A new study identified large clusters of food deserts, where residents have limited access to affordable, nutritious food, in East London—particularly Newham, Redbridge, and Barking and Dagenham—and in parts of west London such as Ealing and Brent. The findings were published November 6^th in the open-access journal PLOS Complex Systems by Tayla Broadbridge of the University of Nottingham, UK, and colleagues.

Collaborating with competitors may sound counterintuitive, but it can also be the key to international success. The coopetition arrangement is not uncommon in the automotive sector, where rival brands share production lines or jointly develop vehicle models to reduce costs and speed up innovation. In his doctoral dissertation at the University of Vaasa, Finland, Shuwei Jiang reveals that the same logic applies to European SMEs as well – particularly those aiming to enter the Chinese market. Jiang’s dissertation not only discusses the benefits of coopetition but also identifies its risks and the mechanisms that help firms address them.

Abstract

Purpose – We aim to examine two issues. First, we intend to identify the best performing expected return proxies. Second, we investigate whether the expected return proxies for individual stocks can track the corresponding realized returns during extremely good or extremely bad times of the economic environment related to business conditions, stock market valuation and broad market performance.

Design/methodology/approach – We construct four sets of expected return proxies, including: (1) characteristic-based proxies; (2) standard risk-factor-based proxies; (3) risk-factor-based proxies that allow betas to vary with firm characteristics and (4) macroeconomic-variable-based proxies. First, we estimate expected returns for individual stocks using newly developed methods and evaluate the performance of these expected return proxies based on the minimum variance criterion of Lee et al. (2020). Second, we regress expected return proxies and realized returns on indicator variables that capture the extreme phases of the economic environment. Then we compare the estimated coefficients from these two sets of regressions and see if they are similar in magnitude via formal hypothesis testing.

Findings – We find that characteristic-based proxies and risk-factor-based proxies that allow betas to vary with firm characteristics are the two best performing proxies. Therefore, it is important to allow betas to vary with firm characteristics in constructing expected return proxies. We also find that model-based expected return proxies do a reasonably good job capturing actual returns during extremely bad and extremely good phases of business cycles measured by leading economic indicators, consumer confidence and business confidence. However, there is a large gap between the adjustment of model-based expected returns and realized returns during extreme episodes of stock market valuation or broad market performance.

Originality/value – We examine four types of expected return proxies and use the newly developed methodology in Lee et al. (2020) to see which one is the best. In addition, we document whether model-based expected returns from individual stocks adjust partially or fully to keep pace with actual returns in response to changing economic conditions. No prior studies have examined these two issues.