PMLGSep 24, 2023

Performance Evaluation of Equal-Weight Portfolio and Optimum Risk Portfolio on Indian Stocks

arXiv:2309.13696v1h-index: 31
Originality Synthesis-oriented
AI Analysis

It addresses portfolio optimization for investors in the Indian stock market, but is incremental as it applies existing methods to new data.

This work compared three portfolio design approaches—minimizing risk, optimizing risk, and equal weighting—on Indian stocks from 13 sectors, identifying the best-performing method for each sector based on returns from 2022 data.

Designing an optimum portfolio for allocating suitable weights to its constituent assets so that the return and risk associated with the portfolio are optimized is a computationally hard problem. The seminal work of Markowitz that attempted to solve the problem by estimating the future returns of the stocks is found to perform sub-optimally on real-world stock market data. This is because the estimation task becomes extremely challenging due to the stochastic and volatile nature of stock prices. This work illustrates three approaches to portfolio design minimizing the risk, optimizing the risk, and assigning equal weights to the stocks of a portfolio. Thirteen critical sectors listed on the National Stock Exchange (NSE) of India are first chosen. Three portfolios are designed following the above approaches choosing the top ten stocks from each sector based on their free-float market capitalization. The portfolios are designed using the historical prices of the stocks from Jan 1, 2017, to Dec 31, 2022. The portfolios are evaluated on the stock price data from Jan 1, 2022, to Dec 31, 2022. The performances of the portfolios are compared, and the portfolio yielding the higher return for each sector is identified.

Foundations

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