Analysis of Sell-in-May-and-Go-Away Strategy on the Markets of 122 Equity Indices and 39 Commodities

Abstract

The problem of efficiency of financial markets has always been one of the most important, including, among others, calendar effects. The sell-in-May-and-go-away (also called Halloween) effect is worth considering from the point of view of assessing the portfolio management effectiveness and behavioral finance. This paper tests the sell-in-May-and-go-away strategy and its modifications on the market of 122 equity indices and 39 commodities in the eight approaches, depending on the investment time horizon (October-15 th May, November-15 th May, October-1 st May, November-1 st May) and types of computed rates of return (accrued rates of return and average daily geometric rates of return). Calculations presented in this paper indicate the presence of the sell-in-May-and-go-away effect on the analyzed markets in the classic time frame, as well as in the different time frames.

Comparative Analysis of Sell-In-May-And-Go-Away and Monthly Effect Before and During COVID-19 Pandemic at LQ45 IDX

“…The May Effect was most pronounced in emerging markets, where returns were 11.07 percent higher from November to April than from May to October. As stated by Borowski (2015), the research results indicate that there is a Sell-In-May-And-Go-Away effect on the market when analysed in both traditional and non-traditional time frames. According to the research of Hapsari & Sumarsono (2020), the Sell-In-May-And-Go-Away phenomenon has a significant impact on negative returns in the agricultural stock market and other sectors.…”

“…However, annual returns for November-April are nearly six times greater than returns for May-October. Moreover, Borowski (2015) notes that calendar effects, in particular Sell-in-May-And-Go-Away, are a major concern in financial market efficiency. According to the investment horizon and calculated rate of return, the study looked at the Sell-in-May-and-Go-Away strategy and its modifications on 122 equity indexes and 39 commodities using eight distinct methodologies.…”

Are soft commodities markets affected by the Halloween effect?

“…This long-time span ensures the credibility of results. Second, in contrast to previous studies (Borowski 2015c, Arendas 2017, we perform not only the two-sample t-test and the rank-sum Wilcoxon test. The investigation of the ARCH effect and confirming its presence in our data allows us to estimate GARCH (1, 1) models with a dummy variable representing the Halloween effect.…”

“…The Halloween effect in commodities markets was also investigated, but not to the same extent (Borowski 2015c;Arendas 2017;. Borowski (2015c) tested the Halloween effect for 39 commodities: base metals, energy products, agricultural items (including soft commodities) and precious metals in several periods of different lengths ranging from 9 years for barley (December 2006-May 2015 to 65 years for copper (January 1950-May 2015.…”

“…The Halloween effect in commodities markets was also investigated, but not to the same extent (Borowski 2015c;Arendas 2017;. Borowski (2015c) tested the Halloween effect for 39 commodities: base metals, energy products, agricultural items (including soft commodities) and precious metals in several periods of different lengths ranging from 9 years for barley (December 2006-May 2015 to 65 years for copper (January 1950-May 2015. He performed parametric tests for equality of two means and for equality of two variances and confirmed the presence of the Halloween effect for gasoline, gold, heating oil, lean hogs, nickel, rubber, tin, and wheat.…”