Portfolio construction and weight optimisation using principal component analysis. (21st June 2022) - Laddha, Kartikay; Kapoor, Vidhi; Panda, Siba
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.