Original paper
Abstract
Mutual fund returns strongly persist over multi-year periods - that is the central finding of this paper. Further, consumer and fund manager behavior both play a large role in explaining these long-term continuation patterns - consumers invest heavily in last-year's winning funds, and managers of these winners invest these inflows in momentum stocks to continue to outperform other funds for at least two years following the ranking year. By contrast, managers of losing funds appear reluctant to sell their losing stocks to finance the purchase of new momentum stocks, perhaps due to a disposition effect. Thus, momentum continues to separate winning from losing managers for a much longer period than indicated by prior studies.
Even more surprising is that persistence in winning fund returns is not entirely explained by momentum - we find strong evidence that flow-related buying, especially among growth-oriented funds, pushes up stock prices. Specifically, stocks that winning funds purchase in response to persistent flows have returns that beat their size, book-to-market, and momentum benchmarks by two to three percent per year over a four-year period. Cross-sectional regressions indicate that these abnormal returns are strongly related to fund inflows, but not to the past performance of the funds - thus, casting some doubt on prior findings of persistent manager talent in picking stocks. Finally, at the style-adjusted net returns level, we find no persistence, consistent with the results of prior studies. On balance, we confirm that money is smart in chasing winning managers, but that a "copycat" strategy of mimicking winning fund stock trades to take advantage of flow-related returns appears to be the smartest strategy.
Keywords: mutual funds, market efficiency, performance evaluation
Trading rules
- Create a shortlist of active mutual fund managers.
- Refer to 13F filings to identify preferred stock selections of each manager.
- Allocate funds to stocks that a significant number of managers highlight as their top recommendations.
Python code
Backtrader
Here’s the basic outline of the Backtrader python code for the given trading rules:
import backtrader as bt
import backtrader.feeds as btfeeds
import pandas as pd
class MutualFundStrategy(bt.Strategy):
def __init__(self):
self.selection_pool = self._establish_selection_pool()
def next(self):
best_ideas = self._get_best_ideas()
self._invest_in_best_ideas(best_ideas)
def _establish_selection_pool(self):
# Your code to establish a selection pool of active mutual fund managers
pass
def _utilize_13F_filings(self, manager):
# Your code to utilize 13F filings to pinpoint top stock picks for each manager
pass
def _get_best_ideas(self):
best_ideas = []
for manager in self.selection_pool:
top_picks = self._utilize_13F_filings(manager)
best_ideas.extend(top_picks)
# Your code to find stocks that are favored by the majority of managers as their "best ideas"
return best_ideas
def _invest_in_best_ideas(self, best_ideas):
# Your code to invest in the best ideas
pass
# Data feed setup and backtesting code
if __name__ == '__main__':
cerebro = bt.Cerebro()
data = btfeeds.GenericCSVData(
dataname='your-data-file.csv',
dtformat=('%Y-%m-%d'),
openinterest=-1
)
cerebro.adddata(data)
cerebro.addstrategy(MutualFundStrategy)
cerebro.run()Please note that this is just a basic outline of the code, and you will need to implement the logic for each function and provide the required data to make it functional.