Post-Earnings Drift (PEAD) Model

Overview

This pillar analyzes the Post-Earnings Announcement Drift (PEAD), a well-documented market anomaly where a stock's price continues to move in the direction of an earnings surprise for weeks or months. It helps identify sustained trading opportunities beyond the initial market reaction.

What It Does

The model quantifies the significance of a company's earnings surprise by comparing reported earnings per share (EPS) to analyst consensus estimates. It then uses historical data to forecast the probable direction and duration of the subsequent price drift. The analysis incorporates trading volume and past drift patterns to refine its predictions.

Why It Matters

Markets are not perfectly efficient and often underreact to new information. This pillar provides a systematic way to exploit this delayed reaction, offering a predictive edge in forecasting a stock's medium-term price trajectory after a significant earnings event.

How It Works

First, the model collects consensus analyst EPS estimates before an earnings report. After the company reports its actual EPS, it calculates the Standardized Unexpected Earnings (SUE) to measure the surprise. It then analyzes historical price action of stocks with similar SUE scores and volume patterns to project a likely price path over the next 60 days.

Methodology

The core metric is Standardized Unexpected Earnings (SUE), calculated as (Actual EPS - Consensus EPS) / Standard Deviation of Estimates. The analysis window is typically the 60 trading days following the earnings announcement. Drift is measured by calculating the Cumulative Abnormal Return (CAR) against a relevant benchmark like the S&P 500 to isolate the stock-specific movement.

Edge & Advantage

This model provides an edge by systematically identifying and trading on a proven market inefficiency that most short-term traders overlook.

Key Indicators

• Standardized Unexpected Earnings (SUE)

  high

  Measures the size of an earnings surprise relative to analyst expectations and forecast variance.

• Cumulative Abnormal Return (CAR)

  high

  Tracks the stock's performance relative to a market benchmark, isolating the drift effect.

• Post-Earnings Volume Surge

  medium

  Indicates the level of market conviction behind the initial price move.

Data Sources

• Refinitiv / FactSet

  Provides historical and current consensus analyst earnings estimates.

• Company SEC Filings (EDGAR)

  Official source for reported quarterly earnings (10-Q reports).

• Polygon.io

  Source for historical stock price and volume data needed to calculate returns.

Example Questions This Pillar Answers

• → Will stock XYZ close above $150 by the end of this quarter, following its positive earnings surprise?
• → Will [Tech Company] outperform the NASDAQ 100 index in the 60 days following its earnings report?
• → Will [Retail Company]'s stock price increase by more than 5% in the month after it reported negative earnings?

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