We employ deep learning in forecasting high-frequency returns at multiple horizons for 115 stocks traded on Nasdaq using order book information at the most granular level. While raw order book states can be used as input to the forecasting models, we achieve state-of-the-art predictive accuracy...

We study how banks manage their default risk to optimally negotiate quantities and prices of contracts in over-the-counter markets. We show that costly actions exerted by banks to reduce their default probabilities are inefficient. Negative externalities due to counterparty concentration may...