You can view the full text of this article for free using the link below.

Title: Analysing the relationship between idiosyncratic risk and strategic capabilities using penalty-based selection and shrinkage methods

Authors: Wenbin Sun; Sudhakar Raju

Addresses: Helzberg School of Management, Rockhurst University, 1100 Rockhurst Road, Kansas City, MO 64110, USA ' Helzberg School of Management, Rockhurst University, 1100 Rockhurst Road, Kansas City, MO 64110, USA

Abstract: Recent research has documented the dramatic increase in idiosyncratic risk and the under-diversification of portfolios. We provide a unique marketing perspective to the financial risk management literature by suggesting that idiosyncratic volatility can be reduced by enhancing marketing, operational and R&D capabilities. We investigate the relationship between idiosyncratic risk, firm capabilities and financial control variables using the least absolute selection and shrinkage operator (LASSO) - a penalty-based variable selection and shrinkage technique developed in the context of 'machine learning' and 'big data' that has not been much used in the empirical marketing literature. Our results differ from those reported in the literature. Using the more stringent criterion imposed by the LASSO, we find that whereas R&D, marketing and operational capabilities have no statistically significant individual effects, the interactive effects between marketing capability and R&D intensity have a significant effect on reducing idiosyncratic risk.

Keywords: idiosyncratic risk; marketing capability; R&D intensity; operational capability; least absolute selection and shrinkage operator; LASSO; machine learning; big data.

DOI: 10.1504/IJBDA.2019.098833

International Journal of Business and Data Analytics, 2019 Vol.1 No.1, pp.69 - 88

Available online: 27 Mar 2019 *

Full-text access for editors Access for subscribers Free access Comment on this article