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Pre-launch new product demand forecasting using the Bass model : a statistical and machine learning-based approach

Lee, Hakyeon; Kim, Sang-gook; Park, Hyun-woo; Kang, Pilsung - In: Technological forecasting & social change : an … 86 (2014), pp. 49-64

Persistent link: https://www.econbiz.de/10010479686

Predicting mortgage early delinquency with machine learning methods

Chen, Shunqin; Guo, Zhengfeng; Zhao, Xinlei - In: European journal of operational research : EJOR 290 (2021) 1, pp. 358-372

Persistent link: https://www.econbiz.de/10012436411

Deus ex machina? : a framework for macro forecasting with machine learning

Bolhuis, Marijn A.; Rayner, Brett - 2020

We develop a framework to nowcast (and forecast) economic variables with machine learning techniques. We explain how machine learning methods can address common shortcomings of traditional OLS-based models and use several machine learning models to predict real output growth with lower forecast...

Persistent link: https://www.econbiz.de/10012251288

Automated trading with boosting and expert weighting

Creamer, German; Freund, Yoav - In: Quantitative Finance 10 (2010) 4, pp. 401-420

this algorithm generates abnormal returns during the test period. Our experiments show that the boosting approach is able …

Persistent link: https://www.econbiz.de/10008675039

Machine learning advances for time series forecasting

Masini, Ricardo P.; Medeiros, Marcelo C.; Mendes, Eduardo F. - 2020

penalized regressions and ensemble of models. The nonlinear methods considered in the paper include shallow and deep neural … also consider ensemble and hybrid models by combining ingredients from different alternatives. Tests for superior …

Persistent link: https://www.econbiz.de/10012817069