Is smart beta investing profitable? evidence from the Nordic stock market

ABSTRACT

This study examines the profitability of the mixing and integrating approach for constructing multi-factor smart beta portfolios. While most studies explore this issue in a U.S. market setting, this is the first study that exclusively focus on the Nordic equity market, which exhibits some unique and stylized features as recently highlighted in the literature. Our findings indicate first strong evidence for return variations for sorting stocks on value-, momentum-, and ex-ante beta-signals. Surprisingly, variations in payoffs are not only small stock phenomena in the Nordic equity markets. While the current literature does not yet agree on a consensus, our study supports the literature documenting the superiority of the integrating approach. Our results challenge the efficient market hypothesis in a market environment offering a high-level of information-flow-efficiency.

KEYWORDS:

• Smart beta
• multi-factor investing
• value
• momentum
• low beta

JEL CLASSIFICATION:

• G12
• G14

Acknowledgments

We would like to thank an anonymous reviewer for providing useful and insightful comments.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

¹ See https://www.ftserussell.com/press/global-smart-beta-adoption-reaches-record-high-58-percen.

² Recent related studies investigating smart-beta factor investing are De Franco and Monnier (2019), Shimiziu and Shiohama (2020), Jiang et al. (2020), and Lester (2019). De Franco and Monnier (2019) explore a multifactor portfolio from a performance-agnostic point of view and argue that investing in a long–short static multifactor strategy implies that one invests into a new (synthetic) factor. They conclude that the equal-weighting of these factors (e.g., value, size, momentum and low volatility) results in a synthetic factor that has surprisingly no predictive power on stocks’ return. Shimiziu and Shiohama (2020) explore risk-based asset allocation approaches for factor investing strategies and propose an inverse factor volatility strategy. Their findings indicate that factor portfolios using their proposed inverse factor volatility strategy significantly outperformed market capitalization weighted portfolios by successfully acquiring factor risk premiums. Jiang et al. (2020) and Lester (2019) confirm Shimiziu’s and Shiohama’s (2020) results in finding that factor tracking strategies outperform naïve diversification.

³ As pointed out in Grobys and Huhta-Halkola (2019), Nordic countries have held constantly a triple-A credit rating (with the exception of Finland which is currently rated at the same level as the US.)

⁴ A similar period is considered in Grobys and Huhta-Halkola (2019) as it corresponds to a time frame, where the Nordic stock markets have been fairly liquid and open to the international investors.

⁵ Note that the average market capitalization of Finnish companies excluding Nokia would is EUR 806 million and average size of Danish companies excluding Novo Nordisk is EUR 958 million. The large effect of excluding a single observation from the sample depicts the nature of the Nordic stock markets well, as one or two stocks can have a disproportionally large effect on the population parameters.

⁶ It is important to bear in mind that our analyses presented in the following sections exclude 10% of the smallest stocks.

⁷ Future studies are encouraged to explore risk-adjusted payoffs using other pricing models such as the Fama and French (2015) five-factor model. Due to the increasing number of asset pricing models proposed in the literature, this exercise is left for future research.

⁸ This figure is in line with Grobys (2016) who explored the profitability of momentum in the European Union.

⁹ This finding is in line with Frazzini and Pedersen (2014).

¹⁰ Our main conclusions remain unchanged when making inference based on the CAPM-alphas.

¹¹ Note that we have at this stage already excluded ex-ante the smallest 10% of companies from the sample as detailed in section 2.