A

Stock FOMO Cooling Dashboard

4.25

Derivation Chain

Step 1 Stock market surge + FOMO psychology
→
Step 2 Retail investor trading decision support
→
Step 3 FOMO-driven overbought detection and cooling service

Problem

During market surges like KOSPI breaking 6300 or Samsung Electronics/SK Hynix jumping 7%, retail investors (especially salaried workers aged 30-50) experience FOMO-driven buying at peaks, resulting in average losses of 15-25%. Swept up by 'buy now or miss out' sentiment on Naver stock forums and KakaoTalk investment groups, they buy without analysis. Impulse buying rates on surge days are 3x or more above normal.

Solution

A dashboard that aggregates a real-time FOMO Index (search volume spike rate, community sentiment overheating, RSI and volume anomalies) to visually warn that 'this stock is in a FOMO overheating zone.' It features a 72-hour cooling timer alongside historical pattern analysis showing return distributions after similar peak-buying episodes. The key differentiator is a contrarian approach: instead of encouraging purchases, it shows data on 'what happens if you wait.'

Target: Retail investors aged 30-50, salaried workers with investment capital of 10M-100M won (~$7,500-$75,000), active traders checking stock apps 3+ times per week
Revenue Model: Premium SaaS Monthly Subscription at 49,000 won (~$37)/account, Free Plan (limited to 3 stocks/day), 25% discount for annual billing. Target Premium conversion rate: 8%
Ecosystem Role: Infrastructure
MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty
3.0/5
U Urgency
5.0/5
M Market
4.0/5
R Realizability
4.0/5
V Validation
5.0/5
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 (63%)

Tech Complexity
24.0/40
Data Availability
18.8/25
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 (70/100)

Competition
10.0/20
Market Demand
20.0/20
Timing
16.0/20
Revenue Signals
9.0/15
Pick-Axe Fit
7.5/15
Solo Buildability
8.0/10
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

Backend [medium] Frontend [medium] Data Pipeline [medium]
Dashboard