Original paper
Abstract
We develop a profile of overvalued equity, and show that firms meeting this profile experience abnormal stock returns net of transaction costs of -22 to -25 percent over the twelve months following portfolio formation. We show our model is distinct from predictors proposed in prior work, and our results robust to alternative measurements of expected returns. We also show that overvaluation is not confined to small firms and that institutions do not trade as if they identify overvalued equity. The profitable predictability we document suggests a pricing anomaly relating to the 2.5% of the firms in the population that our model identifies as substantially overvalued. Although we believe markets are generally efficient within the bounds of transaction costs, our evidence suggests that violations of minimally rational use of publicly available information do occur. To the extent that anomalies disappear or attenuate once documented in the literature (Doukas et al. 2002, Schwert 2003), our results are of interest to financial economists and investors.
Keywords:Â Overvalued Equity, Agency Costs, Earnings Manipulation, Earnings Overstatement, Financial Fraud, O-Score
Trading rules
- Investment universe: companies with market cap > $1 Billion
- Evaluation: yearly in April (after year-end fundamental data is reported)
- Calculate O-SCORE (0 to 5) for each company based on:
- Highest quintile of sales growth
- Lowest quintile of CFO to total assets
- Net stock issuance greater than industry median (current or prior year)
- Acquisition of another company within the last five years
- Highest quintile of PROBM (probability of manipulation)
- Short stocks with O-SCORE of 5
- Rebalance portfolio yearly
- PROBM calculation using Beneish (1999) model with eight variables:
- Days Sales in Receivables Index (DSRI)
- Gross Margin Index (GMI)
- Asset Quality Index (AQI)
- Sales Growth Index (SGI)
- Depreciation Index (DEPI)
- Sales General and Administrative Expenses Index (SGAI)
- Leverage Index (LVGI)
- Total Accruals to Total Assets (TATA)
- Update regression coefficients in PROBM equation annually
Python code
Backtrader
import backtrader as bt
import pandas as pd
import numpy as np
from scipy.stats import mstats
class OvervaluedStocks(bt.Strategy):
def __init__(self):
self.order = None
def next(self):
if self.order:
return
if len(self.datas) > 0:
self.log('Rebalance')
self.rebalance_portfolio()
self.order = self.sell()
def rebalance_portfolio(self):
stocks = self.get_overvalued_stocks()
for stock in stocks:
self.order_target_percent(stock, target=-1/len(stocks))
def get_overvalued_stocks(self):
# Get fundamental data and calculate O-SCORE for each stock
fundamental_data = self.get_fundamental_data()
fundamental_data['o_score'] = self.calculate_o_score(fundamental_data)
# Short stocks with O-SCORE of 5
overvalued_stocks = fundamental_data[fundamental_data['o_score'] == 5].index.tolist()
return overvalued_stocks
def get_fundamental_data(self):
# TODO: Implement method to get fundamental data for the investment universe
pass
def calculate_o_score(self, df):
# Calculate O-SCORE based on the 5 criteria
df['sales_growth_quintile'] = pd.qcut(df['sales_growth'], 5, labels=False)
df['cfo_to_assets_quintile'] = pd.qcut(df['cfo_to_assets'], 5, labels=False)
df['probm_quintile'] = pd.qcut(df['probm'], 5, labels=False)
df['o_score'] = (
(df['sales_growth_quintile'] == 4).astype(int) +
(df['cfo_to_assets_quintile'] == 0).astype(int) +
(df['net_stock_issuance'] > df['industry_median_issuance']).astype(int) +
(df['has_acquisition'] == 1).astype(int) +
(df['probm_quintile'] == 4).astype(int)
)
return df['o_score']
def log(self, txt):
print(f'{self.datetime.date()}, {txt}')
if __name__ == '__main__':
cerebro = bt.Cerebro()
cerebro.addstrategy(OvervaluedStocks)
# Add data feeds for investment universe
# TODO: Implement method to get data feeds for stocks with market cap > $1 Billion
cerebro.broker.setcash(100000)
cerebro.broker.setcommission(commission=0.001)
cerebro.addsizer(bt.sizers.PercentSizer, percents=95)
print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())
cerebro.run()
print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())Please note that you need to implement the methods for getting fundamental data and data feeds for the investment universe, as well as the PROBM calculation using the Beneish (1999) model. The provided code is a template that covers the basic structure of the strategy.