In this paper, we investigate the effects of machine translation tools on translated texts and the accuracy of authorship and translator attribution of translated texts. We show that the more translation performed on a text by a specific machine translation tool, the more effects unique to that translator are observed. We also propose a novel method to perform machine translator and authorship attribution of translated texts using a feature set that led to 91.13% and 91.54% accuracy on average, respectively. We claim that the features leading to highest accuracy in translator attribution are translator-dependent features and that even though translator-effect-heavy features are present in translated text, we can still succeed in authorship attribution. These findings demonstrate that stylometric features of the original text are preserved at some level despite multiple consequent translations and the introduction of translator-dependent features. The main contribution of our work is the discovery of a feature set used to accurately perform both translator and authorship attribution on a corpus of diverse topics from the twenty-first century, which has been consequently translated multiple times using machine translation tools.