Original paper
Abstract
We demonstrate that returns and volatilities of the Fama-French size and value factors are significantly determined by non-fundamental flow-induced trading from actively managed equity mutual funds. Mutual fund flows are largely ignorant about systematic risks. These non-fundamental shifts in demand induce large return heterogeneity within and across the Fama-French size and book-to-market portfolios. We show that aggregate mutual fund flow- induced trades across the size and book-to-market spectrum significantly influence the size and value premia, followed by large subsequent reversals. We also find that the expected volatilities of mutual funds’ flow-induced trades strongly predict future factor volatilities. Our results highlight the importance of non-fundamental demand shocks in determining factor premia and factor volatilities.
Keywords:Â Mutual fund flows, factor premia, factor volatility
Trading rules
- Target assets: Stocks from the NYSE, AMEX, and NASDAQ.
- Organize stocks into five groups determined by their market value.
- Choose the group with the smallest market capitalization.
- Hold for one year.
- Rebalance portfolio annually.
Python code
Backtrader
import backtrader as bt
import datetime
class MarketCapStrategy(bt.Strategy):
params = (
('quintile', 5),
('rebalance_days', 252),
)
def __init__(self):
self.counter = 0
def next(self):
self.counter += 1
if self.counter % self.params.rebalance_days == 0:
self.rebalance_portfolio()
def rebalance_portfolio(self):
market_caps = []
for data in self.datas:
market_cap = data.close[0] * data.volume[0]
market_caps.append((data, market_cap))
sorted_data = sorted(market_caps, key=lambda x: x[1])
quintile_size = len(sorted_data) // self.params.quintile
lowest_quintile = sorted_data[:quintile_size]
# Calculate total market cap for lowest quintile
total_market_cap = sum([x[1] for x in lowest_quintile])
# Sell all current holdings
for data in self.datas:
self.order_target_percent(data, target=0)
# Buy stocks in lowest quintile
for data, market_cap in lowest_quintile:
target_weight = market_cap / total_market_cap
self.order_target_percent(data, target=target_weight)
if __name__ == '__main__':
cerebro = bt.Cerebro()
# Add investment universe
for ticker in ['NYSE_ticker1', 'NYSE_ticker2', 'AMEX_ticker1', 'NASDAQ_ticker1']:
data = bt.feeds.YahooFinanceData(
dataname=ticker,
fromdate=datetime.datetime(2015, 1, 1),
todate=datetime.datetime(2021, 9, 30),
adjclose=True,
plot=False,
)
cerebro.adddata(data)
cerebro.addstrategy(MarketCapStrategy)
cerebro.broker.setcash(100000.0)
cerebro.broker.setcommission(commission=0.001)
print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())
cerebro.run()
print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())