Quantitative Research

Emerging quantitative work in risk, data, statistics, and modelling.

This page positions Milton's professional direction: quantitative finance study, analytical projects, research interests, and the habit of making assumptions visible.

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Research operating system

Question, data, model, uncertainty, explanation.

Research objective

Turn interest into reviewable evidence.

The goal is not to sound technical. The goal is to show how a problem was framed, which data was used, how the method worked, what the result means, and where the limitations sit.

Question Data Model Test Explain

Research interests

The topics that connect Milton's study path to applied work.

Statistics

Inference and uncertainty

Regression, distributions, hypothesis testing, simulation, sampling, and separating signal from noise.

Risk Analysis

Scenario and stress thinking

Portfolio risk, drawdowns, volatility, sensitivity analysis, and communicating uncertainty clearly.

Quantitative Finance

Markets, instruments, and models

Financial mathematics, asset behaviour, index structure, risk-return tradeoffs, and model limits.

Data Engineering

Reliable analytical pipelines

ETL, validation, medallion-style layers, SQL, reporting outputs, and reproducible project structure.

Python

Analysis and automation

Notebooks, scripts, modelling prototypes, visual explainers, data cleaning, and repeatable workflows.

Operations Research

Structured decision support

Optimization, constraints, allocation, process thinking, and practical decision frameworks.

Research method

What makes the work employer-friendly.

A recruiter or collaborator should not have to guess what the project proves. Each piece of work should make the question, tools, assumptions, outputs, and lessons easy to review.

01

Frame the problem

Define the research question, audience, assumptions, and decision relevance.

02

Show the evidence

Use clean data, transparent methods, tested outputs, and reproducible notes.

03

Explain the limitation

State what the model can and cannot say. Good judgement matters as much as output.

Capability map

How the research page should be read.

For recruiters

Evidence of analytical growth, technical direction, and professional judgement.

For students

A bridge from classroom mathematics to data, risk, and financial-market thinking.

For collaborators

Clear areas where projects, research notes, or education products can be developed.

For market learners

Context for Market Lab as education and research, not trading advice.

Roadmap

From learning path to portfolio evidence.

Now

Clarify the research platform

Separate tutoring, research, portfolio work, and Market Lab boundaries.

Document statistics, risk, data engineering, and index-market structure concepts.

Later

Build polished case studies

Turn projects into reviewable work with assumptions, visuals, results, and lessons.