This is a summary of links featured on Quantocracy on Monday, 03/21/2022. To see our most recent links, visit the Quant Mashup. Read on readers!
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Measuring Hedge Fund Performance with Factor Model Monte Carlo [Light Finance]Due diligence for hedge funds presents a unique set of challenges for analysts and asset allocators. Funds often have significant discretion to invest across multiple asset classes and instruments. Funds may also deploy strategies of varying complexity ranging from well-known approaches such as equity long-short to more exotic schemes like capital-structure arbitrage. Investments might be in
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Risk-Managed Equity Exposure II [Factor Research]Relying on single entry and exit signals for trading introduces model risk However, simple tactical asset allocation strategies are surprisingly robust Necessity of signal diversification is questionable INTRODUCTION Last week, we explored a simple risk management system for an equity allocation that might be suitable for investors with a cautious outlook on stock markets. Given record-high
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How to find your own Safe Haven investing strategy [Raposa Trade]On Sunday September 2, 1666 a small fire started on the premises of the King's baker in London. By Thursday, the resulting inferno–later dubbed the Great Fire–had reduced much of the medieval City of London to ash. Writing of the fire in his diary, Samuel Pepys described a catastrophic scene of almost biblical proportions: "the conflagration was so universalthere was nothing heard
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Predicting volatility with neural networks [SR SV]Predicting realized volatility is critical for trading signals and position calibration. Econometric models, such as GARCH and HAR, forecast future volatility based on past returns in a fairly intuitive and transparent way. However, recurrent neural networks have become a serious competitor. Neural networks are adaptive machine learning methods that use interconnected layers of neurons.