README.md

metadata

language:
  - ko
pretty_name: KRX Investment Warning Prediction Dataset (OHLCV + Korean News)
tags:
  - finance
  - krx
  - korea
  - time-series
  - ohlcv
  - news
  - multimodal
  - anomaly-detection
  - binary-classification
task_categories:
  - text-classification
  - time-series-forecasting
task_ids:
  - binary-classification
license: mit

KRX Investment Warning Prediction Dataset (OHLCV + Korean News)

Dataset Summary

This dataset is a test dataset for predicting Investment Warning (투자주의종목) designations in the Korean stock market (KRX).
It contains raw daily OHLCV price data and Korean news text (title + body), designed for multimodal anomaly detection / binary classification.

Important: No technical indicators are included, and no normalization/scaling is applied.

• Date range: 2025-07-01 ~ 2025-09-30
• Prediction horizon: whether a stock will be designated as an investment warning within the next 1 trading day

Task

Binary classification:

• Label 0: Normal trading (no investment warning designation within the next 1 trading day)
• Label 1: Investment warning designation (within the next 1 trading day)

Label Alignment

For each (ticker, date=t), set label=1 if the stock is designated as an investment warning on t+1 (the next trading day).

Data Sources

Source	Description
Stock Prices	Daily OHLCV data for KRX listed stocks
Investment Warning	KRX investment warning designation history (labels)
News	Korean news articles per stock (title + body)

Dataset Format

This dataset is structured to be used directly with Hugging Face datasets, and consists of three columns:

• labels: Binary label (0 or 1)
• time_series: Price time-series information (OHLCV)
• texts: Korean news text mapped to the corresponding stock (title + body)

The exact internal structure of time_series and texts (e.g., list/dict formats, sequence length, date ordering) follows the dataset schema.
In general, time_series is provided as a fixed-length historical window, and texts contains news from the same date (or window period), either concatenated or stored as a list.

Example (Conceptual)

• labels: 0 or 1
• time_series: [[open, high, low, close, volume], ...]
• texts: ["article1 ...", "article2 ..."]

Feature Details

Price (OHLCV) — time_series

• OHLCV is provided as raw daily bars.
• No technical indicators (e.g., RSI, MACD) are included.
• No normalization/scaling is applied.
• Currency unit: KRW
• Volume: number of shares (not value)

News — texts

• News is mapped to tickers via an exact ticker-code mapping.
• Deduplication has been applied.
• Each news item includes title + body (stored as a single string or list depending on schema).

Recommended Metrics

Because investment warning events are likely to be rare (class imbalance), the following metrics are recommended:

• ROC-AUC, PR-AUC
• F1 (positive class), precision/recall
• Precision/recall at Top-k (useful for practical detection scenarios)
• (Optional) probability calibration