Junk Stocks vs Quality Stocks, A Perspective from Mood Seasonality

ABSTRACT

Based on their own quality measure, Asness et al. (2019) document that quality stocks significantly outperform junk stocks. In this article, we attempt to explore the quality-based return patterns from behavioural perspective. The empirical findings demonstrate that the relative performance of quality stocks exhibits significant mood seasonality, i.e. the return spreads of quality and junk stocks decrease (increase) during high (low)-mood months. Furthermore, we also find that the return spreads exhibit significant mood recurrence and reversal effects proposed by Hirshleifer et al. (2020). All of these findings support the argument that mood would be an important source for the outperformance of quality stocks.

KEYWORDS:

• Quality-based return patterns
• return seasonality
• mood effect
• behavioural bias

Highlights

• Our empirical findings support the argument that investors mood is another important source of quality-based return patterns.

• We document that the relative performance of quality stocks significantly decreases (increases) during high (low)-mood months.

• We find that the spreads between quality and junk stocks exhibit significant recurrence (reversal) effects during congruent (noncongruent)-mood months.

• We show that investors mood significantly influences junk stocks while such effect is marginal for quality stocks.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

¹ March is used as a proxy for high-mood period, since Kamstra et al. (2017) document that investors experience highest recovery from the SAD effect derived from the increasing length of daytime.

² The detailed discussion can be found in section 2 (Background and hypotheses) of Hirshleifer, Jiang, and DiGiovanni (2020).

³ https://www.aqr.com/Insights/Datasets/Quality-Minus-Junk-10-QualitySorted-Portfolios-Monthly

⁴ Hirshleifer, Jiang, and DiGiovanni (2020) point out that mood can be viewed as a special case of investor sentiment.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [72001169].