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earnings-forecast

Analyze earnings forecasts using top-down, bottom-up, SUE, and PEAD methods.

1|Updated Jun 23, 2026
Search Tags:#financial analysis#investment analysis#market forecasting#earnings forecast#trading opportunities
One-click install
npx skills add https://github.com/644408071-design/Kokpop --skill earnings-forecast-644408071-design
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: earnings-forecast
Source: https://github.com/644408071-design/Kokpop/tree/main/agent/src/skills/earnings-forecast
Command: npx skills add https://github.com/644408071-design/Kokpop --skill earnings-forecast-644408071-design

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, pandas, scipy, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill analyzes and predicts earnings forecasts, providing insights to identify over-performance trading opportunities by comparing expected versus actual earnings.

Core Features & Use Cases

  • Earnings Forecast Analysis: Utilizes various methods such as top-down and bottom-up forecasting, SUE, and PEAD to analyze earnings expectations.
  • Anomaly Detection: Identifies stocks with earnings that significantly exceed or fall short of expectations.
  • Investment Decision Support: Assists investors in making informed decisions based on the analysis of earnings forecasts.

Quick Start

Generate a report for earnings forecast analysis for Company XYZ for the current quarter.

Frequently Asked Questions about earnings-forecast

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I predict earnings forecasts and identify trading opportunities?▼

To predict earnings forecasts and identify trading opportunities, you analyze expected versus actual earnings using financial modeling techniques like SUE and PEAD to detect significant over-performance or shortfalls for investment decision-making.

What is post-earnings announcement drift (PEAD) and how does it work?▼

Post-earnings announcement drift (PEAD) works by analyzing the tendency of a stock's price to continue moving in the direction of an earnings surprise, helping investors identify over-performance trading opportunities following significant forecast deviations.

How do I calculate standardized unexpected earnings (SUE) for market forecasting?▼

To calculate standardized unexpected earnings (SUE) for market forecasting, compare actual earnings against expectations and scale the result by historical deviation, identifying stocks that significantly exceed or fall short of forecasts.

Can I use numpy and pandas for bottom-up earnings forecast analysis?▼

Yes, you can use numpy and pandas for bottom-up earnings forecast analysis, as these dependencies support the underlying financial modeling and data manipulation required to evaluate expected versus actual earnings data.

What is the best way to analyze top-down and bottom-up earnings forecasts?▼

The best way to analyze top-down and bottom-up earnings forecasts is to apply financial modeling techniques that compare expected and actual results, detecting anomalies where earnings significantly exceed or fall short of market expectations.

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