Predicting S P500 Index direction with Transfer Learning and a Causal Graph as main Input

11/26/2020
by   Djoumbissie David Romain, et al.
0

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 12 years test period which include three unstable and difficult sub-period to predict.

READ FULL TEXT

page 3

page 5

research
12/31/2021

Macroeconomic and financial management in an uncertain world: What can we learn from complexity science?

This paper discusses serious drawbacks of existing knowledge in macroeco...
research
09/09/2021

Tracking Turbulence Through Financial News During COVID-19

Grave human toll notwithstanding, the COVID-19 pandemic created uniquely...
research
10/28/2022

Multiresolution Signal Processing of Financial Market Objects

Financial markets are among the most complex entities in our environment...
research
09/09/2015

Transfer learning approach for financial applications

Artificial neural networks learn how to solve new problems through a com...
research
05/20/2020

Learning Undirected Graphs in Financial Markets

We investigate the problem of learning undirected graphical models under...
research
07/09/2019

Dreaming machine learning: Lipschitz extensions for reinforcement learning on financial markets

We develop a new topological structure for the construction of a reinfor...

Please sign up or login with your details

Forgot password? Click here to reset