Forecasting solar stock prices using tree-based machine learning classification: How important are silver prices? |
| |
Institution: | 1. Department of Computer Information Systems, Jordan University of Science and Technology, Irbid 22110, Jordan;;2. College of Engineering and IT, University of Dubai, Academic City, Dubai 14143, United Arab Emirates;3. Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman 19328, Jordan;;4. Faculty of Computer Sciences and Informatics, Amman Arab University, Amman 11953, Jordan; |
| |
Abstract: | Solar energy is one of the fastest growing sources of electricity generation. Forecasting solar stock prices is important for investors and venture capitalists interested in the renewable energy sector. This paper uses tree-based machine learning methods to forecast the direction of solar stock prices. The feature set used in prediction includes a selection of well-known technical indicators, silver prices, silver price volatility, and oil price volatility. The solar stock price direction prediction accuracy of random forests, bagging, support vector machines, and extremely randomized trees is much higher than that of logit. For a forecast horizon of between 8 and 20 days, random forests, bagging, support vector machines, and extremely randomized trees achieve a prediction accuracy greater than 85%. Although not as prominent as technical indicators like MA200, WAD, and MA20, oil price volatility and silver price volatility are also important predictors. An investment portfolio trading strategy based on trading signals generated from the extremely randomized trees stock price direction prediction outperforms a simple buy and hold strategy. These results demonstrate the accuracy of using tree-based machine learning methods to forecast the direction of solar stock prices and adds to the broader literature on using machine learning techniques to forecast stock prices. |
| |
Keywords: | Forecasting Machine learning Random forests Solar energy Stock prices |
本文献已被 ScienceDirect 等数据库收录! |
|