Original paper

SSRN-id2903067.pdf1781.9KB

Abstract

We analyze shifts in political uncertainty and equity premiums over the U.S. Presidential election cycle. Somewhat ironically, it is midterm elections that turn out to be associated with the highest pre-election increases in uncertainty, leading to higher equity premiums post-midterm as the policy uncertainty and ex ante risk premiums decrease. We show this to have been the case for federal elections held over the past two centuries. We argue that the political uncertainty, which we measure primarily by the Economic Policy Uncertainty index, tends to be a tail risk that has previously been found to be compensated by higher premiums. We also find that the “lost CAPM” for expected equity returns reappears to fit returns amidst the post-midterm flood of public information, but not at other times in the Presidential cycle. We further show that the idiosyncratic volatility and lottery-demand puzzles disappear in post-midterm months.

Keywords: Political uncertainty; Midterm election; Presidential cycle; Lost CAPM; Idiosyncratic volatility

Trading rules

Target stocks listed under the Dow Jones Industrial Average, available through various investment vehicles like ETFs, funds, or futures.
In the year leading up to elections, initiate stock purchases at the start of November.
Retain these stocks until April concludes.
For the remaining duration of the election cycle, maintain your capital in the cash market.

Python code

Backtrader

import backtrader as bt
from datetime import datetime

class PreElectionStrategy(bt.Strategy):
    params = (('pre_election_years', [2020, 2024, 2028]),)

    def __init__(self):
        self.dji_etf = self.datas[0]  # Assuming DJI ETF is the first data feed

    def next(self):
        current_year = self.datetime.date().year
        current_month = self.datetime.date().month

        if current_year in self.params.pre_election_years:
            if current_month == 11 and not self.position:
                self.buy(data=self.dji_etf)  # Buy at the beginning of November
            elif current_month == 5 and self.position:
                self.sell(data=self.dji_etf)  # Sell at the end of April
        elif self.position:
            self.sell(data=self.dji_etf)  # Sell if not in pre-election year

if __name__ == '__main__':
    cerebro = bt.Cerebro()

    # Add DJI ETF data feed
    data = bt.feeds.YahooFinanceData(dataname='DIA',
                                     fromdate=datetime(2000, 1, 1),
                                     todate=datetime(2021, 12, 31))
    cerebro.adddata(data)

    # Add the strategy
    cerebro.addstrategy(PreElectionStrategy)

    # Set initial cash
    cerebro.broker.setcash(100000.0)

    # Run the backtest
    cerebro.run()

Equity Premiums in the Presidential Cycle: the Midterm Election Resolution of Uncertainty

Original paper

Abstract

Trading rules

Python code

Backtrader