A random forest-based model for crypto asset forecasts in futures markets with out-of-sample prediction

In this study, a price prediction model for futures markets of crypto assets is presented. Random Forest was used to study three scenarios as a function of input variables: technical indicators, candlestick patterns and both simultaneously. In turn, the model parameters, the time intervals, and the most suitable investment horizons were studied. In addition to showing the results from the model, a one-year out-of-sample prediction was simulated. The entire year of 2020 was chosen because the three possible stock market scenarios occurred in this year: a sideways market, a bear market resulting from the global pandemic and an end-of-year bull market. Last, this out-of-sample simulation was analyzed as a real operation, that is, by retraining the model after each new collection of data, so that the model had the maximum information at all times. In conclusion, using candlestick patterns instead of technical indicators, improves the efficiency of the results.     

“互联网+”背景下林业电商发展策略探究

“互联网+”背景下,电子商务已经成为许多传统产业转型的重要手段,同时催生了众多热门的第三方购物平台,现代信息技术与电商的融合也为许多传统产业注入了新的活力,让传统产业变得“信息化、智慧化”,实现了质的飞跃。林业作为我国传统的基础产业,正处于向电商转型的初级阶段。以林业电商为探讨对象,分析了我国林产品销售过程中存在的共性问题,并结合实际与经验从人才引进、物流运输、品牌打造、信息获取等方面提出了相关建议。