An inverse framework uses quantile-normalized orderbook data and machine learning models to predict allocative efficiency in double auctions from bids, asks, and prices without knowing induced values.
Modelling high-frequency limit order book dynamics with support vector machines.Quantitative Finance, 15(8):1315–1329
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An `Inverse' Experimental Framework to Estimate Market Efficiency
An inverse framework uses quantile-normalized orderbook data and machine learning models to predict allocative efficiency in double auctions from bids, asks, and prices without knowing induced values.