Original paper
Abstract
This paper uses a sample of 4,750 stock swap mergers, cash mergers, and cash tender offers during 1963 - 1998 to characterize the risk and return in risk arbitrage. For out-of-sample comparison, we also examine the risk/return profile for a sample of active risk arbitrage hedge funds during 1990 - 1998. Results from both samples indicate that risk arbitrage returns are positively correlated with market returns in severely depreciating markets but uncorrelated with market returns in flat and appreciating markets. This result suggests that returns to risk arbitrage are similar to those obtained from selling uncovered index put options. Although linear asset pricing models provide reasonable estimates of the excess returns in risk arbitrage, a contingent claims analysis that incorporates the non-linearity in returns provides a more accurate description of the risk/return relationship. After controlling for both the non-linear return profile and transaction costs, we find that risk arbitrage generates excess returns of 4% per year.
Keywords:Â Risk arbitrage, asset pricing, market efficiency
Trading rules
- Focus on merger announcements from North America and Europe.
- Focus target firms that have a market capitalization exceeding $500 million.
- Target must have sufficient trading volume.
- The acquiring firm's stock should be easy to borrow.
- Evaluate the potential for arbitrage in the deal by ensuring:
- Positive acquisition premium
- Offer encompasses majority of target’s outstanding shares
- Acquirer does not already own a substantial portion of target’s shares
- Adjust portfolio weights equally on a daily basis.
- Rely on online platforms for recent information on mergers (for instance, check the website http://www.mergerinvesting.com/pendingmergers).
Python code
Backtrader
import backtrader as bt
import requests
import pandas as pd
class MergerArbitrageStrategy(bt.Strategy):
def __init__(self):
self.pending_mergers_url = "http://www.mergerinvesting.com/pendingmergers"
def next(self):
# Get merger information
merger_data = self.get_merger_data()
# Filter merger information based on conditions
valid_mergers = self.filter_merger_data(merger_data)
# Calculate equal weights for valid mergers
weights = 1.0 / len(valid_mergers)
# Rebalance portfolio
self.rebalance_portfolio(valid_mergers, weights)
def get_merger_data(self):
response = requests.get(self.pending_mergers_url)
merger_data = pd.read_html(response.content)[0]
return merger_data
def filter_merger_data(self, merger_data):
filtered_mergers = merger_data[
(merger_data['Target Market Cap'] > 500e6) &
(merger_data['Target Volume'] > 0) &
(merger_data['Acquirer Borrowable']) &
(merger_data['Premium'] > 0) &
(merger_data['Offer Percentage'] > 0.5) &
(merger_data['Acquirer Ownership'] < 0.5)
]
return filtered_mergers
def rebalance_portfolio(self, valid_mergers, weights):
# Exit all positions
for position in self.getpositions():
self.sell(data=position)
# Enter new positions
for _, merger in valid_mergers.iterrows():
target = merger['Target Ticker']
acquirer = merger['Acquirer Ticker']
target_data = self.getdatabyname(target)
acquirer_data = self.getdatabyname(acquirer)
# Buy target and sell acquirer
self.buy(data=target_data, size=weights * self.broker.getvalue())
self.sell(data=acquirer_data, size=weights * self.broker.getvalue())
# Add data feeds, cerebro setup, and run the strategyKeep in mind that this code is a basic template for implementing the merger arbitrage strategy in Backtrader, and may require additional modifications depending on the data source and format. The code assumes that the merger_data DataFrame contains columns for the target market cap, target volume, acquirer borrowability, premium, offer percentage, and acquirer ownership. The columns’ names should be updated to match the actual data source.