Analysing the relationship between idiosyncratic risk and strategic capabilities using penalty-based selection and shrinkage methods. (27th March 2019)

• Record Type: Journal Article
• Authors: Sun, Wenbin; Raju, Sudhakar
• Is Part Of: International journal of business and data analytics. Volume 1:Number 1(2019)
• Page Start: 69
• Page End: 88
• Publication Date: 2019-03-27
• Subjects: idiosyncratic risk -- marketing capability -- R&D intensity -- operational capability -- least absolute selection and shrinkage operator -- LASSO -- machine learning -- big data; Commercial statistics -- Data processing -- Periodicals; Industrial management -- Mathematical models -- Periodicals; Business -- Mathematical models -- Periodicals; Management -- Statistical methods -- Periodicals; Business -- Research -- Periodicals; 658.403
• Languages: English
• ISSNs: 2515-9100

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.