Original paper
Abstract
Recent literature indicates that a liquidity investment style – the process of investing in relatively less liquid stocks within the liquid universe of publicly traded stocks – has led to excess returns relative to size and value. While previously documented at the security level, we examine whether this style can be uncovered at the mutual fund level. In aggregate and across a wide range of mutual fund categories, we find that on average mutual funds that held less liquid stocks significantly outperformed mutual funds that held more liquid stocks. This demonstrates that the liquidity premium is sufficiently strong to show up in portfolios where the managers are most likely not directly focusing on liquidity. Surprisingly, the outperformance of the mutual funds that held less liquid stocks was primarily due to superior performance in down markets, especially market crashes.
Keywords:Â Liquidity, Stocks, Portfolio Management, Mutual Funds
Trading rules
- Target small-cap equity mutual funds due to pronounced liquidity effects in smaller firms.
- Obtain end-of-year equity holdings for each fund using 13F filings or mutual fund data providers (e.g., Morningstar).
- Determine the liquidity metric for every stock in a fund, utilizing the turnover ratio (shares exchanged relative to outstanding shares).
- Create a weighted average liquidity score for each fund (stock’s liquidity score multiplied by its weight in the fund).
- Initiate long positions in the uppermost 20% of funds marked by the lowest liquidity composite score.
- Adjust the portfolio on a yearly basis.
Python code
Backtrader
import backtrader as bt
import pandas as pd
class LiquidityEffect(bt.Strategy):
def __init__(self):
self.small_cap_funds = self.get_small_cap_funds()
def next(self):
if self.datetime.date().month == 1 and self.datetime.date().day == 1:
self.rebalance_portfolio()
def get_small_cap_funds(self):
# Retrieve small-cap equity mutual funds list from a data source
pass
def calculate_liquidity_score(self, fund):
holdings = self.get_fund_holdings(fund)
liquidity_scores = []
for stock in holdings:
turnover = stock["shares_traded"] / stock["outstanding_shares"]
liquidity_scores.append(turnover * stock["weight"])
return sum(liquidity_scores)
def get_fund_holdings(self, fund):
# Retrieve end-of-year equity holdings for the given fund from a data source
pass
def rebalance_portfolio(self):
liquidity_scores = {}
for fund in self.small_cap_funds:
liquidity_scores[fund] = self.calculate_liquidity_score(fund)
sorted_funds = sorted(liquidity_scores, key=liquidity_scores.get)
top_quintile = sorted_funds[:len(sorted_funds) // 5]
for fund in self.small_cap_funds:
if fund in top_quintile:
self.order_target_percent(fund, target=1 / len(top_quintile))
else:
self.order_target_percent(fund, target=0)
if __name__ == '__main__':
cerebro = bt.Cerebro()
cerebro.addstrategy(LiquidityEffect)
# Add small-cap equity mutual funds data to Cerebro
# ...
cerebro.run()