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In this article, we revisit a stock market anomaly widely known as the “Sell in May” (SIM) effect according to which returns tend to be higher in winter months than in summer months. Motivated by the increased attention this phenomenon has recently received in top-tier finance journals, we provide two contributions. First, we review the academic literature by systematically comparing studies in terms of their country coverage, methodology, results, explanations, trading implications and potential post-publication disappearance in order to derive a general picture on the existence and practical relevance of the SIM effect. Second, we extend the empirical work on the subject by analyzing whether the SIM effect exists in investment universes of highly liquid individual stocks and commodity futures which we would expect to be most efficiently priced. Our results indicate that this is indeed the case (with a higher effect strength in the stock market than in the commodity futures market). Furthermore, our findings support earlier studies showing that the effect is robust (across testing approaches and time) and appears to be concentrated in the industrial sector. However, we find that the SIM effect has become weaker (stronger) in the stock (commodity) market since it has become part of the public information set and that the effectiveness and persuasiveness of standard investment strategies based on the effect are limited.
Copulas
(2019)
Statistik und Ökonometrie für Wirtschaftswissenschaftler : eine anwendungsorientierte Einführung
(2020)
Have trend-following signals in commodity futures markets become less reliable in recent years?
(2021)
Nachhaltiges Investieren
(2022)
Intraklassenkorrelation
(2021)
Google-Trendindikator
(2021)
Saisonbereinigung
(2020)
Naive Prognosen
(2020)
Normalverteilungsmischungen
(2018)
Tests auf Zufälligkeit
(2018)
Styleanalyse
(2019)
Ausfallwahrscheinlichkeit
(2019)
Schätzung der Kerndichte
(2019)
A comparison of minimum variance and maximum Sharpe ratio portfolios for mainstream investors
(2022)
Recent studies offer striking evidence that, in stock markets, the predictive power of fundamental variables and seasonal effects tends to diminish over time. In this article, we analyse whether this also holds for the popular variable moving average (VMA) rules of Brock et al. (1992). While previous research on this issue has strongly concentrated on US and emerging stock market indices, we fill a research gap by focussing on a wide range of developed market indices and individual stocks. Using a trend regression approach for a dataset covering 1972 to 2015, we find that most analysed trading rule specifications show negative trend coefficients. These results, robust in a variety of settings, indicate that VMA rule signals have steadily lost their ability to forecast future price movements accurately. Analysing several rationales for this outcome, we find that negative trends in the autocorrelation of stock returns are a highly promising explanation for the poorer performance of VMA rules because they are designed to capture autocorrelation.
In this article, we revisit recent evidence indicating that the choice of performance measure appears to be irrelevant for the ranking of investment alternatives in the commodity market. Extending the previous literature in several important ways, we provide the following insights into the rankings produced by the 13 most popular performance measures for 24 commodities. First, ranking differences are somewhat larger in the spot market than in the futures market. Second, when we use daily instead of monthly data, performance measures that model reward based on average returns still produce similar performance rankings. However, when data of higher frequency is used for performance measures modeling reward based on higher partial moments, performance rankings differ crucially from those produced by measures focusing on average returns. Finally, the degree of ranking (dis)similarity appears to vary over time. Empirically, then, the choice of performance measure can matter. Nevertheless, our findings do not invalidate recent theoretical results on ranking similarity, because population rankings may not be identical with sample rankings, which are subject to estimation error.
In this article, we analyse whether industries worldwide significantly react to the changing environmental demands of their stakeholders. Specifically, we test the hypothesis of increasing environmentally responsible company behaviour in recent years. We do this by constructing environmental industry ratings based on a novel data-set covering three geographic regions and 42 industries from 2009 to 2015 and by fitting robust trend regression models to these ratings. Interestingly, we cannot observe a general upward trend in all industries and regions. In other words, the ‘green wave’ does not appear to carry all business sectors. This becomes particularly clear when looking at rankings derived from estimated trends. Industries in Europe show especially significant downward trends. Even though most European industries are currently rated higher than their counterparts in the Asia-Pacific region and North America, these trends indicate that Europe may lose this leading position in the future. Besides delivering such general tendencies, our estimated trends combined with the current rating levels provide important decision support. They show which industry sectors and regions are particularly (un)interesting for socially responsible investment products, ecologically conscious applicants or environmentally responsible consumers.
Factor portfolios derived from phenomena identified in the cross-section of stock returns have become vital parts of modern investment products and financial models. Even though much has been learned about the properties of these portfolios in recent years, one issue still remains unaddressed. Are factor returns long-range dependent (LRD)? We seek to answer this important research question because if factor returns were LRD, optimal portfolio decisions and traditional asset pricing methods/tests based on these factors would be severely biased and the validity of a large strand of prior research would be compromised. Specifically, using Hurst exponent approaches within rescaled range and detrended fluctuation frameworks, we analyse the presence of LRD in the returns of factor portfolios formed based on size, book-to-market, momentum and beta characteristics. For the periods from 1931 to 2014 (US market) and 1990 to 2014 (20 international markets) and supported by several robustness checks, we find no systematic evidence of persistence or anti-persistence in the factor returns. This implies that the factor use can be considered unproblematic in both asset management and asset pricing.