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Energy poverty prediction and effective targeting for just transitions with machine learning

Spandagos, Constantine; Tovar Reaños, Miguel Angel; … - 2023

The prevalence of energy poverty as a major challenge in numerous countries, the escalating energy crisis that … energy poverty schemes, enabling the accurate prediction of energy vulnerable households via objective, publicly available … algorithms, most of our analysis is performed using a Random Forest classifier. Our approach to explore energy poverty beyond …

Persistent link: https://www.econbiz.de/10014391248

Energy poverty prediction in the United Kingdom : a machine learning approach

Al Kez, Dlzar; Foley, Aoife; Abdul, Zrar Khald; … - In: Energy policy : the international journal of the … 184 (2024), pp. 1-17

Persistent link: https://www.econbiz.de/10014454912

Forecasting realized volatility using machine learning and mixed-frequency data (the case of the Russian stock market)

Pyrlik, Vladimir; Elizarov, Pavel; Leonova, Aleksandra - 2021

Persistent link: https://www.econbiz.de/10013163805

Using past violence and current news to predict changes in violence

Mueller, Hannes; Rauh, Christopher - 2022

Persistent link: https://www.econbiz.de/10013263446

Can ensemble machine learning methods predict stock returns for Indian banks using technical indicators?

Mohapatra, Sabyasachi; Mukherjee, Rohan; Roy, Arindam; … - In: Journal of risk and financial management : JRFM 15 (2022) 8, pp. 1-16

This paper develops ensemble machine learning models (XGBoost, Gradient Boosting, and AdaBoost in addition to Random Forest) for predicting stock returns of Indian banks using technical indicators. These indicators are based on three broad categories of technical analysis: Price, Volume, and...

Persistent link: https://www.econbiz.de/10013380477

Quarterly multidimensional poverty estimates in Mexico using machine learning algorithms

Rincón, Ratzanyel - In: Estudios económicos 38 (2023) 1, pp. 3-68

Persistent link: https://www.econbiz.de/10014280115

Predicting fiscal crises : a machine learning approach

Hellwig, Klaus-Peter - 2021

In this paper I assess the ability of econometric and machine learning techniques to predict fiscal crises out of sample. I show that the econometric approaches used in many policy applications cannot outperform a simple heuristic rule of thumb. Machine learning techniques (elastic net, random...

Persistent link: https://www.econbiz.de/10012612343

A Survey of Sentiment Analysis Through Machine Learning for Forecasting Indian Stock Market Trend

Dash, Anup Sekhar; Pathare, Suresh Bajirao - 2022

Public money is invested in the Indian Stock market through retail investors, Foreign institutional investors, Domestic Institutional investors. In recent times, the participation of retail investors in the Indian stock market has increased significantly. Many times, the investment decisions go...

Persistent link: https://www.econbiz.de/10013297285

Effectiveness of random forest model in predicting stock prices of solar energy companies in India

Meher, Bharat Kumar; Anand, Abhishek; Kumar, Sunil; … - In: International Journal of Energy Economics and Policy : IJEEP 14 (2024) 2, pp. 426-434

Persistent link: https://www.econbiz.de/10014496442

Machine learning on stock price movement forecast : the sample of the Taiwan stock exchange

Huang, Chin-sheng; Liu, Yi-Sheng - In: International journal of economics and financial issues … 9 (2019) 2, pp. 189-201

Persistent link: https://www.econbiz.de/10012149403

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