Abstract

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.

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