The study involves the selection of stocks from the NIFTY AUTO index based on certain criteria and the construction of an optimal portfolio using Modern Portfolio Theory.The project also includes the implementation of various Python libraries for data analysis, visualization, and portfolio optimization.Modern Portfolio Theory (MPT), which seeks to maximize expected return for a given level of risk or reduce risk for a given level of expected return, is one of the strategies used for portfolio optimization.The study investigates techniques for creating diversified portfolios that strike a balance between risk and return, including mean-variance optimization, Sharpe ratio optimization, and efficient frontier analysis.The library tools such as NumPy, Pandas, matplotlib are used for data analysis, optimization, and visualization, making it well-suited for this task.The study utilizes historical data on stock prices, returns, and other relevant financial metrics to analyze the performance of individual stocks within the NIFTY AUTO index.The study's conclusion is advance knowledge of portfolio management techniques used in the automobile industry and show the value of quantitative techniques based on Python for financial analysis and decision-making.The study's optimal portfolios offer insightful guidance to investors who aim to accomplish their investment objectives while navigating the intricacies of the NIFTY AUTO index.
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Gupta et al. (2024) studied this question.
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