JOURNAL ARTICLE
Keynes and the Provincial investment problem.
Published In: Cambridge Journal of Economics, 2025, v. 49, n. 2. P. 189 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Woods, J E 3 of 3
Abstract
This article examines John Maynard Keynes' long-term involvement (1924–1946) in managing the investment portfolio of the Provincial Insurance Company, a family-owned non-life insurer controlled by the Scott family. It highlights fundamental and increasing differences between Keynes and Francis C. Scott, the company's managing director, regarding investment philosophy and portfolio management, particularly from the late 1930s onward. Keynes shifted from a short-term, tactical asset allocation approach ("credit cycling") in the 1920s to a long-term, intrinsic value-based strategy emphasizing concentrated holdings with a margin of safety, contrasting with Scott's preference for diversification, fixed asset weightings, and low-volatility investments. These divergent views culminated in the 1942 creation of a "Stable Fund" (40% of assets) reflecting Scott's risk-averse approach, which Keynes and his interim successor Ian Macpherson found problematic. The article uses three case studies to illustrate these conflicts and situates the "Provincial investment problem" as a clash between Keynes' contrarian, knowledge-based risk concept and Scott's conventional, diversification-based risk management, underscoring challenges in reconciling innovative investment theory with traditional insurance practice.
Additional Information
- Source:Cambridge Journal of Economics. 2025/03, Vol. 49, Issue 2, p189
- Document Type:Article
- Subject Area:History
- Publication Date:2025
- ISSN:0309-166X
- DOI:10.1093/cje/beaf002
- Accession Number:184408203
- Copyright Statement:Copyright of Cambridge Journal of Economics is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Looking to go deeper into this topic? Look for more articles on EBSCOhost.