Accounting Research and Audit Practices | Journals

The IUP Journal of Accounting Research and Audit Practices:

Deep Learning and Asset Allocation: A Bibliometric Analysis Using AHP Approach

Article Details

Pub. Date	: Jan, 2022
Product Name	: The IUP Journal of Accounting Research and Audit Practices
Product Type	: Article
Product Code	: IJARAP40122
Author Name	: Anurika Vaish1, Nishit Kumar Srivastava2, Priyanshu Priyam Srivastava3 and Sarthak Sengupta*
Availability	: YES
Subject/Domain	: Finance
Download Format	: PDF Format
No. of Pages	: 15

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Abstract

The study explores investing, along with a review of deep learning-based asset allocation. The objective is to analyze the relevant studies done across the world on deep learning and asset allocation. It is found that there is an association between age and investment decisions. A ranking framework regarding preferred avenues for investment purposes is also proposed. The statistical tools and techniques used are based on qualitative and quantitative analyses. The study paves the way for further research and development by decentralizing Artificial Intelligence (AI)-based portfolio management.

Introduction

Investing is both science and art. It requires a thorough understanding of financial markets. A majority of people lack the knowledge or time to assess the various financial investment possibilities accessible to them; they seek the assistance of portfolio managers, who make trading decisions on their clients' behalf based on their risk appetite. Investing can be hard and daunting for the average person who is not directly involved in the financial sector. After all, from stocks and bonds to real estate and money market accounts, there is a plethora of possibilities. For the vast majority of people, finding the time and developing the expertise to research equities and then invest properly becomes extremely tough. The paper presents deep learning-based asset allocation approaches. Machine learning includes deep learning as a subset, whereas machine learning is a subset of Artificial Intelligence (AI). It has been