Original paper

SSRN-id1566794.pdf1013.6KB

Abstract

We construct hypothetical copycat funds to investigate the performance of free-riding strategies that duplicate the disclosed asset holdings of actively managed mutual funds. On average, copycat funds are able to marginally outperform their target mutual funds, but their relative success has increased after 2004, when SEC regulation imposed all mutual funds to quarterly disclose their holdings. We find a substantial cross-sectional dispersion in the relative performance of copycat funds. Free-riding on the portfolios disclosed by past winning funds and the funds that disclose representative holdings generates significantly better performance net of trading costs and expenses than the vast majority of mutual funds. The results indicate that free-riding on disclosed fund holdings is an attractive strategy and suggest that mutual funds can suffer from information disclosure requirements.

Keywords: Mutual Funds, Portfolio Disclosure, Copycat Funds, Relative Performance, SEC

Trading rules

Create a set of actively managing mutual fund professionals.
Use 13F documents to identify each manager's preferred stock choices.
Allocate funds to stocks most commonly selected by these managers 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.

Better than the Original? The Relative Success of Copycat Funds

Original paper

Abstract

Trading rules

Python code

Backtrader