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A Random Forests Approach to Predicting Clean Energy Stock Prices
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- Ghosh, Indranil & Jana, Rabin K., 2024. "Clean energy stock price forecasting and response to macroeconomic variables: A novel framework using Facebook's Prophet, NeuralProphet and explainable AI," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
- Henriques, Irene & Sadorsky, Perry, 2023. "Forecasting rare earth stock prices with machine learning," Resources Policy, Elsevier, vol. 86(PA).
- Htet Htet Htun & Michael Biehl & Nicolai Petkov, 2023. "Survey of feature selection and extraction techniques for stock market prediction," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-25, December.
- Mahalakshmi Manian & Parthajit Kayal, 2024. "Detecting and Forecasting Financial Bubbles in The Indian Stock Market Using Machine Learning Models," Working Papers 2024-270, Madras School of Economics,Chennai,India.
- Chigozie Andy Ngwaba, 2025. "Forecasting Covered Call Exchange-Traded Funds (ETFs) Using Time Series, Machine Learning, and Deep Learning Models," JRFM, MDPI, vol. 18(3), pages 1-15, February.
- Perry Sadorsky, 2021. "Predicting Gold and Silver Price Direction Using Tree-Based Classifiers," JRFM, MDPI, vol. 14(5), pages 1-21, April.
- Nawaf Almaskati, 2022. "Machine learning in finance: Major applications, issues, metrics, and future trends," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 9(03), pages 1-32, September.
- Liu, Yingnan & Bu, Ningbo & Li, Zhiqiang & Zhang, Yongmin & Zhao, Zhenyu, 2025. "AT-FinGPT: Financial risk prediction via an audio-text large language model," Finance Research Letters, Elsevier, vol. 77(C).
- Day, Min-Yuh & Ni, Yensen, 2023. "Do clean energy indices outperform using contrarian strategies based on contrarian trading rules?," Energy, Elsevier, vol. 272(C).
- Sadorsky, Perry, 2022. "Forecasting solar stock prices using tree-based machine learning classification: How important are silver prices?," The North American Journal of Economics and Finance, Elsevier, vol. 61(C).
- Cecilia Martinez-Castillo & Gonzalo Astray & Juan Carlos Mejuto, 2021. "Modelling and Prediction of Monthly Global Irradiation Using Different Prediction Models," Energies, MDPI, vol. 14(8), pages 1-16, April.
- Munish Khanna & Mohak Kulshrestha & Law K. Singh & Shankar Thawkar & Kapil Shrivastava, 2022. "Performance Evaluation of Machine Learning Algorithms for Stock Price and Stock Index Movement Prediction Using Trend Deterministic Data Prediction," International Journal of Applied Metaheuristic Computing (IJAMC), IGI Global, vol. 13(1), pages 1-30, January.
- Wei Liu & Yoshihisa Suzuki & Shuyi Du, 2024. "Forecasting the Stock Price of Listed Innovative SMEs Using Machine Learning Methods Based on Bayesian optimization: Evidence from China," Computational Economics, Springer;Society for Computational Economics, vol. 63(5), pages 2035-2068, May.
- Ghaemi Asl, Mahdi & Adekoya, Oluwasegun Babatunde & Rashidi, Muhammad Mahdi & Oliyide, Johnson Ayobami & Rajab, Sahel, 2024. "A new approach to forecasting Islamic and conventional oil and gas stock prices," International Review of Economics & Finance, Elsevier, vol. 96(PA).
- Audi, Marc & Poulin, Marc & Ahmad, Khalil & Ali, Amjad, 2025. "Quantile Analysis of Oil Price Shocks and Stock Market Performance: A European Perspective," MPRA Paper 124295, University Library of Munich, Germany.
- Min-Yuh Day & Yensen Ni & Chinning Hsu & Paoyu Huang, 2022. "Do Investment Strategies Matter for Trading Global Clean Energy and Global Energy ETFs?," Energies, MDPI, vol. 15(9), pages 1-15, May.
- Ivan Borisov Todorov & Fernando Sánchez Lasheras, 2022. "Forecasting Applied to the Electricity, Energy, Gas and Oil Industries: A Systematic Review," Mathematics, MDPI, vol. 10(21), pages 1-15, October.
- Wenfeng Ma & Yuxuan Hong & Yuping Song, 2024. "On Stock Volatility Forecasting under Mixed-Frequency Data Based on Hybrid RR-MIDAS and CNN-LSTM Models," Mathematics, MDPI, vol. 12(10), pages 1-21, May.
- Farid Bagheri & Diego Reforgiato Recupero & Espen Sirnes, 2023. "Leveraging Return Prediction Approaches for Improved Value-at-Risk Estimation," Data, MDPI, vol. 8(8), pages 1-22, August.
- Ghosh, Pushpendu & Neufeld, Ariel & Sahoo, Jajati Keshari, 2022. "Forecasting directional movements of stock prices for intraday trading using LSTM and random forests," Finance Research Letters, Elsevier, vol. 46(PA).
- Gaoxiu Qiao & Yijun Pan & Chao Liang & Lu Wang & Jinghui Wang, 2024. "Forecasting Chinese crude oil futures volatility: New evidence based on dual feature processing of large‐scale variables," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(7), pages 2495-2521, November.
- Madadkhani, Shiva & Ikonnikova, Svetlana, 2024. "Toward high-resolution projection of electricity prices: A machine learning approach to quantifying the effects of high fuel and CO2 prices," Energy Economics, Elsevier, vol. 129(C).
- Marian Pompiliu Cristescu & Raluca Andreea Nerisanu & Dumitru Alexandru Mara & Simona-Vasilica Oprea, 2022. "Using Market News Sentiment Analysis for Stock Market Prediction," Mathematics, MDPI, vol. 10(22), pages 1-12, November.
- Diana T. Mosa & Shaymaa E. Sorour & Amr A. Abohany & Fahima A. Maghraby, 2024. "CCFD: Efficient Credit Card Fraud Detection Using Meta-Heuristic Techniques and Machine Learning Algorithms," Mathematics, MDPI, vol. 12(14), pages 1-27, July.
- Day, Min-Yuh & Ni, Yensen, 2023. "The profitability of seasonal trading timing: Insights from energy-related markets," Energy Economics, Elsevier, vol. 128(C).
- Qiang Gao & Xinzhu Zhou & Kunpeng Zhang & Li Huang & Siyuan Liu & Fan Zhou, 2022. "Incorporating Interactive Facts for Stock Selection via Neural Recursive ODEs," Papers 2210.15925, arXiv.org.