This is a summary of links recently featured on Quantocracy as of Monday, 10/06/2025. To see our most recent links, visit the Quant Mashup. Read on readers!
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Book and Workshop Introduction: Generative AI for Trading and Asset Management [EP Chan]The world of finance is no stranger to artificial intelligence. Most quantitative asset managers are already familiar with discriminative models, for example, given yesterdays return, what is the probability that todays return will be positive? Many are also familiar with reinforcement learning, used for tasks like optimizing order execution or figuring out how to set the best capital
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Gold s Rally and the Gold Mining Stocks Trap [Quantpedia]Gold has been in the headlines lately as it climbs to new highs, prompting many investors to look for ways to benefit from the rally. However, many institutional investors such as mutual funds and pension funds face restrictions on buying physical gold or gold-backed ETFs. Instead, they often turn to gold mining stocks to gain indirect exposure to golds price. That approach seems
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The Role of Data in Financial Modeling and Risk Management [Relative Value Arbitrage]Much emphasis has been placed on developing accurate and robust financial models, whether for pricing, trading, or risk management. However, a crucial yet often overlooked component of any quantitative system is the reliability of the underlying data. In this post, we explore some issues with financial data and how to address them. How to Deal with Missing Financial Data? In the financial
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Employing volatility of volatility in long-term volatility forecasts [Outcast Beta]We demonstrate how simple long-term volatility forecasts can be improved by incorporating the volatility of short-term volatility into forecasting models. The theoretical framework for modelling volatility of short-term volatility, along with its role in long-term forecasts, will be outlined. Empirical tests will then illustrate the value of including volatility of volatility measures in practice.
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Weekly Research Recap [Quant Seeker]State-Dependent Market (In)Efficiency in Cryptocurrency Markets (Barak, Razmi, and Mousavi) Cryptocurrency return predictability is regime-dependent. This paper tests strategies based on Directional Change events, where a new trend is confirmed only once price moves a fixed percentage from the last extreme. A machine-learning model adaptively selects the optimal threshold each day. From