Quantitative methods · AI · Market dynamics

Applied Research in Finance & Markets

A personal initiative. Applied Markets publishes original, empirical work on quantitative methods, AI, and financial market dynamics. Every artifact begins with a question that has a tractable experimental form.

See experiments About

One hypothesis One focused experiment One honest publication

Research Pillars

Two areas of focus. Each with its own methods and questions.

Pillar I Quantitative Finance The mathematical foundations that markets run on — statistics, probability, information theory, and stochastic processes studied through original derivations and empirical analysis.
Pillar II AI for Finance Modern AI applied to financial reasoning, forecasting, and market simulation — LLMs, reinforcement learning, and agent-based models tested on real data with honest results.

At a Glance

Focus

Small, rigorous experiments on real financial data and market mechanisms.

Artifacts

Experiments, articles, and Python notebooks.

Standard

Original findings, even when the result is partial.

Release Pattern

ModeEmpirical

BiasClarity

CadenceContinuous

QualityHonest

Research Pillars

Pillar I

Quantitative Finance

The mathematical foundations that markets run on. Statistics, probability, information theory, and stochastic processes — studied through original derivations and empirical analysis.

probability information theory stochastic processes risk modeling market microstructure scoring rules

Pillar II

AI for Finance

Modern AI applied to financial reasoning, forecasting, and market simulation. LLMs, reinforcement learning, and agent-based models — tested on real data with honest results.

LLM reasoning forecasting prediction markets RL for execution agent simulation causal inference

Operating Loop

01 — Choose

Pick

Pick one market phenomenon, instrument, or mechanism to study.

02 — Define

Set

Set one hypothesis and the key questions around it.

03 — Run

Execute

Execute the smallest real experiment that produces evidence.

04 — Publish

Record

Record the result, even if it is partial or failed.

Editorial Boundary

No trading signals Published work is research, not strategy. No alpha, no predictions meant to trade on.
No learning notes Published work should not be passive study notes.
No reposting Do not summarize other people's blogs or papers as output.
Original only Keep the record tied to direct implementation and measurement.
Keep moving Progress matters more than chasing state-of-the-art optics.

Artifact Index

All published work, in one place.

Recent artifacts in one compact table. Use this as the quick reference layer: scan the latest published work, then open the artifact you need.

Type	Title	Reference	Meta	Action
First experiments arriving March 2026. Active. Artifacts in progress.