Digital marketplaces processing billions of dollars annually represent critical infrastructure in sociotechnical ecosystems, yet their performance optimization lacks principled measurement frameworks that can inform algorithmic governance decisions regarding market efficiency and fairness from complex market data. By looking at orderbook data from double auction markets alone, because bids and asks do not represent true maximum willingnesses to buy and true minimum willingnesses to sell, there is little an economist can say about the market's actual performance in terms of allocative efficiency. We turn to experimental data to address this issue, `inverting' the standard induced value approach of double auction experiments. Our aim is to predict key market features relevant to market efficiency, particularly allocative efficiency, using orderbook data only -- specifically bids, asks and price realizations, but not the induced reservation values -- as early as possible. Since there is no established model of strategically optimal behavior in these markets, and because orderbook data is highly unstructured, non-stationary and non-linear, we propose quantile-based normalization techniques that help us build general predictive models. We develop and train several models, including linear regressions and gradient boosting trees, leveraging quantile-based input from the underlying supply-demand model. Our models can predict allocative efficiency with reasonable accuracy from the earliest bids and asks, and these predictions improve with additional realized price data. The performance of the prediction techniques varies by target and market type. Our framework holds significant potential for application to real-world market data, offering valuable insights into market efficiency and performance, even prior to any trade realizations.

翻译：每年处理数十亿美元交易的数字市场，是社会经济技术生态系统中的关键基础设施。然而，其性能优化缺乏原则性的衡量框架，这些框架本应能从复杂的市场数据中，为关于市场效率和公平性的算法治理决策提供信息。仅观察来自双向拍卖市场的订单簿数据，由于买价和卖价并不代表真实的最高购买意愿和真实的最低出售意愿，经济学家很难就市场在配置效率方面的实际表现做出判断。我们转而利用实验数据来解决这个问题，“逆向”使用双向拍卖实验中标准的诱导价值法。我们的目标是尽可能早地仅利用订单簿数据——具体而言是买价、卖价和实现价格，但排除诱导保留价——来预测与市场效率（尤其是配置效率）相关的关键市场特征。由于这些市场中不存在既定的策略最优行为模型，且订单簿数据高度非结构化、非平稳和非线性，我们提出了基于分位数的归一化技术，以帮助构建通用的预测模型。我们开发并训练了多种模型，包括线性回归和梯度提升树，利用了来自基础供需模型的量化输入。我们的模型能够从最早的买卖报价出发，以合理的准确度预测配置效率，并且这些预测会随着更多实现价格数据的加入而改善。预测技术的性能因目标市场和市场类型而异。我们的框架在应用于真实市场数据方面具有显著潜力，甚至在交易实现之前，就能为市场效率和性能提供有价值的见解。