Researchers have developed a novel experimental framework to estimate market efficiency using only orderbook data, bypassing the need for traditional induced value experiments. This approach aims to predict key market features, such as allocative efficiency, from early bid and ask data, with predictions improving as more realized price data becomes available. The framework utilizes quantile-based normalization techniques to build predictive models, including linear regressions and gradient boosting trees, and has potential applications for real-world market analysis. AI
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IMPACT Offers a new method for analyzing market performance using orderbook data, potentially improving algorithmic governance.
RANK_REASON This is a research paper published on arXiv detailing a new experimental framework for market efficiency estimation.