BIST Volatility Prediction and Risk Radar: Financial Applications of Gradient Boosting Models

This project builds a volatility prediction and risk monitoring system for the Istanbul Stock Exchange (BIST) of Turkey. It uses gradient boosting machine learning models such as LightGBM, XGBoost, and CatBoost, combined with feature engineering and risk monitoring components, to provide technical solutions for financial risk management. It has clear market targeting and practical value.

The Istanbul Stock Exchange (BIST) is Turkey's only stock exchange and an important member of emerging markets. It is affected by multiple factors such as geopolitics, monetary policy, and international trade, leading to high market volatility. Establishing an effective risk monitoring mechanism is an important prerequisite for investors to participate in this market.

The project uses three gradient boosting algorithms: XGBoost (stable and interpretable), LightGBM (fast training and low memory usage), and CatBoost (good at handling categorical features). These may be used for performance comparison or to integrate strategies to leverage their combined advantages.

Feature engineering is key. Common features include historical volatility, higher moments of returns, trading volume, technical indicators, macroeconomic indicators, etc. Market microstructure features (such as bid-ask spread, order flow imbalance) may also be considered, which directly affect the model's predictive ability.

The system includes real-time data collection, prediction engine, risk early warning (volatility exceeding threshold alerts), and visualization interface, realizing a complete process from prediction to monitoring and having practical application value.

Gradient boosting trees can handle non-linear relationships, output feature importance, have strong robustness, and high training efficiency, making them suitable for financial time series prediction scenarios.

Evaluation uses financial scenario indicators (directional accuracy, backtesting returns, etc.). Backtesting needs to avoid data leakage and look-ahead bias to ensure the results are true and valid.

The project demonstrates the application of machine learning in financial risk management. Its technology selection and design ideas are typical practices at the intersection of quantitative finance and machine learning, providing a reference case for readers interested in financial AI.