We build a framework to forecast out-of-sample any asset price movement in a tight window around the release of any macroeconomic indicator. As a case study, we examine the predictability of the EUR/USD exchange rate conditional on the market-based surprise in the first 60 min after the release of the United States Non-Farm Payrolls and ISM manufacturing index. We empirically investigate whether the findings of the literature obtained using an in-sample approach also hold in a real-time out-of-sample exercise. We demonstrate that the main econometric models lose accuracy in an out-of-sample application and cannot outperform a naive model. Additionally, we highlight that the main reason is the counterintuitive direction of a large number of small asset price movements around the release. Finally, we show that clustering the responses to the market-based surprises by their size improves the predictability, reconciling the role given by the literature to nonlinearities.

我们构建了一个框架来预测任何宏观经济指标发布前后的紧缩窗口中的任何资产价格变动的样本外。作为案例研究，我们以美国非农就业人数和 ISM 制造业指数发布后的前 60 分钟内基于市场的意外情况为条件，检验了 EUR/USD 汇率的可预测性。我们凭经验调查使用样本内方法获得的文献结果是否也适用于实时样本外练习。我们证明了主要的计量经济学模型在样本外应用中失去了准确性，并且无法胜过朴素模型。此外，我们强调，主要原因是发布前后大量小资产价格走势的反直觉方向。最后，