Built a Machine Learning Model for Stock Prediction That Quantifies Volatility More Effectively

I developed a machine learning model that fundamentally improves how volatility is quantified for stock price prediction. Traditional models either assume fixed volatility (Black-Scholes, GARCH) or overfit historical data without considering how uncertainty itself evolves. My approach models the relationship between knowns and unknowns probabilistically and structurally over time, making it highly effective for tracking volatility shifts.

Volatility is often treated as a derived statistical measure, but in reality, it is a manifestation of epistemic uncertainty—the interplay between what is known, what is unknown, and how these elements influence price movements. My model does not assume a rigid volatility structure but instead treats market behavior as a self-learning, self-revising probability space, where volatility emerges dynamically from new information, liquidity shifts, and trader behavior. By embedding epistemic feedback loops, the model updates its probabilistic estimations in real-time, ensuring that uncertainty itself is structurally integrated into the prediction process rather than being retrofitted as an afterthought. This epistemic approach provides a structural framework to understand volatility beyond statistical heuristics, allowing for a more robust interpretation of market conditions and price behaviors.

Most stock prediction models either ignore volatility, overfit historical patterns, or fail to structure uncertainty. My model explicitly reasons about how volatility evolves. Bayesian volatility modeling combined with machine learning adapts predictions dynamically to changing market conditions. The framework is built to be extensible for financial forecasting beyond simple price prediction.

The model accounts for real-time volatility fluctuations, making it more reliable in turbulent markets. It provides a structured way to measure market uncertainty, a key factor often missing in trading algorithms. It improves decision-making for quantitative traders and researchers looking to refine predictive strategies.

Collaboration and Access: The code is currently closed-source due to the confidential nature of the underlying mathematical framework, but I am open to collaborating with serious traders and researchers who are willing to invest in increasing their predictive power. If you are interested in applying this model to your trading strategy or would like to discuss potential collaboration, feel free to reach out in DMs. We will then decide on further collaboration.