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US Stock Downturn Pension Defense Coach

4.15

Derivation Chain

Step 1 S&P 500 crash and AI layoff fears
→
Step 2 Pension asset defense issues for 50s overseas stock investors
→
Step 3 Decision-making on adjusting overseas ETF allocation in DC-type retirement pensions

Problem

Among office workers around age 55, the number of people who have allocated 30-50% of their DC-type retirement pension to S&P 500 ETFs has surged. During market crashes, they must decide alone whether to sell now or hold. Securities apps only show returns, not the reasoning linked to remaining years until retirement, expected payout, and tax implications. Fear-driven selling at the bottom repeatedly leads to losses of millions of won in retirement funds.

Solution

On the web, users input their DC-type retirement pension management status (ETF ratio, balance) and expected retirement date. The service visually compares 'probability of recovery by retirement', 'difference in final payout if switching to safe assets now', and 'three after-tax net income scenarios' against the current drawdown. It explains based on historical similar downturns (2020, 2022).

Target: Office workers aged 50-58 at large corporations or public enterprises, enrolled in DC-type retirement pensions, with experience in overseas ETF investments, 3-7 years before retirement.
Revenue Model: Basic simulation is free; personalized rebalancing report PDF is 5,000 KRW per report (~$3.75). Quarterly subscription report is 29,000 KRW (~$21.75).
Ecosystem Role: Education
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.
N=.15 U=.20 M=.15 R=.30 V=.20

Feasibility (70%)

Tech Complexity
29.3/40
Data Availability
20.6/25
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 (74/100)

Competition
10.0/20
Market Demand
20.0/20
Timing
16.0/20
Revenue Signals
10.5/15
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

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