Original paper
Abstract
The accruals anomaly - the negative relationship between accounting accruals and subsequent stock returns - has been well documented in the academic and practitioner literatures for almost a decade. To the extent that this anomaly represents market inefficiency, one would expect sophisticated investors to learn about it and arbitrage the anomaly away. Yet, we show that the accruals anomaly still persists and its magnitude has not declined over time. While we find that institutional investors react promptly to accruals information, it is clear that their reaction is rather weak and is primarily characteristic of active investors who constitute a minority of institutions. The main reason: Extreme accruals firms have characteristics which are unattractive to most institutional investors. Individual investors are by and large unable to profit from trading on accruals information due to the high transaction and information costs associated with implementing a consistently profitable accruals strategy. Consequently, the accruals anomaly persists, and will probably endure.
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.