Original paper

SSRN-id2616056.pdf923.4KB

Abstract

The recent increase in passive investment products has provided investors with easy access to international markets. The basic motivation of this paper is to offer new tools to investors who want to allocate assets across countries. This study investigates the performance of equity country selection strategies based on a combination of theoretically and empirically motivated variables. Thus, we create portfolios and assess their performance using asset pricing models. The empirical examination is based on data from 78 countries from 1999 to 2015. The strategies that are based on the earnings-toprice (EP) ratio, the turnover ratio, and skewness prove to be useful tools for international investors. Furthermore, portfolios from sorts on the blended rankings of skewness combined with the EP ratio or the turnover ratio are also characterized by an attractive risk-return ratio. However, joint strategies do not outperform strategies that are based on single metrics. Consequently, we argue that investors would be better off building a diversified portfolio rather than combining their options into one strategy because of the low correlation among returns on single-variable strategies.

Keywords: country selection strategies, asset pricing, cross section of stock returns, value investing, momentum, skewness preference, liquidity premium, turnover ratio, international investments

Trading rules

Investment universe: ETFs tracking individual country equity indexes.
Criteria for selection: Opt for the 5 countries displaying the strongest momentum over a period usually spanning 10 to 12 months.
Adjust portfolio composition: On a monthly basis.

Python code

Backtrader

import backtrader as bt

class CountryMomentum(bt.Strategy):
    params = (
        ('lookback_period', 11),  # X-month momentum (11 months for 12-month lookback, including the current month)
        ('top_n', 5),             # Top 5 countries
    )

    def __init__(self):
        self.inds = {}
        for d in self.datas:
            self.inds[d] = {}
            self.inds[d]['momentum'] = bt.indicators.Momentum(d.close, period=self.params.lookback_period)

    def next(self):
        country_momentums = []
        for d in self.datas:
            country_momentums.append((d, self.inds[d]['momentum'][0]))

        # Sort countries by momentum and select top N countries
        top_countries = sorted(country_momentums, key=lambda x: x[1], reverse=True)[:self.params.top_n]

        # Calculate equal weights for the selected countries
        weight = 1 / len(top_countries)

        # Rebalance the portfolio
        for d in self.datas:
            if d in [country_data for country_data, _ in top_countries]:
                self.order_target_percent(d, target=weight)
            else:
                self.order_target_percent(d, target=0)

# Create a Cerebro engine
cerebro = bt.Cerebro()

# Add the data feeds to Cerebro
# Add each country-specific ETF as a data feed here

# Add the strategy to Cerebro
cerebro.addstrategy(CountryMomentum)

# Run Cerebro engine
results = cerebro.run()

# Plot the results
cerebro.plot()

Please note that you will need to add the data feeds for the country-specific equity index ETFs before running the Cerebro engine.

Combining Equity Country Selection Strategies

Original paper

Abstract

Trading rules

Python code

Backtrader