A Big data analytical framework for portfolio optimization

TL;DR

This paper presents a comprehensive big data framework for portfolio optimization that integrates structured and unstructured data to improve asset selection, weighting, and management for better investment decisions.

Contribution

It introduces a novel 5-stage methodology combining DEA, text mining, clustering, ranking, and heuristics for portfolio optimization using big data.

Findings

Effective asset selection and weighting using the proposed framework.

Improved risk minimization and return maximization.

Enhanced decision-making with integrated data analysis.

Abstract

With the advent of Web 2.0, various types of data are being produced every day. This has led to the revolution of big data. Huge amount of structured and unstructured data are produced in financial markets. Processing these data could help an investor to make an informed investment decision. In this paper, a framework has been developed to incorporate both structured and unstructured data for portfolio optimization. Portfolio optimization consists of three processes: Asset selection, Asset weighting and Asset management. This framework proposes to achieve the first two processes using a 5-stage methodology. The stages include shortlisting stocks using Data Envelopment Analysis (DEA), incorporation of the qualitative factors using text mining, stock clustering, stock ranking and optimizing the portfolio using heuristics. This framework would help the investors to select appropriate assets to make portfolio, invest in them to minimize the risk and maximize the return and monitor their performance.