-learning techniques to analyze the efficiency of forecasts. To this end, I extract information from forecast reports using a combination …

This study aims to forecast oil prices using evolutionary techniques such as gene expression programming (GEP) and … implications for both theory and practice …

This paper aims to forecast the Market Risk premium (MRP) in the US stock market by applying machine learning … forecast the Market Risk Premium in a daily basis using Artificial Neural Networks (ANNs). Second, it is not based on a …

-switching models, and forecast combination to predict the dynamics in the S&P 500. First, we aggregate the weekly information of 115 …

We assess financial theory-based and machine learning-implied measurements of stock risk premia by comparing the … preferable to rely on a theory-based approach instead of engaging in the computerintensive hyper-parameter tuning of statistical … models. The theory-based approach also delivers a solid performance at the one year horizon, at which only one machine …

predictability, popular predictors from the literature fail to outperform the simple historical average benchmark forecast in out … model restrictions, forecast combination, diffusion indices, and regime shifts—improve forecasting performance by addressing …

development and production processes. With this in mind, this paper proposes new multivariate models to forecast monthly car sales … data using economic variables and Google online search data. An out-of-sample forecasting comparison with forecast horizons … forecast horizons. These results also hold after several robustness checks which consider nonlinear models, different out …

quarterly frequency. We forecast gross domestic product (GDP) for two German states (Free State of Saxony and Baden- Württemberg …-indicator, pooled and factor forecasts in a pseudo real-time setting. Our results show that we can significantly increase forecast …