PCA and MLE-Based Statistical Factor Models for Asset Pricing

Valivati, S and Dutta, S and Jain, S and Bs, P (2023) PCA and MLE-Based Statistical Factor Models for Asset Pricing. In: UNSPECIFIED.

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Abstract

Asset pricing revolves around determining the theoretical value of an asset (stock) by evaluating a wide range of influence factors. High-dimensional data poses extraordinary challenges to asset pricing research as it results in a low signal-to-noise ratio environment that affects the computation of risk premia. Over the years data-driven solutions to the problems of empirical asset pricing have been found by developing a close bond with machine learning techniques. In practical applications, the use of dimensionality reduction techniques such as Statistical Factor Models exploit the concept of low dimensional spaces accounting for most of the variation in high dimensional data. This work explores the use of Statistical Factor Models based on conventional Principal Components Analysis and a novel Maximum Likelihood Estimation - computed numerically with the Expectation-Maximization algorithm using a centroid initialization method for smoother convergence. On comparing the implementations for 3 and 5 factors across various sample sizes, it was noted that that the proposed Maximum Likelihood Estimation based model outperforms the conventional Principal Components based factor model with a lower average mean deviation in L2 norm value for factor loadings. © 2023 IEEE.

Item Type:	Conference Paper
Publication:	2023 International Conference on Network, Multimedia and Information Technology, NMITCON 2023
Publisher:	Institute of Electrical and Electronics Engineers Inc.
Additional Information:	The copyright for this article belongs to Institute of Electrical and Electronics Engineers Inc.
Keywords:	Cluster analysis; Clustering algorithms; Costs; Factor analysis; Image segmentation; Learning systems; Maximum likelihood estimation; Maximum principle; Signal to noise ratio, Asset pricing; EM algorithms; Factor model; High dimensional data; Ma ximum likelihoods; Maximum-likelihood; Maximum-likelihood estimation; Principal-component analysis; Statistical factor model; Theoretical values, Principal component analysis
Department/Centre:	Division of Interdisciplinary Sciences > Management Studies
Date Deposited:	04 Mar 2024 09:39
Last Modified:	04 Mar 2024 09:39
URI:	https://eprints.iisc.ac.in/id/eprint/84374

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