Original paper
Abstract
The investment premium -- the finding that firms with low asset growth deliver high average returns -- is an integral part of recent factor models. I document empirically that the investment premium (1) reflects leverage, (2) does not exist among zero-leverage firms, and (3) increases with firms' refinancing intensities. This new evidence challenges prominent explanations of the investment premium including the q-theory of investment and behavioral finance. To explain the evidence, I develop a model in which firms make both optimal investment and financing decisions. The model shows that the investment premium reflects both leverage and refinancing intensities consistent with my empirical findings.
Keywords:Â Equity returns, investments, leverage, debt maturity, debt overhang
Trading rules
- Trading scope: Stocks from NYSE, Amex, and NASDAQ.
- Stock allocation:
- Group stocks into five categories by Size (from Smallest to Largest) using NYSE market cap every end of June.
- Sort stocks into five distinct Investment (Inv) categories (from Low to High) based on NYSE criteria.
- Combine the two sorts, resulting in 25 distinct Size-Inv combinations.
- Investment calculation:
- For portfolios formed in June of year t, determine Inv by measuring asset growth for the fiscal year closing in t-1, divided by the total assets at the close of t-1.
- Going long:
- Choose the portfolio with the highest Size and the lowest Investment.
- Going short:
- Choose the portfolio with the highest Size and the highest Investment.
- Weighing the portfolio:
- Use value-weighted portfolios.
Python code
Backtrader
import backtrader as bt
class SizeInvStrategy(bt.Strategy):
def __init__(self):
self.market_caps = {}
self.total_assets = {}
self.investments = {}
def next(self):
# Check if June
if self.datetime.date().month != 6:
return
# Allocate stocks to Size and Investment groups
self.market_caps.clear()
self.total_assets.clear()
self.investments.clear()
for data in self.datas:
symbol = data._name
self.market_caps[symbol] = data.market_cap[0]
self.total_assets[symbol] = data.total_assets[0]
self.investments[symbol] = (data.total_assets[0] - data.total_assets[-252]) / data.total_assets[-252]
size_sorted = sorted(self.market_caps.items(), key=lambda x: x[1])
inv_sorted = sorted(self.investments.items(), key=lambda x: x[1])
size_groups = [size_sorted[i::5] for i in range(5)]
inv_groups = [inv_sorted[i::5] for i in range(5)]
# Find long and short positions
long_position = size_groups[-1][0]
short_position = size_groups[-1][-1]
# Close existing positions
for data in self.datas:
if self.getposition(data).size > 0:
self.sell(data)
elif self.getposition(data).size < 0:
self.buy(data)
# Open new long and short positions
self.buy(data=self.getdatabyname(long_position), target=0.5)
self.sell(data=self.getdatabyname(short_position), target=0.5)
# Load data, set up cerebro, and run the strategy
cerebro = bt.Cerebro()
# Add your data feed here using the 'cerebro.adddata()' method
cerebro.addstrategy(SizeInvStrategy)
results = cerebro.run()Please note that this code assumes the availability of custom data fields like market_cap and total_assets in the data feed. You would need to create a custom data feed class in Backtrader that includes these fields for the code to work correctly.