Original paper
Abstract
Over a third of Initial Public Offerings (IPOs) listing on NYSE, AMEX and NASDAQ from 1981 through 2008 accepted offer prices on or below the minimum of their initial price range. This is a striking number of issuers accepting large discounts, and has been given no attention in the extant IPO literature. I argue that issuers are only willing to accept such discounts if the expected returns on funds raised are exceptional, and make up for the foregone assets-in- place. If the low demand for allocations in these “cold” IPOs are a result of investors bounded rationality, then abnormal returns will be observed as the market corrects. Using a sample of more than 5000 IPOs, I document significant and robust abnormal returns up towards 5% (excluding Initial Day Returns) during the first months of trading. These abnormal returns are greater and more persistent if general market conditions are strong, supporting a bounded rationality explanation.
Keywords: IPOs, Book-building, Medium-run IPO returns, Long-run IPO returns, Bounded Rationality, Lockups
Trading rules
- Focus on NYSE, AMEX, and NASDAQ IPOs
- Exclude penny stocks, unit offerings, REITs, ADRs, closed-end funds, and IPOs priced below $5
- Select IPOs with final offer price below initial filing range
- Purchase “Cold IPOs” one month after offering
- Hold positions for two months
- Hedge each IPO with a short position in a related industry (using industry ETF or shorting a basket of stocks)
- Maintain equal weighting for IPOs in the portfolio
Python code
Backtrader
import backtrader as bt
class ColdIPOStrategy(bt.Strategy):
def __init__(self):
self.cold_ipos = []
def next(self):
# Check if one month has passed since the IPO
for data in self.datas:
if len(data) == 21:
if data.close[0] < data.initial_filing_range and data.price >= 5 and not data.is_penny_stock and not data.is_unit_offering and not data.is_reit and not data.is_adr and not data.is_closed_end_fund:
self.cold_ipos.append(data)
# Purchase Cold IPOs after one month and hold for two months
for ipo in self.cold_ipos:
if len(ipo) == 21:
self.buy(data=ipo)
elif len(ipo) == 63:
self.sell(data=ipo)
self.cold_ipos.remove(ipo)
# Hedge each IPO with a short position in a related industry
related_etf = ipo.related_industry_etf
if related_etf:
if len(ipo) == 21:
self.sell(data=related_etf)
elif len(ipo) == 63:
self.buy(data=related_etf)
def rebalance_portfolio(self):
value_per_position = self.broker.getvalue() / len(self.cold_ipos)
for ipo in self.cold_ipos:
size = value_per_position / ipo.close[0]
self.order_target_size(data=ipo, target=size)Note: This code assumes you have access to the necessary data, such as initial filing range, penny stocks, unit offerings, REITs, ADRs, closed-end funds, and related industry ETFs. You will need to preprocess and feed this data to the Backtrader platform.