Original paper

SSRN-id293617.pdf154.0KB

Abstract

Mutual funds must disclose their portfolio holdings to investors semiannually. The costs and benefits of such disclosures are a long-standing subject of debate. For actively managed funds, one cost of disclosure is a potential reduction in the private benefits from research on asset values. Disclosure provides public access to information on the assets that the fund manager views as undervalued. This paper tries to quantify this potential cost of disclosure by testing whether "copycat" mutual funds, funds that purchase the same assets as actively-managed funds as soon as those asset holdings are disclosed, can earn returns that are similar to those of the actively-managed funds. Copycat funds do not incur the research expenses associated with the actively-managed funds that they are mimicking, but they miss the opportunity to invest in assets that managers identify as positive return opportunities between disclosure dates. Our results for a limited sample of high expense funds in the 1990s suggest that while returns before expenses are significantly higher for the underlying actively managed funds relative to the copycat funds, after expenses copycat funds earn statistically indistinguishable, and possibly higher, returns than the underlying actively managed funds. These findings contribute to the policy debate on the optimal level and frequency of fund disclosure.

Keywords: mutual funds, disclosure regulation, intellectual property, patents

Trading rules

Create a list of proactive mutual fund managers.
Use the 13F documents to identify top stock picks of each manager.
Allocate capital to stocks predominantly selected as top recommendations by these managers.

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.

Copycat Funds: Information Disclosure Regulation and the Returns to Active Management in the Mutual Fund Industry

Original paper

Abstract

Trading rules

Python code

Backtrader