Kamp, MichaelMichaelKampBoley, MarioMarioBoleyGärtner, ThomasThomasGärtner2022-03-132022-03-132015https://publica.fraunhofer.de/handle/publica/39627010.1137/1.9781611973440.74Corporate earnings are a crucial indicator for investment and business valuation. Despite their importance and the fact that classic econometric approaches fail to match analyst forecasts by orders of magnitude, the automatic prediction of corporate earnings from public data is not in the focus of current machine learning research. In this paper, we present for the first time a fully automatized machine learning method for earnings prediction that at the same time a) only relies on publicly available data and b) can outperform human analysts. The latter is shown empirically in an experiment involving all S&P 100 companies in a test period from 2008 to 2012. The approach employs a simple linear regression model based on a novel feature space of stock market prices and their pairwise correlations. With this work we follow the recent trend of nowcasting, i.e., of creating accurate contemporary forecasts of undisclosed target values based on publicly observable proxy variables.en005conference paper