Financial Risk and Returns Prediction with Modular Networked Learning

TL;DR

This paper presents a modular neural network system that predicts financial risks and returns, achieving over 80% success in interval predictions and challenging the Efficient Market Hypothesis.

Contribution

It introduces a novel modular networked learning framework for financial prediction, integrating multiple neural modules for risk and return estimation.

Findings

Achieved over 80% success rate in interval prediction on major indices.

Demonstrated the effectiveness of modular neural systems in financial forecasting.

Challenged the Efficient Market Hypothesis with high-accuracy predictions.

Abstract

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.