Quant Radio: Volatility Based Stock Trading with AI and Statistics

In this episode, we dive into VolTS — a fresh trading strategy that combines old-school statistical analysis with modern machine learning to predict stock trends based on volatility patterns. Discover how clustering, Granger causality tests, and volatility estimators like Yang-Zhang and Parkinson come together in a systematic framework focused on mid-volatility tech stocks. We explore its backtesting results, potential for outperforming buy-and-hold, and the risks of shifting market regimes. Whether you're a quant, trader, or curious about AI in finance, this one's packed with insight.

Topics:

Volatility clustering using K-means++

Predictive relationships via Granger Causality

Trend following vs. buy-and-hold performance

Risk metrics and anomaly filtering

Future directions: crypto markets, NLP, and hybrid models

Tune in for a smart, accessible breakdown of one of the more innovative approaches to algorithmic trading.

Find the full research paper here: https://community.quantopian.com/c/community-forums/volts-a-volatility-based-trading-system-to-forecast-stock-markets-trend-using-statistics-and-machine-learning-1c4e6f

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Quant Radio is an AI-generated podcast, intended to help people develop their knowledge and skills in Quant finance. This podcast is not intended to provide investment advice.