BIOX vs MOS

Bioceres Crop Solutions Corp. vs Mosaic Company (The) — Valuation Comparison 2026

BIOX

Agricultural Chemicals
Bioceres Crop Solutions Corp.
Quality
6.2
out of 10
Value Trap
33
LOW
Price
$0.45
Last close
Models
5/13
Active
VS

MOS

Agricultural Chemicals
Mosaic Company (The)
Quality
7.0
out of 10
Value Trap
Price
$23.90
Last close
Models
10/13
Active

Model-by-Model Comparison

ModelType BIOX Fair ValueBIOX Upside MOS Fair ValueMOS Upside
Bayesian DCF Intrinsic $2.40 +437.5%
Earnings Power Value Intrinsic $21.42 -7.5%
EROIC Spread Intrinsic $0.91 +104.7% $16.82 -29.6%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $0.01 -97.8%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $•••.•• ••.•% $•••.•• ••.•%
PWERM Option-Based $•••.•• ••.•% $•••.•• ••.•%
Regime Cross-Sectional Relative $•••.•• ••.•% $•••.•• ••.•%
Sentiment SOTP Hybrid $•••.•• ••.•% $•••.•• ••.•%
CUCE Ensemble Ensemble $•••.•• ••.•% $•••.•• ••.•%
FTNN Topology Relative $•••.•• ••.•% $•••.•• ••.•%
🔒

Unlock Full 13-Model Comparison

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

Access Full Analysis — From $27/mo →

BIOX vs MOS — Which Stock Is More Undervalued?

MOS scores higher with a 7.0/10 quality rating vs BIOX's 6.2/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Bioceres Crop Solutions Corp. (BIOX) and Mosaic Company (The) (MOS) 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.

BIOX currently trades at $0.45 with a QOC of 6.2/10, while MOS trades at $23.90 with a QOC of 7.0/10.

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).