Original paper
Abstract
This paper examines the annual risks and returns of three disparate, hypothetical merger arbitrage portfolio strategies as an attempt to capture alpha from an in-sample study of 793 global M&A transactions covering the January 2000 thru December 2016 time period. Previously written and undoubtedly the most prominent literature into M&A and merger arbitrage [Schleifer & Vishny ‘97], [Mitchell & Pulvino ‘01] and [Baker & Savasoglu ‘02] focus on the limits of pure arbitrage the creation of such from noise/shock trading and the risk & return characteristics of a merger arbitrage trading strategy respectively. This paper by no means covers the universe of the arbitrage literature and touches only a fraction of arbitrage research – yet, this is the only paper I know of examining global merger arbitrage transactions and the construct of a long-only merger arbitrage strategy encompassing both US as well as non-US merger arbitrage deals and bench-marking the portfolios' risk and returns series against a non-U.S. benchmark.
This study demonstrates that alpha can be added by purchasing target companies as part of a long-only global merger arbitrage strategy. Furthermore, above benchmark returns can be earned by also buying the targets of terminated deals. This study is less conclusive on the incremental value of purchasing the acquiring company's shares following a successful or terminated deal where stock is part of the deal consideration.
Keywords:Â merger arbitrage, targets, terminated deals, CAPM
Trading rules
- Focus on announced mergers in North America and Europe.
- Ensure the targeted firm boasts a market capitalization surpassing $500 million.
- The chosen firm should exhibit adequate trade volume.
- In stock agreements, it should be straightforward to borrow the buyer's shares.
- Validate the potential for arbitrage in the trade by confirming:
- A favorable acquisition premium.
- Offer encompasses majority of target’s outstanding shares.
- Acquirer does not already own a substantial portion of target’s shares.
- Adjust an equally-weighted portfolio every day.
- Refer to online platforms for the latest merger details (for instance, 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.