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.
Benchmark dataset for mid-price forecasting of limit order book data with machine learning methods.Journal of F orecasting, 37(8):852–866
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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.