  An artificial agent for financial risk and returns' prediction is built with
a modular cognitive system comprised of interconnected recurrent neural
networks, such that the agent learns to predict the financial returns, and
learns to predict the squared deviation around these predicted returns. These
two expectations are used to build a volatility-sensitive interval prediction
for financial returns, which is evaluated on three major financial indices and
shown to be able to predict financial returns with higher than 80% success rate
in interval prediction in both training and testing, raising into question the
Efficient Market Hypothesis. The agent is introduced as an example of a class
of artificial intelligent systems that are equipped with a Modular Networked
Learning cognitive system, defined as an integrated networked system of machine
learning modules, where each module constitutes a functional unit that is
trained for a given specific task that solves a subproblem of a complex main
problem expressed as a network of linked subproblems. In the case of neural
networks, these systems function as a form of an "artificial brain", where each
module is like a specialized brain region comprised of a neural network with a
specific architecture.
