Diversification and Risk Assessment using Data Science: Downside Risk vs. Mean Variance Optimization

• Author(s): Yashasvi Bora
• Paper ID: 1704721
• Page: 769-777
• Published Date: 23-06-2023
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 6 Issue 12 June-2023

Abstract

Diversification and risk assessment are essential aspects of portfolio management. In this research paper, we explore the application of data science techniques to compare two popular portfolio optimization methods: Downside Risk and Mean Variance Optimization. The study begins by collecting historical financial data for a set of stocks. Using this data, we calculate the portfolio weights for both optimization methods. The Mean Variance Optimization technique aims to maximize returns while minimizing volatility, while the Downside Risk approach focuses on minimizing the potential for losses. To measure the diversification benefits of each strategy, we analyze the sector allocation of the optimized portfolios and compare the sortino ratios. By computing sector weights, we acquire insights into the concentration or dispersion of investments across different industries. This research allows us to evaluate the diversification obtained by each optimization method and compare their efficacy in distributing risk across sectors. The results generated from the data analysis and visualization illustrate the contrasting characteristics of the Downside Risk and Mean Variance Optimization approaches. The sector-wise analysis highlights variations in sector allocations, illustrating the disparities in diversification techniques adopted by each strategy. Additionally, the risk assessment analysis provides insights into the potential downside risks connected with each portfolio. This research contributes to the field of portfolio management by providing a comprehensive comparison between Downside Risk and Mean Variance Optimization methods. It demonstrates the potential of data science techniques in evaluating portfolio diversification and risk assessment. The findings can assist investors and financial professionals in making informed decisions regarding portfolio construction and risk management strategies.

Keywords

Diversification, Risk Assessment, Portfolio Optimization, Data Science, Downside Risk, Mean Variance Optimization

Citations

IRE Journals:
Yashasvi Bora "Diversification and Risk Assessment using Data Science: Downside Risk vs. Mean Variance Optimization" Iconic Research And Engineering Journals Volume 6 Issue 12 2023 Page 769-777

IEEE:
Yashasvi Bora "Diversification and Risk Assessment using Data Science: Downside Risk vs. Mean Variance Optimization" Iconic Research And Engineering Journals, 6(12)