NDLI: Financial Portfolio Optimization

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Financial Portfolio Optimization

Content Provider	Scilit
Author	Gupta, Mansi Semwal, Sonali Bali, Shivani
Copyright Year	2021
Description	In financial data science and more specifically in investment analytics, portfolio optimization is a very important aspect. A portfolio consists of multiple securities, each having its own weight. Based upon these weights, the overall portfolio return and risk are determined. Investors try to find the optimal portfolio, which helps in maximizing return for a given risk by adjusting these weights given to securities. In this chapter, a diverse and exemplar portfolio is considered. It has stocks from four companies belonging to four different sectors (from Indian market NSE Index): Hindustan Unilever, Reliance Industries, Tata Consultancy Services (TCS), and Sun Pharma. The optimized portfolio is constructed with adjusted weightage for each company in the portfolio using Markowitz portfolio optimization (MPO) on Python. The obtained optimized portfolio yielded a logarithmic portfolio return of 0.04268 at minimum risk (standard deviation) of 0.14951 and maximum possible logarithmic return of 0.15873 at a risk (standard deviation) of 0.17938. Using Markowitz portfolio optimization in a strategic manner for such portfolio where there is diversity along with different shades of similarity can fetch more optimized portfolio than obtained by the classical approach of Markowitz portfolio optimization. Book Name: Industry 4.0, AI, and Data Science
Related Links	https://api.taylorfrancis.com/content/chapters/edit/download?identifierName=doi&identifierValue=10.1201/9781003097181-14&type=chapterpdf
Ending Page	240
Page Count	10
Starting Page	231
DOI	10.1201/9781003097181-14
Language	English
Publisher	Informa UK Limited
Publisher Date	2021-06-02
Access Restriction	Open
Subject Keyword	Book Name: Industry 4.0, Ai, and Data Science Information Systems Diversity Portfolio Optimization Markowitz Portfolio Using Markowitz Standard Deviation Portfolio Return
Content Type	Text
Resource Type	Chapter

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