Original paper
Abstract
This paper investigates the subsequent return implications of accruals within a sample of large, developed, international equity markets and assesses whether similar institutional features account for the accrual anomaly across countries. I investigate the returns implications of accruals in 17 countries over the 1989 to 2003 time period. In general, the results of country-specific analysis indicate that the accrual anomaly is a global phenomenon. After decomposing total accruals, I find, in general, that accrual mispricing is largest for working capital accruals, specifically current asset accruals. However, the results of further analysis suggest that there is no dominant factor that explains the accrual anomaly internationally. Overall, the results indicate that the accrual anomaly is present in international markets yet the factor(s) driving the accrual anomaly appear to vary across markets.
Trading rules
- Investment Universe: NYSE, AMEX, and NASDAQ stocks
- Calculate Balance Sheet Accruals (BS_ACC): BS_ACC = (∆CA - ∆Cash) - (∆CL - ∆STD - ∆ITP) - Dep
- ∆CA: Annual change in current assets
- ∆Cash: Change in cash and cash equivalents
- ∆CL: Change in current liabilities
- ∆STD: Change in debt within current liabilities
- ∆ITP: Change in income taxes payable
- Dep: Annual depreciation and amortization expense
- Categorize stocks into ten groups using accruals as the criteria.
- Initiate long positions on stocks with the least accruals and short those with the most.
- Adjust the portfolio once a year in May, post-earnings releases.
Python code
Backtrader
import backtrader as bt
class AccrualAnomaly(bt.Strategy):
def __init__(self):
self.accruals = {}
def next(self):
if self.data.datetime.date().month != 5: # Rebalance only in May
return
self.accruals.clear()
for data in self.datas:
ca = data.current_assets
cash = data.cash_equivalents
cl = data.current_liabilities
std = data.short_term_debt
itp = data.income_taxes_payable
dep = data.depreciation_amortization_expense
delta_ca = ca - ca(-1)
delta_cash = cash - cash(-1)
delta_cl = cl - cl(-1)
delta_std = std - std(-1)
delta_itp = itp - itp(-1)
bs_acc = (delta_ca - delta_cash) - (delta_cl - delta_std - delta_itp) - dep
self.accruals[data] = bs_acc
sorted_stocks = sorted(self.datas, key=lambda d: self.accruals[d])
low_decile = int(len(sorted_stocks) * 0.1)
high_decile = int(len(sorted_stocks) * 0.9)
long_stocks = sorted_stocks[:low_decile]
short_stocks = sorted_stocks[high_decile:]
self.sell_stocks(short_stocks)
self.buy_stocks(long_stocks)
def sell_stocks(self, short_stocks):
for stock in short_stocks:
self.sell(data=stock)
def buy_stocks(self, long_stocks):
for stock in long_stocks:
self.buy(data=stock)
if __name__ == '__main__':
cerebro = bt.Cerebro()
cerebro.addstrategy(AccrualAnomaly)
# Add your data feeds for NYSE, AMEX, and NASDAQ stocks here
cerebro.run()This code snippet provides a basic implementation of the Accrual Anomaly trading strategy in Backtrader. You’ll need to add your data feeds for NYSE, AMEX, and NASDAQ stocks to complete the implementation.