B

Stock-Tariff News Impact Parser

3.35

Derivation Chain

Step 1 Stock price volatility in Hyundai Motor, Samsung Electro-Mechanics, etc. due to Trump tariff policies
→
Step 2 Individual investor demand for tariff news interpretation
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Step 3 Automated tariff news-to-stock impact analysis tool

Problem

With frequent tariff policy announcements and actions from the Trump administration, individual investors struggle to determine how each tariff news item affects their holdings. Manually researching export ratios, HS code-specific tariff rates, and supply chain dependencies takes 1–2 hours per news item, by which time the stock price has already reacted.

Solution

Users register their stock portfolio, and whenever tariff-related news breaks, the system automatically analyzes each holding's export ratio, tariff-affected product proportion, and estimated operating profit impact, delivering results via push notification. Historical price reaction patterns of similar stocks during past tariff events are also provided.

Target: Individual investors aged 30–50 with high exposure to major Korean exporters (Hyundai Motor, Samsung Electro-Mechanics, Kia, etc.), and investment bloggers/YouTubers who need overseas revenue ratio analysis
Revenue Model: Basic Free (3 stocks, once-daily analysis), Premium at $22/month (unlimited stocks, real-time alerts, historical pattern analysis)
Ecosystem Role: Consumer
MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty
3.0/5
U Urgency
4.0/5
M Market
4.0/5
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.
SaaS N=.15 U=.20 M=.15 R=.30 V=.20 Senior N=.25 U=.25 M=.05 R=.30 V=.15

Feasibility (70%)

Tech Complexity
29.3/40
Data Availability
20.8/25
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 (56/100)

Competition
8.0/20
Market Demand
6.2/20
Timing
20.0/20
Revenue Signals
7.5/15
Pick-Axe Fit
7.5/15
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