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Continue reading →: Differential Machine Learning with Twin Networks in R: Forecasting Bitcoin with Volatility ProxiesIntroduction Differential Machine Learning (DML), as introduced in the recent arXiv paper (Differential Machine Learning for 0DTE Options with Stochastic Volatility and Jumps), extends supervised learning by incorporating not only function values but also their derivatives. In financial contexts, this often means sensitivities such as Greeks. However, when direct derivatives…
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Continue reading →: Momentum Investing Enhanced by Microsoft Foundry-Hosted Large Language ModelLLM-enhanced momentum investing combines traditional momentum signals with real-time news interpretation by large language models (LLMs). The idea is straightforward: stocks with strong past returns are candidates for momentum portfolios, but their inclusion and weight are refined by LLM-generated sentiment scores derived from firm-specific news. This hybrid approach improves risk-adjusted…
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Continue reading →: Modeling Bitcoin Volatility Through Structural Breaks: A Compositional PerspectiveRecent advances in time series modeling have emphasized the importance of structural breaks—abrupt changes in the underlying dynamics of financial or economic data. The paper “Directional-Shift Dirichlet ARMA Models for Compositional Time Series with Structural Break Intervention” (Katz, 2026) introduces a Bayesian framework that captures these breaks using three interpretable…
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Continue reading →: Gold/Silver Ratio: GenAI with Quant Agent on Azure AI Foundry1. Introduction: The Strategic Edge of Agentic Finance In the contemporary landscape of quantitative finance, the bottleneck is no longer data availability, but the speed of insight generation. Leveraging the Microsoft AI Foundry ecosystem, we have moved beyond static scripting into the realm of Autonomous Financial Agents. This article explores…
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I’m Selcuk Disci
The DataGeeek focuses on machine learning, deep learning, and Generative AI in data science using financial data for educational and informational purposes.
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