What if your dividend model accounted for uncertainty instead of ignoring it
Research tools for stochastic equity valuation
Most valuation frameworks assume dividends grow at a fixed rate. We know they do not. Our resources include published stochastic dividend growth models, full mathematical derivations, working code, historical validation reports, and implementation case studies. Everything is documented, versioned, and open to scrutiny. We do not sell courses or manage money. We publish what we have learned and invite you to test it.
Explore methodsResearch papers, model templates, and implementation guides
Our resources are built for practitioners who need rigor, not marketing. Each item includes
the full derivation, the code, and the validation report.
Published research papers
Peer-reviewed frameworks for stochastic dividend growth modeling with complete mathematical derivations and explicit assumptions.
Model implementation templates
Working code in Python with documented assumptions, clear variable names, and step-by-step execution paths.
Historical validation reports
Backtest results against real dividend data with documented failure modes, not cherry-picked successes.
Model outputs
Visual examples from our published frameworks
Simulated dividend paths with confidence intervals
Mean reversion in payout ratios
Getting started with stochastic dividend modeling
Practical guidance for using our resources effectively, from first model run to independent validation.
Start with the assumptions
Foundation
Before running any model, read the assumptions section carefully. A model is only as good as the conditions under which it was derived. We document these prominently so you can judge applicability before investing time.
Read assumptions
Check data requirements
Review limitations
10 min
Easy
Run the code before reading the paper
Practice
Our frameworks ship with working implementations. Run the code first to build intuition, then read the derivation to understand why it works. This order prevents getting lost in notation before seeing what the model actually produces.
Install dependencies
Run baseline case
Modify parameters
20 min
Medium
Validate against your own data
Validation
Our historical backtests use our datasets. The real test is whether the framework holds up against data you care about. Substitute your own dividend series and document where the model succeeds and where it breaks.
Prepare dataset
Run backtest
Document deviations
45 min
Hard
Read our methodology
Modeling terminology
A reference for the stochastic processes, dividend policy concepts, and quantitative finance
terms used throughout our frameworks and documentation.
The empirical tendency of dividend payout ratios and growth rates to return toward long-run historical averages after temporary deviations, modeled using processes like Ornstein-Uhlenbeck.
A fundamental result in stochastic calculus that describes how functions of stochastic processes evolve over time, essential for deriving solutions to dividend growth models.
A differential equation in which one or more terms is a stochastic process,
providing the mathematical formalism for modeling dividend growth under
uncertainty.
The full probability distribution of possible future dividend outcomes, as
opposed to a single point estimate, enabling quantification of uncertainty in
valuation decisions.
Brownian motion
Process