Title: Portfolio construction and weight optimisation using principal component analysis

Authors: Kartikay Laddha; Vidhi Kapoor; Siba Panda

Addresses: Department of Data Science and Artificial Intelligence, Mukesh Patel School of Technology Management and Engineering, NMIMS University, Mumbai, India ' Department of Data Science and Artificial Intelligence, Mukesh Patel School of Technology Management and Engineering, NMIMS University, Mumbai, India ' Department of Data Science and Artificial Intelligence, Mukesh Patel School of Technology Management and Engineering, NMIMS University, Mumbai, India

Abstract: Principal component analysis helps in constructing statistical risk factors by explaining the variance inherited from the dataset. It transforms the dataset into new independent components which are uncorrelated and are hence used to optimise portfolios for earning higher returns and more financial control. It helps uncover the underlying drivers hidden in the data by summarising huge feature sets using a few components. In this paper, the method of PCA is used to derive the principal components that capture most of the market volatility and are used to define the cash allocation strategy which helps outperforming the sectoral benchmark index (NIFTY IT) in terms of returns and Sharpe ratio. Our optimised portfolio provides a risk to reward ratio of 1.0605 in comparison to 0.4583 provided by the NIFTY IT index as the weights allocation in our portfolio is explained by the captured variability, which was obtained using the PCA technique. This methodology helps in creating portfolios with reduced dimensionality and variability of the dataset used for practical applications.

Keywords: nifty; portfolio construction; principle component analysis; Sharpe ratio; weight optimisation.

DOI: 10.1504/IJFSE.2022.123957

International Journal of Forensic Software Engineering, 2022 Vol.1 No.4, pp.314 - 334

Received: 23 Apr 2021
Accepted: 29 Aug 2021
Published online: 05 Jul 2022 *

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