Original paper

SSRN-id3281679.pdf3487.4KB

Abstract

Analysing earning’s predictive power on stock returns was in the heart of academic research since late 60’s. First introduced to academic world in 1967 during seminar “Analysis of Security Prices” by Chicago University Professors Ray Ball and Philip Brown. In the next four decades was extensively analysed by many academics and is now a well-documented anomaly and is referred to as Post Earnings Announcement Drift (PEAD). This phenomenon is still at the centre of academic research because it stands at odds with efficient market hypothesis which assumes that all information is instantaneously reflected in stock prices. Professional investors are also closely looking at PEAD as it implies that it is easy to beat the market average by simply ranking stocks based on their earnings surprise and investing in the top decile, quintile or quartile and shorting the bottom part. Academic evidence shows that this strategy produces an abnormal return of somewhere between 2.6% and 9.37% per quarter, according to various authors.

In this paper I will present existing evidence supporting and contradicting “PEAD”, the history of academic research in that field and various techniques used to verify the phenomenon. The paper is organised as follows: first the history of the PEAD academic research is presented, in the second more recent evidence and research techniques used by authors are presented and finally conclusions and various critics of PEAD are shown.

Keywords: PEAD, Post Earnings Announcement Drift, Anomalies, Market Efficiency

Trading rules

Eligible Stocks: Focus on stocks from NYSE, AMEX, and NASDAQ, with the exception of those from the financial and utility sectors, and those priced under $5.
Metric 1: Measure EAR (Earnings Announcement Return) as the extraordinary return over a span of three days surrounding the release date, beyond a risk-comparable portfolio.
Metric 2: Define SUE (Standardized Unexpected Earnings) as the quotient of the earnings variation and the standard deviation of such variations.
Classification: Organize stocks into five equal groups using EAR and SUE values, relying on data from the prior quarter to sidestep future data bias.
Allocation in Portfolio: Assign an even distribution for stocks within each of the five groups.
Position Directions: Initiate long positions for the top-performing intersection of EAR and SUE and short positions for the least-performing intersection.
Duration of Position: Hold for one quarter (equivalent to 60 business days), starting two days post the earnings release.
Portfolio Adjustment: On a quarterly basis.

Python code

Backtrader

import backtrader as bt

class PEADStrategy(bt.Strategy):
    def __init__(self):
        self.ear = {}  # Earnings Announcement Return
        self.sue = {}  # Standardized Unexpected Earnings

    def next(self):
        # Check if it's the second day after the earnings announcement
        if not self.is_second_day_after_announcement():
            return

        # Calculate factors EAR and SUE for each stock
        self.calculate_factors()

        # Sort stocks into quintiles based on EAR and SUE
        long_stocks, short_stocks = self.sort_quintiles()

        # Calculate equal weight for each stock within quintiles
        long_weight = 1.0 / len(long_stocks)
        short_weight = -1.0 / len(short_stocks)

        # Enter long and short positions
        for stock in long_stocks:
            self.order_target_percent(stock, target=long_weight)
        for stock in short_stocks:
            self.order_target_percent(stock, target=short_weight)

        # Hold positions for 1 quarter (60 working days)
        self.hold_positions_for_n_days(60)

    def is_second_day_after_announcement(self):
        # Implement logic to determine if it's the second day after the earnings announcement
        pass

    def calculate_factors(self):
        # Implement logic to calculate EAR and SUE for each stock in the investment universe
        pass

    def sort_quintiles(self):
        # Implement logic to sort stocks into quintiles based on EAR and SUE
        pass

    def hold_positions_for_n_days(self, n):
        # Implement logic to hold positions for n days
        pass

if __name__ == '__main__':
    cerebro = bt.Cerebro()
    # Add data feeds for NYSE, AMEX, and NASDAQ stocks (excluding financial and utility firms and stocks below $5)
    cerebro.addstrategy(PEADStrategy)
    cerebro.run()

Please note that this is just a skeleton code for the PEAD strategy. You need to implement the logic for each function based on your data source and the desired calculations.

50 Years in PEAD Research

Original paper

Abstract

Trading rules

Python code

Backtrader