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This paper applies machine learning algorithms to the modeling of realized betas for the purposes of forecasting stock systematic risk. Forecast horizons range from 1 week up to 1 month. The machine learning algorithms employed are ridge regression, decision tree learning, adaptive boosting,...
Persistent link: https://www.econbiz.de/10013251197
Instead of relying solely on data of a single time series it is possible to use information of parallel, similar time series to improve prediction quality. Our data set consists of microeconomic data of daily store deposits from a large number of different stores. We analyze how prediction...
Persistent link: https://www.econbiz.de/10012838913
This paper extends the machine learning methods developed in Han et al. (2019) for forecasting cross-sectional stock returns to a time-series context. The methods use the elastic net to refine the simple combination return forecast from Rapach et al. (2010). In a time-series application focused...
Persistent link: https://www.econbiz.de/10012865775
We employ forty-seven different algorithms to forecast Australian log real house prices and growth rates, and compare their ability to produce accurate out-of-sample predictions. The algorithms, which are specified in both single- and multi-equation frameworks, consist of traditional time-series...
Persistent link: https://www.econbiz.de/10012866930
Forecasting plays an essential role in energy economics. With new challenges and use cases in the energy system, forecasts have to meet more complex requirements, such as increasing temporal and spatial resolution of data. The concept of machine learning can meet these requirements by providing...
Persistent link: https://www.econbiz.de/10012649104
TThe rapid development of machine learning (ML) provides new tools for predicting financial-economic time series. However, this paper argues that, from the perspective of time series, ML prediction is merely a one-step static forecasting, which is usually good but of limited use. This study...
Persistent link: https://www.econbiz.de/10012846465
This study extends previous work applying unsupervised machine learning to commodity markets. "Clustering Commodity Markets in Space and Time" [DOI: 10.1016/j.resourpol.2021.102162] examined returns and volatility in commodity markets. That paper supported the conventional ontology of commodity...
Persistent link: https://www.econbiz.de/10014356740
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