  We propose a unified multi-tasking framework to represent the complex and
uncertain causal process of financial market dynamics, and then to predict the
movement of any type of index with an application on the monthly direction of
the S&P500 index. our solution is based on three main pillars: (i) the use of
transfer learning to share knowledge and feature (representation, learning)
between all financial markets, increase the size of the training sample and
preserve the stability between training, validation and test sample. (ii) The
combination of multidisciplinary knowledge (Financial economics, behavioral
finance, market microstructure and portfolio construction theories) to
represent a global top-down dynamics of any financial market, through a graph.
(iii) The integration of forward looking unstructured data, different types of
contexts (long, medium and short term) through latent variables/nodes and then,
use a unique VAE network (parameter sharing) to learn simultaneously their
distributional representation. We obtain Accuracy, F1-score, and Matthew
Correlation of 74.3 %, 67 % and 0.42 above the industry and other benchmark on
12 years test period which include three unstable and difficult sub-period to
predict.
