A Comprehensive Review: Applicability of Deep Neural Networks in Business Decision Making and Market Prediction Investment

TL;DR

This paper reviews how deep neural networks are applied in business decision making and market prediction, highlighting their effectiveness and proposing a multi-modal neural network approach for improved financial forecasting.

Contribution

It provides a comprehensive overview of deep neural network applications in finance and introduces a multi-modal neural network framework for better prediction accuracy.

Findings

Deep neural networks excel in financial classification and prediction.

Combining multiple neural networks enhances robustness and scalability.

Deep learning methods outperform traditional techniques in financial analysis.

Abstract

Big data, both in its structured and unstructured formats, have brought in unforeseen challenges in economics and business. How to organize, classify, and then analyze such data to obtain meaningful insights are the ever-going research topics for business leaders and academic researchers. This paper studies recent applications of deep neural networks in decision making in economical business and investment; especially in risk management, portfolio optimization, and algorithmic trading. Set aside limitation in data privacy and cross-market analysis, the article establishes that deep neural networks have performed remarkably in financial classification and prediction. Moreover, the study suggests that by compositing multiple neural networks, spanning different data type modalities, a more robust, efficient, and scalable financial prediction framework can be constructed.