Original paper
Abstract
This paper explains and extends my 2002 paper. It presents a return factor of illiquid-minus-liquid stocks, called IML, which provides a time series of the illiquidity premium. The risk-adjusted predicted return on IML is lower in the period that follows my 2002 paper but it is still significant. IML also has the predicted response to market illiquidity shocks.
Keywords:Â Liquidity Pricing
Trading rules
- Choose the top 3,500 firms from NYSE, AMEX, and NASDAQ by market capitalization.
- The price per share should be no less than $2.
- The company's market capitalization should exceed $10 million.
- Exclude REITs, warrants, ADRs, ETFs, Americus Trust Components, and closed-end funds.
- Segment stocks into four groups by their market value.
- Concentrate on those in the quartile with the smallest market capitalizations.
- Divide these stocks into quartiles based on turnover in the last 12 months.
- Go long on stocks with the lowest turnover.
- Go short on stocks with the highest turnover.
- Annually adjust the portfolio in December.
- Weight stocks equally in the portfolio.
Python code
Backtrader
import backtrader as bt
class LowLiquidityStrategy(bt.Strategy):
def __init__(self):
self.rank_market_cap = None
self.rank_turnover = None
def next(self):
if self.datetime.date().month == 12 and self.datetime.date().day == 31:
# Filter stocks by market cap and per-share price
filtered_stocks = [d for d in self.datas if d.close[0] >= 2 and d.market_cap >= 10000000 and not d.is_excluded]
# Sort by market cap and select top 3500
sorted_by_market_cap = sorted(filtered_stocks, key=lambda d: d.market_cap, reverse=True)[:3500]
# Divide stocks into quartiles by market cap
quartile_size = len(sorted_by_market_cap) // 4
lowest_market_cap_quartile = sorted_by_market_cap[-quartile_size:]
# Divide lowest market cap quartile stocks into quartiles based on turnover
sorted_by_turnover = sorted(lowest_market_cap_quartile, key=lambda d: d.turnover)
turnover_quartile_size = len(sorted_by_turnover) // 4
lowest_turnover_quartile = sorted_by_turnover[:turnover_quartile_size]
highest_turnover_quartile = sorted_by_turnover[-turnover_quartile_size:]
# Go long on stocks with the lowest turnover
for data in lowest_turnover_quartile:
self.order_target_percent(data, target=1.0 / len(lowest_turnover_quartile))
# Go short on stocks with the highest turnover
for data in highest_turnover_quartile:
self.order_target_percent(data, target=-1.0 / len(highest_turnover_quartile))
# Close positions for the stocks not in the lowest or highest turnover quartile
for data in self.datas:
if data not in lowest_turnover_quartile and data not in highest_turnover_quartile:
self.close(data)
cerebro = bt.Cerebro()
cerebro.addstrategy(LowLiquidityStrategy)
# Add your data feeds to cerebro here
cerebro.run()Note that this code is a starting point for implementing the trading rules you provided. You will need to add your data feeds to cerebro and ensure that the data feeds have the necessary information, such as market capitalization, turnover, and any exclusion criteria (REITs, warrants, ADRs, ETFs, Americus Trust Components, and closed-end funds). Additionally, the code assumes the data feeds are daily. You may need to adjust it if you are using a different frequency.