Original paper
Abstract
It is widely believed that the long-standing and highly popular value investing strategy—investing in low-valued stocks and selling short high-valued equities—lost its edge in the past 10-12 years. The reasons for this putative failure of value investing elude investors and academics, making it a challenge to assess the likelihood of the return of value investing to its days of glory. Based on extensive data analysis we show that value investing has generally been unprofitable for almost 30 years, barring a brief resurrection following the dotcom bust. We identify two major reasons for the failure of value investing: (1) accounting deficiencies causing systematic misidentification of value, and particularly of glamour (growth) stocks, and (2) fundamental economic developments which slowed down significantly the reshuffling of value and glamour stocks (mean reversion) which drove the erstwhile gains from the value strategy. We end up by identifying the type of companies (stocks) that may still generate gains from value investing.
Keywords:Â Value investing, Growth investing, Hedged portfolio, Intangibles, R&D, Market to book ratio, Credit crisis
Trading rules
- Focus on the largest quintile (top 20%) of firms (by market value) from NYSE, AMEX, and NASDAQ.
- Create five portfolios using the Book-to-Market ratio as a criterion.
- Highest quintile based on Book-to-Market ratio is held for a 12-month period.
- Portfolio is weighted based on market capitalization.
- Refresh the portfolio composition annually.
Python code
Backtrader
# assume universe_df has columns ['asset', 'marketcap', 'exchange', 'book_to_market']
# include necessary imports
import backtrader as bt
import pandas as pd
class Top20HighestMarketCap(bt.Algo):
def __init__(self, universe_df):
self.top20 = universe_df.sort_values('marketcap', ascending=False)[:int(len(universe_df)*0.2)]['asset']
def __call__(self, target):
if target._name in self.top20:
return True
else:
return False
class BookToMarketQuintilePortfolio(bt.Algo):
def __init__(self, universe_df):
self.quintiles = universe_df.groupby('exchange')['book_to_market'].transform(lambda x: pd.qcut(x, 5, labels=False))
def __call__(self, target):
if target.book_to_market >= 4: # highest quintile
return True
else:
return False
class MarketCapWeighted(bt.Algo):
def __init__(self, universe_df):
self.weights = universe_df['marketcap']/universe_df['marketcap'].sum()
def __call__(self, target):
return self.weights[target._name]
# assume cerebro has been created and data has already been added
universe_df = pd.DataFrame({'asset': [d._name for d in cerebro.datas],
'marketcap': [d.lines[0][-1] for d in cerebro.datas],
'exchange': [d._name.split('-')[0] for d in cerebro.datas],
'book_to_market': [d.lines[1][-1] for d in cerebro.datas]})
cerebro.addanalyzer(bt.analyzers.TimeReturn, timeframe=bt.TimeFrame.Years)
cerebro.addsizer(MarketCapWeighted(universe_df))
cerebro.addstrategy(bt.Strategy)
cerebro.broker.setcommission(commission=0.001)
cerebro.broker.setcash(100000)
# define trading rules
cerebro.addanalyzer(bt.analyzers.TimeReturn, timeframe=bt.TimeFrame.Years, _name='TimeReturn_1yr')
cerebro.addanalyzer(bt.analyzers.SharpeRatio_Annual, timeframe=bt.TimeFrame.Years, _name='SharpeRatio_1yr')
cerebro.addanalyzer(bt.analyzers.DrawDown, _name='DrawDown')
cerebro.addanalyzer(bt.analyzers.Returns, _name='Returns')
cerebro.addanalyzer(bt.analyzers.Transactions, _name='Transactions')
cerebro.addobserver(bt.observers.Value)
cerebro.addobserver(bt.observers.DrawDown)
cerebro.addanalyzer(bt.analyzers.AnnualReturn, timeframe=bt.TimeFrame.Years)
cerebro.addanalyzer(bt.analyzers.Calmar, timeframe=bt.TimeFrame.Years)
cerebro.addanalyzer(bt.analyzers.PositionsValue, _name='PositionsValue')
# define which stocks to select based on market cap
cerebro.addfi(Top20HighestMarketCap(universe_df))
# define quintile strategies
cerebro.addfi(BookToMarketQuintilePortfolio(universe_df))
# run backtest
cerebro.run()
# print out analysis results
print('Annual Return:', cerebro.analyzers.getbyname('AnnualReturn').get_analysis())
print('Drawdown:', cerebro.analyzers.getbyname('Drawdown').get_analysis())
print('Sharpe Ratio:', cerebro.analyzers.getbyname('SharpeRatio_Annual').get_analysis())
print('Calmar Ratio:', cerebro.analyzers.getbyname('Calmar').get_analysis())