HIT vs MRSH

Health In Tech, Inc. vs Marsh — Valuation Comparison 2026

HIT

Insurance Agents, Brokers & Service
Health In Tech, Inc.
Quality
9.1
out of 10
Value Trap
6
SAFE
Price
$0.99
Last close
Models
12/13
Active
VS

MRSH

Insurance Agents, Brokers & Service
Marsh
Quality
9.4
out of 10
Value Trap
12
SAFE
Price
$159.97
Last close
Models
13/13
Active

Model-by-Model Comparison

ModelType HIT Fair ValueHIT Upside MRSH Fair ValueMRSH Upside
Bayesian DCF Intrinsic $0.95 -4.0% $158.26 -1.1%
Earnings Power Value Intrinsic $0.31 -68.6% $33.30 -79.2%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $•••.•• ••.•% $•••.•• ••.•%
PWERM Option-Based $•••.•• ••.•% $•••.•• ••.•%
Regime Cross-Sectional Relative $•••.•• ••.•% $•••.•• ••.•%
Sentiment SOTP Hybrid $•••.•• ••.•% $•••.•• ••.•%
CUCE Ensemble Ensemble $•••.•• ••.•% $•••.•• ••.•%
FTNN Topology Relative $•••.•• ••.•% $•••.•• ••.•%
RCMH-DCF Intrinsic $•••.•• ••.•% $•••.•• ••.•%
🔒

Unlock Full 13-Model Comparison

Access all valuation models for HIT vs MRSH — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

HIT vs MRSH — Which Stock Is More Undervalued?

MRSH scores higher with a 9.4/10 quality rating vs HIT's 9.1/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Health In Tech, Inc. (HIT) and Marsh (MRSH) across 13 institutional-grade valuation models reveals how each company's intrinsic value stacks up against its market price. CirclFi's engine processes SEC EDGAR 10-K and 10-Q filings, FRED macroeconomic data, and GDELT news sentiment to generate independent fair value estimates daily.

HIT currently trades at $0.99 with a QOC of 9.1/10, while MRSH trades at $159.97 with a QOC of 9.4/10.

Both companies are analyzed with models spanning intrinsic (Bayesian DCF, EPV), scenario-based (First Chicago), regime-switching (Markov DDM, RCMH-DCF), machine learning (ML-RIV, FTNN Topology), and ensemble methods (CUCE).