  The finance industry is producing an increasing amount of datasets that
investment professionals can consider to be influential on the price of
financial assets. These datasets were initially mainly limited to exchange
data, namely price, capitalization and volume. Their coverage has now
considerably expanded to include, for example, macroeconomic data, supply and
demand of commodities, balance sheet data and more recently extra-financial
data such as ESG scores. This broadening of the factors retained as influential
constitutes a serious challenge for statistical modeling. Indeed, the
instability of the correlations between these factors makes it practically
impossible to identify the joint laws needed to construct scenarios.
Fortunately, spectacular advances in Deep Learning field in recent years have
given rise to GANs. GANs are a type of generative machine learning models that
produce new data samples with the same characteristics as a training data
distribution in an unsupervised way, avoiding data assumptions and human
induced biases. In this work, we are exploring the use of GANs for synthetic
financial scenarios generation. This pilot study is the result of a
collaboration between Fujitsu and Advestis and it will be followed by a
thorough exploration of the use cases that can benefit from the proposed
solution. We propose a GANs-based algorithm that allows the replication of
multivariate data representing several properties (including, but not limited
to, price, market capitalization, ESG score, controversy score,. . .) of a set
of stocks. This approach differs from examples in the financial literature,
which are mainly focused on the reproduction of temporal asset price scenarios.
We also propose several metrics to evaluate the quality of the data generated
by the GANs. This approach is well fit for the generation of scenarios, the
time direction simply arising as a subsequent (eventually conditioned)
generation of data points drawn from the learned distribution. Our method will
allow to simulate high dimensional scenarios (compared to $\lesssim10$ features
currently employed in most recent use cases) where network complexity is
reduced thanks to a wisely performed feature engineering and selection.
Complete results will be presented in a forthcoming study.
