02 / RESEARCH

AI & Machine Learning

Research into how artificial intelligence and machine learning techniques can be applied to financial data analysis, including supervised learning, unsupervised pattern discovery, feature engineering, and rigorous model evaluation.

Why It Matters

Machine learning offers tools for discovering complex, non-linear relationships in data that traditional statistical methods may miss. However, applying ML to financial data requires careful attention to overfitting, data leakage, and proper validation methodology.

Research Questions

Which machine learning architectures are most robust for financial time-series?
How can feature engineering improve model performance without introducing bias?
What evaluation metrics best capture real-world model utility?
How do we prevent overfitting in low signal-to-noise environments?

Methods

Gradient boosting (XGBoost, LightGBM)

Random forests and ensemble methods

Feature importance analysis (SHAP, permutation)

Temporal cross-validation

Hyperparameter optimization

Model interpretability techniques

Experiments

Research in progress. Current work focuses on comparing ensemble methods for time-series classification tasks and developing robust feature engineering pipelines.

Validation

Models are evaluated using temporal train-test splits, walk-forward validation, and out-of-sample testing. We report multiple metrics including precision, recall, and calibration alongside standard accuracy measures.

Gold AI Strategy Lab

An experimental research environment for studying machine-learning-assisted strategy research on historical gold-market data.

View project

AI Research Assistant

An experimental AI system designed to assist with research workflows, documentation, analysis, and hypothesis generation.