Original paper
Abstract
Conditionally conservative accounting practices mandate the more timely recognition of losses relative to gains through transitory negative accrual items. A direct implication of asymmetrically timely loss recognition is asymmetry in the persistence of accruals depending on whether the firm experiences a gain or a loss in the current year: accruals should be less persistent for loss years relative to profit years. If investors naively fixate on total earnings, however, conditional conservatism would imply that investors will tend to overestimate the persistence of accruals especially in loss years. Consistent with naïve earnings fixation, I find that Sloan’s (1996) accrual anomaly, i.e., the negative association between accruals and future abnormal stock returns, is more pronounced for loss firms relative to profit firms. The evidence is relevant for understanding the origins of the accrual anomaly and highlights that inferences with respect to the pricing of accruals can be affected by pooling loss firms with profit firms.
Keywords:Â accrual anomaly, earnings persistence, conditional conservatism, earnings fixation
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
- Sort stocks into ten groups using accruals as the criterion.
- Take long positions in stocks with the minimal accruals and short those with the maximal accruals.
- Adjust the portfolio each year in May, after the announcement of earnings.
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.