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Journal of Economics and Finance

Erschienen in:

01.03.2018

Does the strength of capital market anomalies exhibit seasonal patterns?

verfasst von: Benjamin R. Auer

Erschienen in: Journal of Economics and Finance | Ausgabe 1/2019

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Abstract

In a recent study, Fiore and Saha (Financ Rev 50(2), 257–273 2015) present the interesting finding that the beta anomaly in US stocks appears only in summer months. Using a novel dataset of arbitrage portfolio returns exploiting size, value, momentum and beta effects in 21 developed stock markets, we analyse whether summer-winter seasonality also occurs for other well-known anomalies and for markets other than the US. In a variety of dummy regression settings, we find that, on a descriptive basis, the returns for the size and value (momentum and beta) anomalies tend to be higher in winter (summer) than in summer (winter). However, in the majority of cases, these results do not withstand statistical testing. Furthermore and in contrast to Fiore and Saha (Financ Rev 50(2), 257–273 2015), our results indicate that the beta anomaly is valuable for investors in both summer and winter and thus disinvesting in winter should not be the preferred investment strategy. With the exception of the size portfolios, where returns appear to be concentrated in January, the economic significance of the other arbitrage portfolios’ summer and winter returns mostly also advises against seasonal investing in arbitrage portfolios.

Vorheriger Artikel The impact of inflation rate on stock market returns: evidence from Kenya

Nächster Artikel Structural factors, global shocks and sovereign debt credit ratings

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This anomaly also occurs for other risk measures such as idiosyncratic volatility (see Ang et al. 2006, 2009) or total volatility (see Dutt and Humphery-Jenner 2013).

Summer-winter seasonality in its traditional form (i.e., higher returns in winter than in summer; called the ‘Sell in May’ or ‘Halloween’ effect) has been documented extensively (see Bouman and Jacobsen 2002; Jacobsen and Visaltanachoti 2009; Zhang and Jacobsen 2013) and is considered to be an anomaly in its own right with explanations such as seasonal affective disorder (see Kamstra et al. 2003) or temperature-related mood (see Cao and Wei 2005). For a highly interesting explanation of various kinds of seasonality based on mood betas, see Hirshleifer et al. (2016).

This might be a consequence of Corhay et al. (1987)’s finding that the Fama-MacBeth estimate of the CAPM-based US stock risk premium is positive and significant only in January.

It extends the existing set of explanations for an empirically inversed risk-return relationship covering benchmarking (see Baker et al. 2011), restricted borrowing (see Frazzini and Pedersen 2014) and a preference of investors for lottery-like stocks (see Mitton and Vorkink 2007; Barberis and Huang 2008; Kumar 2009).

The implementation of such regressions is an important difference to Fiore and Saha (2015) relying on a mere descriptive comparison of returns.

Note that investigating the seasonality of the underlying individual stocks is not an alternative (but can be an addition) to our approach because we show that, even though different arbitrage portfolios for a given market are based on the same stock universe, their seasonality can be quite distinct.

Furthermore, seasonal evidence could be used for sophisticated trading strategies in the cross-section of arbitrage portfolios (see Heston and Sadka 2008).

This dataset (used in, for example, Auer 2018) and a detailed description of the portfolio construction are available under www.aqr.com/library/data-sets/betting-against-beta-equity-factors-monthly.

The dataset also includes returns for Greece, Israel and Portugal. However, because of insufficient return data, we had to exclude these countries from our analysis.

For a comprehensive discussion of dummy regressions and their interpretation, see Hardy (1993). Also note that, in our context, there is no need for a look at the parameter η_i in model (5) because κ_i = −η_i.

In the following, we count only coefficients significant at the 1% or 5% level because the 10% level is not generally considered significant.

For brevity and because the results allow conclusions virtually identical to Tables 1 and 2, we limit ourselves to verbally summarising their design and outcome. Detailed results are available upon request.

An extension of our analysis to the model selection of Bali et al. (2008), namely, AGARCH, EGARCH, GARCH, GJR-GARCH, IGARCH, NGARCH, QGARCH, TGARCH and TS-GARCH, yields similar results.

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Titel: Does the strength of capital market anomalies exhibit seasonal patterns?
verfasst von: Benjamin R. Auer
Publikationsdatum: 01.03.2018
Verlag: Springer US
Erschienen in: Journal of Economics and Finance / Ausgabe 1/2019
Print ISSN: 1055-0925
Elektronische ISSN: 1938-9744
DOI: https://doi.org/10.1007/s12197-018-9432-3

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