Bridging the divide in financial market forecasting: machine learners vs. financial economists
作者：
Highlights：
• An extensive benchmark in financial time series forecasting is performed.
• Best machine learning(ML) methods out-perform best econometric methods.
• The ML methodology employed significantly affects forecasting accuracy.
• Market maturity, forecast horizon & model-assessment method affect forecast accuracy.
• Evidence against the informational value of technical indicators.
摘要
•An extensive benchmark in financial time series forecasting is performed.•Best machine learning(ML) methods out-perform best econometric methods.•The ML methodology employed significantly affects forecasting accuracy.•Market maturity, forecast horizon & model-assessment method affect forecast accuracy.•Evidence against the informational value of technical indicators.
论文关键词：Financial time series forecasting,Market efficiency,Machine learning
论文评审过程：Received 20 November 2015, Revised 19 May 2016, Accepted 19 May 2016, Available online 25 May 2016, Version of Record 3 June 2016.
论文官网地址：https://doi.org/10.1016/j.eswa.2016.05.033
