We propose a novel approach to the anonymisation of datasets through non-parametric learning of the underlying multivariate distribution of dataset features and generation of the new synthetic samples from the learned distribution. The main objective is to ensure equal (or better) performance of...

We propose to use a special type of generative neural networks - a Restricted Boltzmann Machine (RBM) - to build a powerful generator of synthetic market data that can replicate the probability distribution of the original market data. An RBM constructed with stochastic binary activation units...

We provide an axiomatic framework for exchange markets with a willingness-to-pay/willingness-to-accept discrepancy. First, we obtain a two parameter family of market invariants under price-scaling representing the excess demand. One of the parameters can be identified as endowment. The other is...

The "fair" taxation of digital business models is challenging. One of the key aspects - both policy makers and the public opinion consider as most pressuring - is the determination of intragroup transfer prices for intangibles used in digital business models. In this paper, we address the issue...