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The Presidential Puzzle as a Candidate Stable Stock-Market Dependence

Article Quant Q&A · Author: vonjd
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Summary

The document asks for stable, nontrivial dependence patterns in financial data, beyond direct relationships such as a derivative and its underlying. The accepted answer offers the Presidential Puzzle as a candidate: research reported higher average excess stock-market returns during Democratic than Republican presidencies. It presents the finding as an example of a cross-sectional or time-linked pattern that attracted attention because it appeared to persist across an additional out-of-sample period.

The response also notes later work that proposes an economic explanation through a general-equilibrium model, connecting party choice, fiscal redistribution, risk aversion, and expected returns. This gives the pattern both empirical and theoretical context, but the document does not provide the underlying datasets, statistical tests, or a fresh replication. It is therefore an illustrative research lead, not evidence that the relationship is causal, universal, or dependable for trading. The initial question's broader search for robust dependence structures remains open.

Key ideas

  • The Presidential Puzzle describes reported differences in stock-market excess returns across presidential party affiliation.
  • The answer presents persistence across a later period as evidence that the finding merited attention.
  • A proposed explanation links political choices and fiscal redistribution to risk aversion and expected returns.
  • The document offers the puzzle as an illustrative pattern rather than a tested trading strategy.
  • The evidence described does not establish causality or guarantee stability in other samples.

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EquitiesStatisticsMachine learning
Full text
# What is the most stable, non-trivial dependence structure in finance?


# What is the most stable, non-trivial dependence structure in finance?












The highest rated answer to the question on What concepts are the most dangerous ones in quantitative finance work? is this one:

> Correlation Correlations are notoriously unstable in financial time series [...]

My question My question is a little bit broader than just about linear dependence, it is: What is the most stable, non-trivial dependence structure in financial data?

With non-trivial I mean that I don't want answers that are about direct connections, e.g. between derivative and underlying.

The dependence structure can be either cross-sectional or through time with univariate time series, it can also be non-linear.

The context of my question is that I am preparing the documentation for a new machine learning R package I wrote and I am looking for a good showcase in the financial sphere. Now this is not a trivial feat given that correlations are notoriously... see above ;-)

## Answer by phdstudent (score 10, accepted)

https://quant.stackexchange.com/a/38916

It is hard to find a stable non-trivial dependence structure in financial data. Usually when such is found it is hard to rationalize.

One of my favorite (although I am sure there are others) is the so called "Presidential Puzzle". This is an old finding by Santa-Clara and Valkanov (2003) They find that "

> Excess return in the stock market is higher under Democratic than Republican presidencies: 9 percent for the value‐weighted and 16 percent for the equal‐weighted portfolio.

At the time the finding was very robust and did not seem to be explained by anything else. What is more impressive is that 12 years later the result still holds true. We now have a very good out-of-sample period. This is confirmed by Pastor and Veronesi (2017) recent work. More interesting, they rationalize the finding by building a continuous-time general equilibrium model and conclude that:

> When risk aversion is high, agents are more likely to elect the party promising more fiscal redistribution. The model predicts higher average stock market returns under Democratic than Republican presidencies, explaining the well-known “presidential puzzle.”

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This summary was written by Stratmill's research agent from the original; it is not a copy of the source.