Political Indicators

A subscriber requested, as in “Combine Market Trend and Economic Trend Signals?”, testing of a strategy that combines: (1) U.S. Economic Policy Uncertainty (EPU) Index, as described and tested separately in “Economic Policy Uncertainty and the Stock Market”; and, (2) U.S. stock market trend. We consider two such combinations. The first combines:

• 10-month simple moving average (SMA10) for the broad U.S. stock market as proxied by the S&P 500 Index. The trend is bullish (bearish) when the index is above (below) its SMA10 at the end of last month.
• Sign of the change in EPU Index last month. A positive (negative) sign is bearish (bullish).

The second combines:

• SMA10 for the S&P 500 Index as above.
• 12-month simple moving average (SMA12) for the EPU Index. The trend is bullish (bearish) when the EPU Index is below (above) its SMA12 at the end of last month.

We consider alternative timing strategies that hold SPDR S&P 500 (SPY) when: the S&P 500 Index SMA10 is bullish; the EPU Index indicator is bullish; either indicator for a combination is bullish; or, both indicators for a combination are bullish. When not in SPY, we use the 3-month U.S. Treasury bill (T-bill) yield as the return on cash, with 0.1% switching frictions. We assume all indicators for a given month can be accurately estimated for signal execution at the market close the same month. We compute average net monthly return, standard deviation of monthly returns, net monthly Sharpe ratio (with monthly T-bill yield as the risk-free rate), net compound annual growth rate (CAGR) and maximum drawdown (MaxDD) as key strategy performance metrics. We calculate the number of switches for each scenario to indicate sensitivities to switching frictions and taxes. Using monthly values for the EPU Index, the S&P 500 Index, SPY and T-bill yield during January 1993 (inception of SPY) through September 2020, we find that:

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What economic/financial variables are most useful in predicting re-election prospects for incumbent U.S. presidents? In the November 2012 revision of their paper entitled “Social Mood, Stock Market Performance and U.S. Presidential Elections: A Socionomic Perspective on Voting Results”, Robert Prechter, Deepak Goel, Wayne Parker and Matt Lampert analyze relationships between prior U.S. economic and equity market performance and incumbent performance in relevant U.S. presidential elections. They focus on incumbent national popular vote margin, but also consider for validation: percentage of total popular vote, percentage of total electoral vote, electoral vote margin and election wins/losses. They estimate annual U.S. stock market returns from November through October based on a modeled Dow Jones Industrial Average (DJIA) starting 1789 dovetailed with actual DJIA returns starting 1897. They look at Gross Domestic Product (GDP), inflation (producer price index, PPI) and unemployment as key economic performance measures. Using specified DJIA, economic data as available and election results during 1824 (first availability of popular vote results) through 2008, they find that: Keep Reading

“Stock Market and the National Election Cycle” examines the behavior of the U.S. stock market across the U.S. presidential term cycle (years 1, 2, 3 or 4) starting in 1950. Is a longer sample informative? To extend the sample period, we use the long run S&P Composite Index of Robert Shiller. The value of this index each month is the average daily level during that month. It is therefore “blurry” compared to a month-end series, but the blurriness is not of much concern over a 4-year cycle. Using monthly S&P Composite Index levels from the end of December 1872 through August 2019 (about 37.5 presidential terms), we find that:

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How does news flow interact with short-term stock market return? In their April 2019 paper entitled “Forecasting the Equity Premium: Mind the News!”, Philipp Adämmer and Rainer Schüssler test the ability of a machine learning algorithm, the correlated topic model (CTM), to predict the monthly U.S. equity premium based on information in news articles. Their news inputs consist of about 700,000 articles from the New York Times and the Washington Post during June 1980 through December 2018, with early data used for learning and model calibration and data since January 1999 used for out-of-sample testing. They measure the U.S. stock market equity premium as S&P 500 Index return minus the risk-free rate. Specifically, they each month:

1. Update news time series arbitrarily segmented into 100 topics (with robustness checks for 75, 125 and 150 topics).
2. Execute a linear regression to predict the equity premium for each of the 100 topical news flows.
3. Calculate an average prediction across the 100 regressions.
4. Update a model (CTM^Sw) that switches between the best individual topic prediction and the average of 100 predictions, combining the flexibility of model selection with the robustness of model averaging.

They use the inception-to-date (expanding window) average historical equity premium as a benchmark. They include mean-variance optimal portfolio tests that each month allocate to the stock market and the risk-free rate based on either the news model or the historical average equity premium prediction, with the equity return variance computed from either 21-day rolling windows of daily returns or an expanding window of monthly returns. They constrain the equity allocation for this portfolio between 50% short and 150% long, with 0.5% trading frictions. Using the specified news inputs and monthly excess return for the S&P 500 Index during June 1980 through December 2018, they find that:

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Do investors demand a risk premium for divided government because of the policy uncertainty of gridlock? In the April 2013 preliminary draft of their paper entitled “Divided Governments and Asset Prices”, Elvira Sojli and Wing Wah Tham„ investigate the effect of divided government on asset prices by comparing U.S. stock market performance in years of divided and undivided government. They define divided government (in the U.S.) as one party holding the Presidency while the other controls one or both houses of Congress. To isolate the effects of divided government, they account for the states of four variables widely used to predict stock market returns: dividend-price ratio; credit default spread, the difference between BAA and AAA corporate bond yields; term spread, the difference between 10-year U.S. Treasury note and 3-month U.S. Treasury bill (T-bill) yields; and, deviation of the 3-month T-bill yield from its one-year moving average. To determine causality, they investigate stock market futures reactions to betting market predictions of divided versus undivided government during four election nights. Using monthly U.S. stock market returns, data for the four stock return predictors and U.S. Treasury bill yields during 1926 through 2011 (encompassing 43 elections resulting in 23 undivided and 20 divided governments), and high-frequency election outcome betting data (from Intrade) and U.S. stock market futures on biennial election nights during 2004 through 2010, they find that: Keep Reading

Eric Singer, manager of the Congressional Effect Fund (CEFFX), introduces his 2012 book, Trade the Congressional Effect: How to Profit from Congress’s Impact on the Stock Market, by stating: “This book provides a new, empirically objective way to understand day by day what our government takes away from all of us. It shows in hard numbers what we lose out of our wallet when Congress acts. …this book suggests concrete investing strategies to make Congress’s systemic dysfunction work for you, and to hedge the risk and damage that Congress so casually and relentlessly inflicts on your life savings as represented by your portfolio and your house.” Using examples of legislative intervention and focusing on the daily level of the S&P 500 Index (capital gains only) during 1965 through 2011, he concludes that: Keep Reading

Do U.S. stock market returns have any connection to presidential election polling data? In other words, do investors act on the survey-indicated election prospects for the two major party candidates? Presumably, investors would tend to enter (exit) stocks if they thought the candidate with policies more (less) favorable for equity valuation were gaining ground. Using daily releases of Gallup head-to-head polling data for the 2008 presidential election (Obama-McCain for March 11 through November 2) and the 2012 presidential election (Obama-Romney for April 15 through October 16) and contemporaneous daily closing levels of the S&P 500 index, we find that: Keep Reading

Does political drama take over as the principal driver of U.S. stock market implied volatility during election seasons? In their March 2012 paper entitled “U.S. Presidential Elections and Implied Volatility: The Role of Political Uncertainty”, John Goodell and Sami Vähämaa compare the effects of political uncertainty to those of eight other sources of uncertainty on implied stock market volatility (as measured by VIX) during U.S. presidential election campaigns. They define the quadrennial campaign interval as the time from the beginning of February to the beginning of November of election years. They consider two measures of political uncertainty derived from the Iowa Electronic Markets: monthly change in probability of success of the eventual winner; and, monthly change in a measure of how close the race is. They also consider eight competing financial and economic sources of uncertainty as listed below. Using monthly data for these ten variables during the presidential election campaigns of 1992, 1996, 2000, 2004 and 2008 (40 total monthly observations), they find that: Keep Reading

Do equity markets respond predictably to the probability and fact of war? In their May 2011 paper entitled “The War Puzzle: Contradictory Effects of International Conflicts on Stock Markets”, Amelie Brune, Thorsten Hens, Marc Rieger and Mei Wang relate U.S. stock market returns to the estimated likelihood of war involving the U.S. as evidenced by analysis of news (key word counts in the New York Times, with one of the key words being “war”). They consider the six wars most costly to the U.S. since World War II (World War II, Korean War, Vietnam War, Gulf War, Iraq War and Afghanistan War). They distinguish between wars that have obvious preludes and wars that surprise (with the Korean War the paradigm for the latter). Based on availability of multiple measures of probability of war, they use the onset of the Iraq War during late 2002 through early 2003 to benchmark the stocks-war relationship. Using New York Times news reports during the selected wars and contemporaneous levels of the S&P 500 Index and the Dow Jones Industrial Average, they find that: Keep Reading

Are there reliable ways to exploit differences in asset class returns under Democratic and Republican U.S. presidents? In his April 2011 paper entitled “Is the 60-40 Stock-Bond Pension Fund Rule Wise?”, William Ziemba examines relationships between the U.S. presidential election cycle and long-run returns for several asset classes. Specifically, he investigates the differential performance of large capitalization stocks, small capitalization stocks and bonds when Democrats and Republicans hold the presidency. Using annual asset class return data for 1998 through 2010 to extend prior calculations for 1937-1997 and 1942-1997, he finds that: Keep Reading