Original paper

SSRN-id2121150.pdf483.5KB

Abstract

This paper documents evidence of reversals in the long-term returns of international equity markets. We use recent short-term performance to better select contrarian securities that appear ready to reverse. Our late-stage contrarian strategy consistently provides stronger evidence of long-term return reversal than does the traditional pure contrarian strategy when applied to developed and emerging market indices. Despite an absence of cross-sectional contrarian profits for developed markets in our post-1989 subsample, longitudinal analysis provides strong evidence of reversals during this period. Overall, our results suggest that the reversal of long-term returns may be stronger and more pervasive than is generally understood.

Keywords: contrarian effect, international financial integration, developed markets, emerging markets

Trading rules

Investment universe: 26 countries from emerging markets.
Evaluate and rank on a monthly basis using previous five-year returns.

Top quartile: Late-stage outperformers (LW).
Last quartile: Late-stage underperformers (LL).

Within each subgroup, classify nations by their half-year (6-month) momentum.
Long positions: Top 50% of best 6-month momentum in LL group.
Short positions: Bottom 50% of worst 6-month momentum in LW group.
Equal weighting for each country.
Hold positions for 6 months.
Monthly portfolio rebalance (1/6th of the portfolio each month).

Python code

Backtrader

import backtrader as bt

class EmergingMarketStrategy(bt.Strategy):
    def __init__(self):
        self.month_counter = 0
        self.rebalance_period = 6
    
    def next(self):
        # Check if it's time to rebalance the portfolio
        self.month_counter += 1
        if self.month_counter % self.rebalance_period != 0:
            return
        
        # Calculate 60-month performance
        past_60_month_perf = [d.close[0] / d.close[-60] for d in self.datas]
        
        # Calculate 6-month momentum
        six_month_momentum = [d.close[0] / d.close[-6] for d in self.datas]
        
        # Sort countries by 60-month performance and create late-stage groups
        sorted_indices = sorted(range(len(past_60_month_perf)), key=lambda k: past_60_month_perf[k])
        lw_group = sorted_indices[:len(self.datas) // 4]
        ll_group = sorted_indices[-len(self.datas) // 4:]
        
        # Rank countries in each subgroup by 6-month momentum
        lw_ranked = sorted(lw_group, key=lambda k: six_month_momentum[k], reverse=True)
        ll_ranked = sorted(ll_group, key=lambda k: six_month_momentum[k], reverse=True)
        
        # Set target positions
        for i, data in enumerate(self.datas):
            target_weight = 0
            
            # Set long positions for top 50% of best 6-month momentum in LL group
            if i in ll_ranked[:len(ll_group) // 2]:
                target_weight = 1 / (len(ll_group) // 2)
            
            # Set short positions for bottom 50% of worst 6-month momentum in LW group
            if i in lw_ranked[-len(lw_group) // 2:]:
                target_weight = -1 / (len(lw_group) // 2)
            
            # Rebalance the portfolio
            self.order_target_percent(data, target_weight)

Remember to add your data feeds and set up a Cerebro instance to execute this strategy in Backtrader. The code provided above assumes that you have already prepared the data feeds for the 26 emerging market countries.

Long-Term Return Reversal: Evidence from International Market Indices

Original paper

Abstract

Trading rules

Python code

Backtrader