti,j,t)] is the standard deviation analyst one-year ahead earnings estimates of firm j for the fiscal period t t.[7]

Next, for each analyst i in each year t, we compute the mean value of all forecast errors across all firms covered by the analyst, denoted by Accuracy_Meani,t. That is, we focus not on analyst predictions for a particular firm’s earnings, but rather on the analyst-level data. This distinction between firm-level and analyst-level accuracy is crucial. An appropriate question for a portfolio manager to ask may be: “given this analyst did a great job for me on one company, should I trust him on other names?” Thus, for example, if analyst Joe’s 2005 EPS forecasts for IBM, Microsoft, and Cisco are 2, 5, and 0, and the respective actual EPS are 1, 2, and 9, then Accuracy_MeanJoe,2005 equals 1.17 (= (abs(1-2)/1 + abs(2-5)/2 + abs(9-0)/9)/3 = (1+1.5+1)/3).

The aggregate set of analyst Accuracy_Meani,t results per year is then ranked from lowest to highest in terms of accuracy. Specifically, we sort all Accuracy_Meani,t observations into five portfolios (i.e. sets) such that portfolio (1) includes observations with the lowest Accuracy_Meani,t, i.e., the most accurate analysts, and portfolio (5) includes observations with the highest Accuracy_Meani,t, i.e., the least accurate analysts. Note that we require that each analyst covers at least three firms per year so that the mean and median calculations are meaningful. We also require three years of mean information for each analyst so that the time-series tests are meaningful. With the above sorts in hand, we can analyze analysts’ relative forecast accuracy.

We obtain analyst All-Star rankings from the annual Institutional Investor Survey. Analysts’ annual earnings forecasts, matching firms’ actual earnings, and the statistical measure of earnings forecast dispersion from the unadjusted Institutional Brokers Estimate System (I/B/E/S) Detail Files. Stock price data is from the Center for Research in Security Prices (CRSP). Our final sample includes 2,034 analyst-year observations over the period 1987-2012.[8]

Spotting a Winner

Figure 1 plots the transition probabilities across portfolio quintiles of relative forecasting accuracy after controlling for forecasting complexity.[9] The current- and subsequent-period ranks are represented by the horizontal axes, and the probability of migration is represented by the vertical axis. The area above the diagonal represents those analysts with the highest persistence in relative forecasting accuracy. The flat shape of the curve, especially the relatively flat area above the diagonal, clearly demonstrates that relative forecasting accuracy is only weakly persistent over time, that is, superior analyst forecast accuracy in the current period does not necessarily mean that this analyst will continue to outperform other analysts in future periods.

A possible explanation for our finding is that analysts’ contribution to financial markets goes beyond what empiricists can observe. The Institutional Investor survey shows that institutions do not care much about either earnings forecast accuracy or stock recommendation profitability when evaluating sell-side analysts. The number one item that institutions value in analysts is always industry and company knowledge. Thus, analysts that show persistence in the relative forecast accuracy, after controlling for forecasting complexity, may expand greater effort to understand the companies’ industries companies, and their future prospects.

Figure 1 Simon

Figure 1: Transition Diagram between Current and Future Quintile Ranking

In the next section we show that this reduced persistence, however, measures true forecasting ability. In other words, in the short-term analysts may experience good or bad luck (and that can overwhelm skill), but in the long-term luck tends to even out and skill determines results.

Performance of Skill vs. Luck

For the five accuracy portfolios we calculate average recommendations for each stock followed by the analysts in each portfolio. Our portfolios are long stocks in which the consensus recommendation is favorable and short stocks in which the recommendation is unfavorable. Thus, we report the returns of long–short portfolios, which are essentially zero-investment portfolios formed by buying the long portfolio and selling short the short portfolio of each analyst group.

Due to the skewed distribution of analysts’ recommendations, stocks are included in the favorable (long) portfolio when their average recommendation is greater than or equal to four (i.e. a buy recommendation or better). Stocks are included in the unfavorable (short) portfolio if the average recommendation is less than or equal to 3.5 (i.e. between a hold and underperform or worse). We exclude all stocks with an average rating that is greater than 3.5 but less than 4. Recommendations are used until they are reiterated/revised or become stale (i.e. maximum life of 6 months).

This portfolio formation process is conducted prior to the first trading day in year t and the portfolio is only rebalanced when changes in average firm recommendations, caused by the exclusion of stale recommendations or the issuance of new recommendations, moves a firm in or out of the favorable (long) or unfavorable (short) portfolios. Portfolio value-weighted returns are then calculated at the end of each trading day and compounded to monthly returns. The returns to these portfolios are adjusted for four risk factors: the market factor, the size and book-to-market factors proposed by Fama and French,[10] as well as the Carhart[11] momentum factor.

Figure 2 depicts the combined effect of the quality, alphas, of favorable and unfavorable recommendations of each persistent group after controlling for persistence in the relative forecast accuracy and after controlling for forecasting complexity. The figure shows that the high accuracy persistence portfolio produces a positive four-factor-adjusted return of 0.15 percent (p-value < 0.0001) per month. In contrast, the low accuracy persistence portfolio earns factor-adjusted returns of –0.23 percent (p-value < 0.0001), implying analysts are more likely to be inferior forecasters than superior forecasters.

Moreover, one could formulate a hedged investment strategy by buying the high accuracy portfolio and shorting the low accuracy portfolio to earn a monthly return of 0.38 percent (p-value < 0.0001). This monthly abnormal return is equivalent to a compounded annual abnormal return of 4.7 percent. To highlight the importance of our results we also report returns investors would earn by simply following the recommendations of analysts who were most accurate in the prior year. We note that abnormal returns are insignificantly different from zero without taking into account our complexity control measure. This finding is similar to that of Hall and Tacon[12] and confirms that past short-term earnings forecast accuracy alone is not a good predictor of the future profitability of stock recommendations. Overall, these results suggest that identifying persistence in superior analyst forecast accuracy based on a measure that controls for forecasting complexity is powerful in identifying future profitable recommendations.

UPDATE Figure 2 Simon GBR

Figure 2: Annualized Long-Short Abnormal Trading Strategy Returns

Summary and Guidance

We show that it is possible to identify ex ante Wall Street analysts who are truly skilled and not just lucky. To do so, however, investors must focus on the decision making process that analysts use and identify numerical measures that can be proxies for that process. We propose a measure of relative forecast accuracy over time controlling for forecasting complexity. Analysts that show persistence in relative forecast accuracy, after controlling for forecasting complexity, may expand greater effort to understand companies’ industries and their future prospects. This effort is reflected in the profitability of their stock recommendations. Thus, over longer periods, skill has much better chance of shinning through.

While no publicly available ranking system matches the feature of controlling for forecasting complexity, other research (e.g. Li, X.[13]) has shown that simply following the analysts whose recommendations consistently generate profits can be profitable. For example, Zacks Investment Research, Inc. assigns an All-Star Analyst Ratings to analysts on the basis of their stock-picking skills.[14] Specifically, in each industry, at the end of each year, the analysts are ranked into quintiles by the performance of their equally-weighted model portfolio during the year. Analysts that maintain a top Zacks rating (5 Stars) for multiple periods—would be a reasonable approximation for our study.

 


[1] Fama, E. and French, K. (2010). “Luck vs. Skill in the Cross-Section of Mutual Fund Returns,” The Journal of Finance 65 (5): 1915-1947. Fama and French argue that fees paid for active investment management might be wasteful, and hence in the long run passive investment may be superior to active investment. However, some funds show nonzero alphas.

[2] Bradshaw, M. T. (2004). “How do analysts use their earnings forecasts in generating stock recommendations?” The Accounting Review 79 (1), 25-50.

[3] Simon, A. and Curtis, A. (2011). “The Use of Earnings Forecasts is Stock Recommendations: Are Accurate Analysts More Consistent?” Journal of Business Finance & Accounting 38 (1, 2): 119-144.

[4] Loh, R. K., and Mian, G. M. (2006). “Do Accurate Earnings Forecasts Facilitate Superior Investment Recommendations?” Journal of Financial Economics 80 (2): 455-483.

[5] Ohlson, J.A. (1995). “Earnings, Book Value and Dividends in Security Valuation,” Contemporary Accounting Research 11(2): 661-687.

[6] Loh and Mian (2006) note that their model cannot predict the profitability of future recommendations as they measure forecast accuracy and recommendation profitability contemporaneously.

[7] The standard deviation of the absolute forecast error captures the fact that the ability of an analyst to forecast a firm’s operations varies across firms. For example, predicting the performance of firms that are more transparent, have higher accruals quality, are less prone to manipulation, and have less volatile income is more simple and thus analyst forecasts for these firms are likely to demonstrate lower forecast errors. We refer to this effect as forecasting complexity.

[8] In their October issue, Institutional Investor Magazine selects the three best analysts for the year. The ranking is based on a survey sent to institutional investors and includes criteria such industry knowledge, client relations, earnings forecast accuracy, and recommendation profitability. Prior research suggests that All Star analysts are more accurate than their peers.

[9] This is a non-parametric approach which consists of simply estimating probabilities based on relative frequencies of analyst accuracy observed in two consecutive years.

[10] Fama, E. and French, K. (1997). “Industry Costs of Equity,” Journal of Financial Economics 43, 153-193.

[11] Carhart, Mark M. (1997). “On Persistence in Mutual Fund Performance,” Journal of Finance 52 (1): 57–82.

[12] Hall, Jason and Tacon, Paul B. (2010). “Forecast Accuracy and Stock Recommendations,” Journal of Contemporary Accounting and Economics, Vol. 6, pp. 18-33.

[13] Li, X. (2005). “The Persistence of Relative Performance in Stock Recommendations of Sell-side Financial Analysts,” Journal of Accounting and Economics 40 (August): 129−152.)

[14] Zacks Investment Research, Inc, http://www.zacks.com/research/allstar/explained.php.

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The Four-Year U.S. Presidential Cycle and the Stock Market

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The Four-Year U.S. Presidential Cycle and the Stock Market

This article revisits the 2004 article, “Presidential Elections and Stock Market Cycles,” written by Marshall Nickles. That article found that all of the major stock market declines occurred during the first or second years of the four-year U.S. presidential cycle. No major declines occurred during the third or fourth years. More specifically, from 1950 to 2004 (using the Standard and Poor’s 500 Index), the most favorable period (MFP) for investing was from October 1 of the second year of a presidential term to December 31 of the fourth year. The remaining period—from January 1 of the first year of the presidential term to September 30 of the second year—was the least favorable period (LFP) for stock market investors. It appeared that politicians were anxious to exercise policies that were designed to pump up the economy just prior to a presidential election, which in turn had a positive affect on stock prices.[1]

Since 2004, the stock market environment has changed in ways that make it more important than ever to understand the relationship between politics and stock market behavior. Unlike the 2004 article that did not address the above in detail, this article will attempt to do so. More directly, we will focus on two broad issues. First, we provide evidence of the relationship between economics, politics, and the four-year presidential cycle; and second, we include an analysis of stock market performance during the 2008 period. In addition, we introduce a risk measurement for the stock market and argue that the 2008 stock market crash should be considered an anomaly. Finally, we conclude that the four year presidential stock market cycle is likely still in tack.

This article does not attempt to support or refute the Efficient Market Hypothesis, which states that it is not possible to “beat the market.” The academic supporters of this hypothesis believe that stocks always trade at a fair market value; therefore, it is unlikely to outperform the general market unless one assumes more risk. Rather, this article provides evidence that risk may be reduced and returns may increase when an investor considers how economic policy influences stock market prices during the presidential election cycle.

Relationship Between Politics and Economics

The 20th amendment to the U.S. constitution requires a presidential election to take place every four years, which turns out to be all years that are divisible by four (e.g. 2004, 2008, and 2012). The president assumes office the following January after the election. Once presidents take office, they realize that to get reelected they must try to make the economy as healthy as possible four years later. It is this consistency in the U.S. political process that also sets into motion fiscal policies that are frequently predictable and that often have a direct effect on the stock market. In the discipline of economics, fiscal policy is defined as an increase or decrease of taxes and or government spending. The direction that fiscal policy takes can often be directly related to the state of the economy at the time a new president is elected.

It is not surprising to see incumbent presidents push for votes by proposing tax reductions and or increasing spending on specific government programs as an election draws near. In addition, an incumbent political party may also try to persuade the Federal Reserve to complement the administration’s efforts through monetary policy, by increasing the money supply and reducing interest rates. Such fiscal and monetary policies may be introduced as early as the end of the second year of the presidential four-year term. If the results are favorable and the economy responds positively, corporate profits usually rise, and with them, stock prices—just in time for the next presidential election.

These policies can also lead to inflation, which can be disconcerting to investors. If this were to happen, a newly elected president could be pressured to reverse the fiscal and monetary stimulus policies of his or her predecessor, attempt to get inflation under control, and then hope to return to stimulus policies by midterm in preparation for the next election.

On the surface, the concept of inflation appears to be straightforward. That is to say, inflation is understood to mean rising prices. However, the real questions an investor should ask are what caused it, why can it ultimately be a negative for the stock market, and what can be done to reduce it. First, if inflation is the result of excessive fiscal and/or monetary stimulus—known as demand-pull inflation—simply reversing the stimulus policies can help to lower inflation. If, however, rising prices are caused by external factors like rapidly increasing global oil prices—known as cost-push inflation—it can be much harder to control. Since the mid-1980s, the U.S. economy has not seen much cost-push inflation.

When the Federal Reserve increases or decreases interest rates, it is often to combat inflation, to position the U.S. dollar for favorable international trade, or to address a weakening economy. The consequences of rising interest rates are increased costs for businesses and consumers, which in turn can slow aggregate spending and corporate profits, and ultimately depress stock prices.

The above sequence of events appears to be logical and may be taken for granted by many investors, but what they may fail to understand is that the sequence does not always work in lock step. In other words, the effects of rising interest rates often lag in its efficacy and may not have an immediate negative influence on the economy. This lagged relationship between rising interest rates, falling corporate profits, and ultimately declining stock prices can confuse unaware investors. This is because corporate profits can, for a period, continue to increase faster than the negative effects of rising rates. Simply put, over time rising interest rates can put downside pressure on stock prices. However, in the early stages it may not be entirely obvious what is happening to all but the most sophisticated investors, who can bid down stock prices in concert with the anticipation of falling corporate profits.

The inverse is also true: any effort to curb recessions with lower interest rates can have a lagged effect depending on the state of the economy and the magnitude of the decrease in interest rates. The lag effects on the economy can be as late as 6 to 18 months later, although it is often sooner for the stock market.[2]

Risk and the Presidential Cycle

If indeed policy makers are successful in exerting positive influences on the economy as elections approach, it should be logical to expect less volatility in the stock market. This led us to measure the relative changing levels of volatility (i.e. risk) between the first and second halves of presidential cycles. In addition, risk becomes greater the longer an investor is committed to the stock market. Therefore risk reduction may also be accomplished if one were invested for only approximately the second half of the U.S. four-year presidential cycle or about 50 percent of the time.

To verify our claim, we measured risk within presidential cycles since the 1950s. For the purpose of this study, risk is defined as market exposure to time and volatility. The Ulcer Index (UI), which was developed by Peter Martin and Byron McCann, measures such risk.[3] The UI measures the depth and duration of Draw-Downs (DD) from recent stock market peaks.[4] A Drawn-Down measures the peak-to-trough decline during a specific period for the stock market and is usually quoted as a percentage between the peak and the trough.[5] The lower the UI value the lower is the risk.

Figure 1 compares the average Ulcer Index for the first and second years of presidential cycles to that of the third and fourth years since 1950. The Ulcer Index in most presidential periods was higher in the first and second years, with just a couple of significant exceptions (1985 to 1988 and the most notable 2005 to 2008). In general however, the investment risk was higher in the first two years of the presidential cycle, consistent with underperformance of the stock market during that period.

Figure 1. Historical Ulcer Index Average during Presidential Periods

Historical Ulcer Index Average during Presidential Periods

Note: This figure shows the average Ulcer Index for the first two years of each presidential period, compared to the average for the third and fourth years.


Favorable and Unfavorable Periods

With the above in mind, one should see higher performance for the stock market in the form of favorable and unfavorable periods within stock market cycles. To analyze the historical performance of stock price behavior, we opted to use the Dow Jones Industrial Average (DJIA) instead of the commonly used Standard & Poor’s 500 Index (with a ticker symbol of SPX). The specific reasons for selecting the DJIA were as follows: First, it is recognized as a leading measure of the general market, is well published and quoted, and has been around the longest among all the general stock market measures. Second, the DJIA appears to be less risky than other popular stock market indexes like the SPX. During the period 1950 through 2011, the UI for the DJIA was 13.5 while it was 15.2 for the SPX. Third, since the S&P 500 was used in the 2004 article, the authors wanted to use another measure to validate earlier research.

We believe that an expanding level of liquidity (i.e. money) in the economy, combined with a downward trend in interest rates, are major drivers of stock market performance within the four-year presidential cycle. However, the relationship appears to be a lagging one as discussed earlier. Figure 2 shows yearly DJIA growth and lagged interest rates since the 1950s. By lagging interest rates one year, the correlation to stock price behavior becomes more obvious.

The graph shows that within the presidential cycle interest rates tend to go down in advance of the next election. We confirmed this by performing a time-series analysis which shows that, on average, interest rates in the third and fourth year of the presidential period are 0.55 percentage points lower than in the first and second years. (See Appendix 1). Figure 2 also shows that most breaks in the interest-rate reduction cycle occur soon after elections. This is consistent with the notion that right after the elections there can be pressure to raise interest rates to curb any potential inflation. There were a couple of exceptions (1977 to 1980 and 1993 to 1997), but the trend is there. In summary, we provide core evidence of the relationship between changing interest rates and the four year cycle performance.

Figure 2. Dow Jones Industrial Average Growth and Interest Rates across Presidential Periods

Dow Jones Industrial Average Growth and Interest Rates across Presidential Periods

Note: Interest rates are depicted with a one-year lag to illustrate the lagged effect of a given rate on the market.


Figure 2 also depicts the DJIA growth cycle since 1950. Note that in most presidential periods, the valleys in DJIA performance occur in the first and second years of the election periods. There were a few significant exceptions in which performance was higher in the first two years than in the last two (e.g. 1985 to 1988, 1997 to 2000, and 2005 to 2008). Nevertheless, the above facts substantiate the claim in the first article on this topic.[6] More specifically, the stock market typically underperforms in the first and second years of the presidential period, relative to the third and fourth years. To further verify our finding, we performed a time series econometric analysis, which confirms that DJIA growth is on average 7.2 percentage points higher in the third and fourth years of the presidential periods than in the first two years. (See Appendix 2.)

In summary, all of the previous evidence appears to be consistent with our claims: Policies to stimulate the economy as presidential elections approach strongly contribute to the performance of the four-year cycle. Further, there is now sufficient data that the most favorable periods (MFPs) can be validated using two stock market indicators, the SPX and the DJIA. This analysis was also corroborated by showing how the Ulcer Index (i.e. risk) was reflected in the cycles. Finally, Figure 2 shows how the four-year presidential cycle is affected by downward pressure on interest rates.

The 2008 Anomaly

Based on the earlier analysis, it would make sense to invest in the DJIA at the beginning of the favorable period and steer clear of the market during the unfavorable period. This would have been the strategy of choice from 1950 to 2004. However, the negative economic events surrounding the last 2008 presidential election temporarily broke that long-standing trend.

The period from late 2007 through early 2009 was the worst economic crisis the U.S. had seen since the depression of the 1930’s. Like the Great Depression, the 2008 economic environment was an attack on America’s financial structure. There appeared to be at least two core issues that precipitated the most recent economic and stock market upheaval. First, the stock market needs liquidity (i.e. money) steadily flowing through the economy to be profitable. That did not happen in 2008 when several major U.S. banks had to write off large amounts of defaulting mortgage loans. This temporarily dried up liquidity and required immediate Federal Reserve action. Second, the near collapse of the U.S. capital markets spread contagion worldwide. This in turn dramatically slowed economic activity globally, which ultimately put significant downside pressure on U.S. and foreign corporate earnings and both the stock and bond markets.

Because the severity of the 2008 crisis had not been seen during the 1950 to 2004 period, it must be considered an economic and stock market anomaly. Therefore, we do not believe that 2008 is representative of a long-term interruption in the past behavior of the four year presidential cycle. We do believe however, that in an environment with more globally connected financial markets where information flows electronically at a much faster speed, a phenomena that can temporarily break the historically consistent cycle that had existed for 54 years, has been augmented.

Putting the Most Favorable Period to the Test

Because the 2005 to 2008 period is an exception, considering the favorable period of the four-year presidential cycle as an investment strategy should still make sense. Figure 3 shows the current value of a 1953 initial investment of $1,000 based on three investment strategies. Investing in the DJIA during the MFPs and at commercial paper rates during the LFPs yielded a 20,468 percent return. Investing in the DJIA during the LFPs and at commercial paper rates during the MFPs yielded a 519.8 percent return. Investing in the DJIA as a long-term buy-and-hold strategy yielded a 4,408.6 percent return. Notice that the 2005 to 2008 period affected all investment strategies negatively. However, for the MFP investor, the losses from that period were almost completely offset by the gains since the 2009 stock market bottom.

Figure 3: Returns Based on Three Investment Strategies

Returns Based on Three Investment Strategies

Notes. Returns reported include dividends. Investment strategies are:
1. Most Favorable Period Investor: Invests in DJIA during the MFPs and at commercial paper rates during the LFPs.
2. Least Favorable Period Investor: Invests in DJIA during the LFPs and at commercial paper rates during the MFPs.
3. Long-Term DJIA Investor: Invested $1,000 in DJIA in 1953 and has not sold to-date.


Conclusion

The evidence provided in this article shows a propensity for the DJIA to rise during the second half of the four-year presidential cycle. We have discussed why we believe this pattern has been repetitive since 1950. Borrowing from the 2004 study, we adopted the most favorable period within the four year cycle. It begins on October 1 of the second year of the presidential term through December 31 of the fourth year, the favorable period or (MFP). This period performed much better than the unfavorable period, from January 1 in the first year of the presidential term through September 30 of the second year.

However, cycles of any type are not perfectly aligned all the time. This became evident when we attempted to match changes in interest rates and market volatility within the four year cycle. Occasionally this type of market behavior is to be expected and can usually be explained. A recent example is the economic problem in Europe. The point is that there are positive and negative macroeconomic events that can temporarily break a long standing MFP cycle. Even with a history of positive gains in the MFPs from 1950 to 2004, the 2008 market collapse, precipitated by domestic and global economic events, was too powerful for the market to overcome. Although the above was clearly an anomaly, we do not believe it will be the only exception in the future. Globalization and the internet are but two reasons for volatility and uncertainty in the years to come. However, even with the temporary break during the 2008 period, it does not mean that the favorable cycle will not resume. In fact we believe it already has. When we compared the favorable to the unfavorable period for the post 2008 time frame, the DJIA has again performed to date better during the favorable period. In conclusion, we feel that with the advent of merging international markets and modern technology, the more the average investor knows about the interrelationship between politics, economics, and the stock market, the more equipped he or she will be.


Appendix 1

We performed a Prais-Winsten time series analysis of a yearly dataset to account for autocorrelation:

Interest = β1*PresYear + β2 * Inflation + ε, where Interest is the average Federal Funds rate per year; PresYearDummy is a dummy variable with a value of 1 for the third and fourth years of each presidential period, and 0 otherwise; and Inflation is the yearly inflation rate.

The results are:


Appendix 2

We performed a Prais-Winsten time series analysis of a yearly dataset to account for autocorrelation:

ln(DJIA) = β1*PresYear + β2 * GDP + ε, where DJIA is the Dow Jones Industrial Average; PresYear is a dummy variable with a value of 1 for the third and fourth years of each presidential period, and 0 otherwise; and GDP is the real GDP per capita per year.

The results are:





[1] Nickles, Marshall. “Presidential Elections and Stock Market Cycles: Can you profit from the relationship?” Graziadio Business Review, 7 no. 3 (2004). Retrieved from 7(3) http://gbr.pepperdine.edu/2010/08/presidential-elections-and-stock-market-cycles.

[2] McConnell, Campbell, Stanley Brue, and Sean Flynn. Economics, 18th Ed. New York: McGraw-Hill Irwin, (2009), pp. 671 – 679. Also see: Thorbecke, W. “On stock market returns and monetary policy.” Journal of Finance, 52, (1997) 635 – 654. Doi: 10.1111%2Fj. 1540-6261.1997.tb04816.x.

[3] Martin, P.G. and McCain, B.B. “Financial Dictionary,” (2009). Retrieved August 17, 2012 from www.investors.com/Financialdictionary/term/ulcer_index_ui.asp.

[4] Anonymous. “FinancialDictionary,” (2012). Retrieved August 15, 2012, from www.investors.com/FinancialDictionary/Term/Drawdown.asp.

[5] Ibid.

[6] Nickles, “Presidential.”

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