We introduce a framework to infer lead-lag networks between the states of elements of com- plex systems, determined at different timescales. As such networks encode the causal structure of a system, infering lead-lag networks for many pairs of timescales provides a global picture of the mutual...

Using more than 6.7 billions of trades, we explore how the tick-by-tick dynamics of limit order books depends on the aggregate actions of large investment funds on a much larger (quarterly) timescale. In particular, we find that the well-established long memory of market order signs is markedly...

We check the claims that data from Google Trends contain enough data to predict future financial index returns. We first discuss the many subtle (and less subtle) biases that may affect the back-test of a trading strategy, particularly when based on such data. Expectedly, the choice of keywords...

We systematically investigate the links between price returns and ESG features. We propose across-validation scheme with random company-wise validation to mitigate the relative initial lackof quantity and quality of ESG data, which allows us to use most of the latest and best data toboth train...