This paper examines whether deep/machine learning can help find any statistical and/or economic evidence of out-of-sample bond return predictability when real-time, instead of fully-revised, macro variables are taken as predictors. First, when using pure real-time macro information alone, we...

Using the minute-frequency data of the top 30 coins listed on Binance, which represent 86% of the total dollar trading volume of the cryptocurrency market, we document strong evidence of cross-cryptocurrency return predictability. The lagged returns of other cryptocurrencies serve as significant...

We use boosted decision trees to generate daily out-of-sample forecasts of excess returns for Bitcoin and Ethereum, the two best-known and largest cryptocurrencies. The decision trees incorporate information from 39 predictors, including variables relating to cryptocurrency fundamentals,...