Original paper
Abstract
The strategy of buying and holding “net nets” has been advocated by deep value investors for decades, but systematic studies of the returns to such a strategy are few. We detail the returns generated from a net nets strategy implemented from 1984 - 2008, and then attempt to explain the excess returns (alpha) generated by the net nets strategy. We find that monthly returns amount to 2.55%, and excess returns using a simple market model amount to 1.66%. After controlling for a variety of risk factors and firm characteristics, and imposing several filters, we find a remaining significant excess return.
Keywords:Â deep value, net nets, excess returns
Trading rules
- Scope of investment: Focus on stocks from the London Exchange, excluding those with multiple share classes, foreign entities, lightly regulated markets, and the financial sector.
- Portfolio creation: Every year in July.
- Eligibility: Stocks with an NCAV/MV ratio exceeding 1.5.
- Portfolio approach: Adopt a buy-and-hold position.
- Duration of holding: Maintain positions for a full year.
- Allocation approach: Distribute equal weight to each stock.
Python code
Backtrader
import backtrader as bt
import datetime
class NCAVStrategy(bt.Strategy):
def __init__(self):
self.ncav_ratio_threshold = 1.5
def next(self):
if self.datetime.month[0] == 7 and self.datetime.day[0] == 1:
self.rebalance_portfolio()
def rebalance_portfolio(self):
self.sell_all_stocks()
selected_stocks = self.select_stocks()
if not selected_stocks:
return
weight = 1.0 / len(selected_stocks)
for stock in selected_stocks:
self.buy(stock, target=weight)
def sell_all_stocks(self):
for position in self.positions:
if position.is_long:
self.sell(position.data)
def select_stocks(self):
ncav_stocks = []
for stock in self.getdatanames():
data = self.getdatabyname(stock)
ncav = data.ncav[0]
mv = data.close[0]
if mv == 0:
continue
ncav_mv_ratio = ncav / mv
if ncav_mv_ratio > self.ncav_ratio_threshold:
ncav_stocks.append(data)
return ncav_stocks
if __name__ == '__main__':
cerebro = bt.Cerebro()
cerebro.addstrategy(NCAVStrategy)
# Load data and add to cerebro
# ...
cerebro.run()Note that this code assumes that the NCAV data is available for each stock in the dataset. You’ll need to load the stock data and NCAV data for each stock on the London Exchange and add them to the Cerebro instance.