The hottest stock on Korea's biggest trading forum right now is Beyond Meat ($BYND)....which did a fat short squeeze this last week.
Just a few days ago? Quantum computing names.
Koreans are gambling addicts.
I don't mean that pejoratively. I mean it as an edge.
Korean retail is in tune with the hot momentum stocks in frothy markets like the current one we're in.
There's a reason why Squid Games came from there :)
DCInside's "stockus" board has almost 300 million global monthly visits with users posting real-time trade ideas, hype cycles, and full-blown mania around U.S. equities.
Most "momentum traders" ignore this data entirely. They can't read Korean. They don't monitor foreign retail forums.
The challenge? Sifting through thousands of Korean-language posts to extract ticker mentions, map sentiment, and identify which names have true velocity vs noise.
I built an AI scraper to do exactly that.
Here's how it works:
- Scrape the last 7 days of posts from DCInside's stockus board
- Extract ticker mentions (both English symbols + Korean nicknames)
- Validate tickers + map to engagement metrics (comments, upvotes, views)
- Flag small-cap, low-float names with squeeze potential
- Output a time-series heatmap showing mention velocity
Let me show you....
The Prompt
We are using Manus for this exercise.
I find Manus very solid for Excel related tasks.
Here's the 6 page prompt I pre-prepared:
DCInside_Stock_Ticker_Analysis_Prompt.pdf
The Result
Full Excel Output from Manus: DCInside_StockUS_7day_trending_ROBUST.xlsx
The AI scraped 7,491 posts and surfaced 35 unique tickers.
Here's what Korean retail was buying:
Top 3 by mentions:
- BYND → 357 mentions (311 unique posts)
- TSLA → 264 mentions (132 unique posts)
- RGTI → 215 mentions (215 unique posts)
Within this list, it flagged a few prime targets for squeezes / small enough names for retail apes to impact price:
Small-move flags:
Two ways to use this:
(1) Track weekly to gauge attention shifts. If you run this every 7 days, you can measure changes in "mindshare." Rising mentions = continuation potential. Falling mentions = topping risk. This can be a momentum strategy if you develop an eye for this.
(2) Focus on ticker mentions, not engagement metrics. Some of the numbers (especially on market cap) aren't as reliable as I'd want. The core signal is mention count + velocity. I've found ChatGPT better for data accuracy (which matters a lot more for diligence matters), but for pattern recognition and ticker discovery this is great.