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- W2783451642 abstract "We describe how sets of many unusual prior events can be linked and used to predict subsequent unusual events. We apply the technique to extract useful meaning from the massive, seemingly random co-incidences in the dynamics of the US stock market. We show how to qualify the links, to visualize their evolution, and to determine their similarity. We use the similarity for unsupervised clustering of sets of links. We make daily predictions over ten years that, when implemented, show excess returns in large capitalization equities in the US stock market. We begin from an insight about how our new puppy learns that she is about to get her evening walk well before we take out her leash. Our learning algorithm extends her efforts." @default.
- W2783451642 created "2018-01-26" @default.
- W2783451642 creator A5087298778 @default.
- W2783451642 date "2017-12-01" @default.
- W2783451642 modified "2023-09-23" @default.
- W2783451642 title "Linking many unusual co-incidences" @default.
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- W2783451642 doi "https://doi.org/10.1109/bigdata.2017.8258104" @default.
- W2783451642 hasPublicationYear "2017" @default.
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