B

Stock News Regulatory Risk Detector

3.35

Derivation Chain

Step 1 KOSPI surge & semiconductor stock volatility
→
Step 2 Need for regulatory risk monitoring on investment holdings
→
Step 3 Policy/regulatory change impact analysis service for individual stocks

Competitor references

No usable source links are available for this idea. Its signal notes and scores need independent verification.

Problem

Individual investors holding semiconductor and AI-related stocks struggle to assess how policy changes — U.S. export controls, Korea's Semiconductor Special Act, EU AI regulations — impact their holdings. Legislative proposals from the National Assembly, Ministry of Economy enforcement decree revisions, and U.S. Department of Commerce notices are scattered across multiple channels. Over 80% of investors only learn about regulatory risks after a stock price crash.

Solution

Automatically monitors domestic and international regulatory/policy changes that may affect the user's holdings. Crawls Korea's National Assembly Bill Information System, Ministry of Economy/Industry press releases, and the U.S. Federal Register, then uses an LLM to assess stock relevance. Delivers alerts for high-relevance regulatory changes with summaries and impact analysis.

Target: Individual investors aged 30–50 holding stocks in regulation-sensitive sectors (semiconductors, AI, biotech)
Revenue Model: SaaS Monthly Subscription: ₩6,900/month (~$5) per account (up to 5 stocks), Premium (international regulations + unlimited stocks) ₩12,900/month (~$10)
Ecosystem Role: Regulation
MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty
4.0/5
U Urgency
4.0/5
M Market
3.0/5
R Realizability
3.0/5
V Validation
3.0/5
NUMR-V Scoring System
N Novelty1-5How uncommon the service is in market context.
U Urgency1-5How urgently users need this problem solved now.
M Market1-5Market size and growth potential from proxy indicators.
R Realizability1-5Buildability for a small team with realistic constraints.
V Validation1-5Validation signal quality from competition and demand data.
Each dimension is scored 1-5 and weighted into grade S/A/B/C/D.
N=.15 U=.20 M=.15 R=.30 V=.20

Feasibility (72%)

Tech Complexity
29.3/40
Data Availability
22.5/25
MVP Timeline
20.0/20
API Bonus
0.0/15
Feasibility Breakdown
Tech Complexity/ 40Difficulty of core implementation stack.
Data Availability/ 25Practical availability and cost of required data.
MVP Timeline/ 20Expected time to ship a usable MVP.
API Bonus/ 15Bonus for viable public API leverage.
Total 100 points; below 50% is filtered out.

Market Validation (57/100)

Competition
8.0/20
Market Demand
6.2/20
Timing
16.0/20
Revenue Signals
9.0/15
Pick-Axe Fit
10.5/15
Solo Buildability
7.0/10
Validation Breakdown
Competition/ 20Signal quality from competitor landscape.
Market Demand/ 20Demand proxies from search and mention patterns.
Timing/ 20Fit with current shifts in tech, behavior, and regulation.
Revenue Signals/ 15Reference evidence for monetization viability.
Pick-Axe Fit/ 15How well the concept serves participants in a trend.
Solo Buildability/ 10Practicality for lean-team implementation.
Total 100 points; below 50 is filtered out.

Technical Requirements

Data Pipeline [medium] AI/ML [medium] Frontend [low]
Dashboard