Original paper
Abstract
Value investing has been a part of the investment lexicon for at least the better part of a century. In particular the diversified systematic “value factor” or “value effect” has been studied extensively since at least the 1980s. Yet, there are still many areas of confusion about value investing. In this article we aim to clarify many of these matters, focusing in particular on the diversified systematic value strategy, but also exploring how this strategy relates to its more concentrated implementation. We highlight many points about value investing and attempt to prove or disprove each of them, referencing an extensive academic literature and performing simple tests based on easily accessible, industry-standard public data.
Keywords:Â Value, momentum, market efficiency, behavioral finance
Trading rules
- Scope of investment: Stocks on NYSE, AMEX, and NASDAQ (excluding entities like REITs, ADRs, closed-end funds, utilities, and financial institutions).
- Sort yearly on June 30th using the EBITDA to TEV ratio.
- TEV calculation: Market Cap + Short-term Debt (DLC) + Long-term Debt (DLTT) + Preferred Stock Value (PSTKRV) - Cash & Short-term Investments (CHE)
- EBITDA calculation: Operating Income Before Depreciation (OIBDP) + Non-operating Income (NOPI)
- Sort by quintiles: Open long positions in the highest EBITDA/TEV quintile, signaling the most underpriced stocks.
- Portfolio: Equally weighted, yearly rebalancing
Python code
Backtrader
import backtrader as bt
import datetime
class EBITDA_TEV(bt.Strategy):
params = (
('exclude_sectors', [15, 40]),
('quintile_cutoff', 0.2),
('rebalance_date', (6, 30)),
)
def __init__(self):
self.order = None
def next(self):
if self.order: # Wait for pending orders
return
# Check if today is rebalance date
today = self.data.datetime.date(0)
if today.month == self.params.rebalance_date[0] and today.day == self.params.rebalance_date[1]:
self.rebalance()
def rebalance(self):
self.log('Rebalancing')
self.close_positions()
ebitda_tev = []
for d in self.datas:
if d._name == 'cash':
continue
sector = d.sector[0]
if sector in self.params.exclude_sectors:
continue
market_cap = d.market_cap[0]
dlc = d.dlc[0]
dltt = d.dltt[0]
pstk_rv = d.pstk_rv[0]
che = d.che[0]
oibdp = d.oibdp[0]
nopi = d.nopi[0]
ebitda = oibdp + nopi
tev = market_cap + dlc + dltt + pstk_rv - che
if tev != 0:
ebitda_tev.append((d, ebitda / tev))
ebitda_tev.sort(key=lambda x: x[1], reverse=True)
quintile_cutoff_index = int(self.params.quintile_cutoff * len(ebitda_tev))
quintile = ebitda_tev[:quintile_cutoff_index]
# Calculate target weights
target_weight = 1.0 / len(quintile)
for stock, ratio in quintile:
self.buy(stock, target=target_weight)
def close_positions(self):
for d in self.getpositions():
self.close(d)
def log(self, txt):
print(txt)
if __name__ == '__main__':
cerebro = bt.Cerebro()
cerebro.addstrategy(EBITDA_TEV)
# Add data feeds
# Note: Add your data feeds here. You should have the data columns specified in the rebalance method.
cerebro.addanalyzer(bt.analyzers.PyFolio, _name='pyfolio')
cerebro.broker.setcash(100000)
cerebro.broker.setcommission(commission=0.001)
results = cerebro.run()
strat = results[0]
pyfolio_analyzer = strat.analyzers.getbyname('pyfolio')
returns, positions, transactions, gross_lev = pyfolio_analyzer.get_pf_items()