Original paper
Abstract
The accruals anomaly, demonstrated by Sloan (1996), generated significant excess returns consistently for over four decades until 2002, but has apparently weakened in the subsequent period. In this paper, I argue that one factor responsible for this decline is the increasing incidence of analysts’ cash flow forecasts that provides markets with forecasts of future accruals. The negative relationship between accruals and future returns is significantly weaker in the presence of cash flow forecasts. This anomalous relationship becomes weaker with the initiation cash flow forecasts but continues after cash flow forecasts are terminated. Further, the mitigating effect of cash flow forecasts is greater for forecasts that are more accurate. The results are incremental to explanations based on the improved accrual quality, reduced manipulation of special items and restructuring charges and greater investment in accruals strategies by hedge funds and highlight the increasing importance of analysts’ cash flow forecasts in the appropriate valuation of stocks.
Keywords: Accrual Anomaly, Cash flow forecasts, Market mispricing, Equity valuation
Trading rules
- Investment Scope: 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 deciles based on accruals.
- Go long on lowest accrual stocks, short on highest accrual stocks.
- Annual portfolio rebalancing 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(AccruralAnomaly)
# 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.