IMPP vs PANL

Imperial Petroleum Inc. vs Pangaea Logistics Solutions Ltd — Valuation Comparison 2026

IMPP

Deep Sea Foreign Transportation of Freight
Imperial Petroleum Inc.
Quality
8.5
out of 10
Value Trap
30
LOW
Price
$5.11
Last close
Models
13/13
Active
VS

PANL

Deep Sea Foreign Transportation of Freight
Pangaea Logistics Solutions Ltd
Quality
8.7
out of 10
Value Trap
20
SAFE
Price
$7.57
Last close
Models
13/13
Active

Model-by-Model Comparison

ModelType IMPP Fair ValueIMPP Upside PANL Fair ValuePANL Upside
Bayesian DCF Intrinsic $12.80 +150.5% $9.47 +25.1%
Earnings Power Value Intrinsic $18.26 +257.4% $2.15 -71.6%
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 $•••.•• ••.•% $•••.•• ••.•%
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IMPP vs PANL — Which Stock Is More Undervalued?

PANL scores higher with a 8.7/10 quality rating vs IMPP's 8.5/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Imperial Petroleum Inc. (IMPP) and Pangaea Logistics Solutions Ltd (PANL) 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.

IMPP currently trades at $5.11 with a QOC of 8.5/10, while PANL trades at $7.57 with a QOC of 8.7/10.

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).