This is a summary of links featured on Quantocracy on Monday, 02/03/2020. To see our most recent links, visit the Quant Mashup. Read on readers!
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Sneak Peak: Robustness to Noise [Allocate Smartly]This is an early preview of a new analytical tool well be adding to our platform later this month. Learn more about what we do. Broadly speaking, the goal of tactical asset allocation is to take advantage of broad market trends via trend-following and/or momentum. Those trends can be difficult to identify because of noise; short-term price fluctuations that confuse/distort the underlying
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Can Managed Futures Offset Equity Losses? [Flirting with Models]Managed futures strategies have historically provided meaningful positive returns during left-tail equity events. Yet as a trading strategy, this outcome is by no means guaranteed. While trend following is mechanically convex, the diverse nature of managed futures programs may actually prevent the strategy from offsetting equity market losses. We generate a large number of random managed
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Machine learning and macro trading strategies [SR SV]Machine learning can improve macro trading strategies, mainly because it makes them more flexible and adaptable, and generalizes knowledge better than fixed rules or trial-and-error approaches. Within the constraints of pre-set hyperparameters machine learning is continuously and autonomously learning from new data, thereby challenging or refining prevalent beliefs. Machine learning and expert
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Sentiment and Factor Performance [Factor Research]Stock sentiment can be aggregated from public sources using a big data approach Results indicate that sentiment has some predictability for short-term factor performance Positive sentiment resulted in higher subsequent returns than negative sentiment INTRODUCTION Albert Einstein famously stated that information is not knowledge, which is more relevant than ever as the amount of available