korean-market-analysis

Analyze Korean stock market data with PyKRX and US market correlations.

2|Updated Apr 6, 2026

Search Tags:#Korean stock market #KOSPI #KOSDAQ #chaebols #cross-market analysis

Ready to run

npx skills add https://github.com/k1064190/stock-expectation --skill korean-market-analysis

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: korean-market-analysis
Source: https://github.com/k1064190/stock-expectation/tree/main/.claude/skills/korean-market-analysis
Command: npx skills add https://github.com/k1064190/stock-expectation --skill korean-market-analysis

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides deep analysis and insights into the Korean stock market, integrating local factors with cross-market dynamics.

Core Features & Use Cases

Korean Market Coverage: Specializes in KOSPI, KOSDAQ, chaebols, and semiconductor supply chains.
Cross-Market Analysis: Incorporates US market movements and global economic trends.
Use Case: Get a comprehensive analysis of the Korean market's performance in relation to the US market, including potential risks and opportunities.

Quick Start

Analyze the current state of the Korean market by running 'korean-market-analysis'.

Frequently Asked Questions about korean-market-analysis

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

FAQPage Schema

How do I analyze the Korean stock market with cross-market correlations to US markets?▼

Korean stock market analysis integrates local market dynamics from KOSPI, KOSDAQ, and chaebols with cross-market correlations to US markets. It uses PyKRX for Korean market data and US market reference points to identify potential risks and opportunities.

What is the best way to evaluate chaebol performance and semiconductor supply chains in KOSPI and KOSDAQ?▼

The best way to evaluate chaebol performance is by running specialized Korean market analysis that extracts local market data via PyKRX. It specifically covers chaebols and semiconductor supply chains, utilizing numpy and pandas to process the metrics for comprehensive performance evaluation.

Do I need PyKRX and US market reference data to perform cross-market analysis on Korean stocks?▼

Yes, you need PyKRX to access Korean market data and US market reference points to perform cross-market analysis on Korean stocks. The analysis explicitly requires these inputs to accurately calculate correlations between local KOSPI/KOSDAQ dynamics and global economic trends.

Can I use pandas and numpy to process local market dynamics for KOSDAQ and KOSPI?▼

Yes, you can use pandas and numpy to process local market dynamics for KOSDAQ and KOSPI. The Skill utilizes these Python libraries for data processing and analysis, enabling in-depth computation of chaebol metrics and cross-market correlations.

How does cross-market analysis incorporate US market movements into Korean stock market insights?▼

Cross-market analysis incorporates US market movements by mapping them against Korean market data retrieved via PyKRX. It calculates correlations between US market reference points and local KOSPI or KOSDAQ dynamics using numpy and pandas to reveal global economic trend impacts.

Why focus on chaebols and semiconductor supply chains when analyzing the Korean stock market?▼

Focusing on chaebols and semiconductor supply chains is essential because they represent the core structural drivers of the Korean stock market. Analyzing these specific sectors within KOSPI and KOSDAQ reveals localized market dynamics that heavily influence overall market performance.