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Continue reading →: Quantitative Event-Driven Modeling: BTC Balance Sheet Shocks with SEC EDGAR Filings in RExecutive Summary MicroStrategy (MSTR) has fundamentally transitioned from a traditional enterprise software firm into an equity-based Bitcoin (BTC) holding vehicle and treasury operation. Evaluating MSTR using conventional corporate finance metrics (such as Price-to-Earnings or EBITDA multiples) fails to capture the core driver of its equity valuation: the dynamic Net Asset…
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Continue reading →: Skewness-Managed Portfolios: A Practical Guide with RIntroduction Portfolio construction often relies on mean–variance optimization or factor models. Yet, recent research highlights the importance of skewness—the third statistical moment—as a driver of asset returns. Assets with lottery-like payoffs (high positive skewness) tend to be overpriced, while negatively skewed assets are often underpriced. A 66‑page study demonstrates that…
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Continue reading →: Breaking the Python Barrier: Building a Pure R-Native DeepAR Engine with LibTorchDeep learning for time series forecasting in R has historically faced a major architectural hurdle: Python overhead. Frameworks like modeltime.gluonts provide interface wrappers around AWS GluonTS, but they rely on a complex execution chain passing through reticulate, virtual environments, Python serialization, and MXNet/PyTorch backends. To overcome the performance bottlenecks and…
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Continue reading →: Analyzing Financial Trends: Kalman Filtering for Gold vs BitcoinIn their paper “A Synchronized Multi‑IMU Wearable System for Tracking of Joint‑Angles in Sports Motion Analysis” (arXiv:2607.26027v1), Samarasekera and colleagues set out to solve a very practical problem: how to reliably measure joint angles in dynamic sports movements using wearable IMUs. Their goal was to design a synchronized pipeline that…

