The quantitative path to macro information efficiency
Financial markets are not information efficient with respect to macroeconomic information because data are notoriously ‘dirty’, relevant economic research is expensive, and establishing stable relations between macro data and market performance is challenging. However, statistical programming and packages have prepared the ground for great advances in macro information efficiency. The quantitative path to macro information efficiency leads over three stages. The first is elaborate in-depth data wrangling that turns raw macro data (and even textual information) into clean and meaningful time series whose frequency and time stamps accord with market prices. The second stage is statistical learning, be it supervised (to validate logical hypotheses), or unsupervised (to detect patterns). The third stage is realistic backtesting to verify the value of the learning process and to assess the commercial viability of a macro trading strategy.
