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 (Octob… Show more
“…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.…”
Section: The Theoretical Frameworkmentioning
confidence: 63%
“…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.…”
Section: Figure 1 Development Of the Number Of Investors In The Indon...mentioning
In 2022, the number of investors in the Indonesian stock market experienced a four-fold growth compared to 2019. This surge occurred during the epidemic period. This financial research aims to analyze the impact of the COVID-19 pandemic on the monthly average return and risk pattern of LQ45, as well as the presence of the Sell-in-May-and-Go-Away (SIMAGA) effect and the optimal investment strategy for LQ45. The study uses a descriptive-comparative methodology and employs mathematical and statistical frameworks. The sample consists of LQ45 companies from 1997 to 2022. Data analysis techniques include the Normality Test, Wilcoxon Rank Test, F Test, and investment strategy simulation. The results indicate that COVID-19 did not have a negative effect on the monthly returns and risk patterns of LQ45, except in 2002. Additionally, the SIMAGA phenomenon is not present in LQ45, but the Sell in August-Buy in November (SIABIN) strategy has been identified as the most effective. These findings provide valuable insights for investors in allocating their investments and determining the best strategy for buying stocks. It is important to consider monthly return variance as a key metric for measuring investment risk and its impact on overall returns.
“…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.…”
Section: The Theoretical Frameworkmentioning
confidence: 63%
“…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.…”
Section: Figure 1 Development Of the Number Of Investors In The Indon...mentioning
In 2022, the number of investors in the Indonesian stock market experienced a four-fold growth compared to 2019. This surge occurred during the epidemic period. This financial research aims to analyze the impact of the COVID-19 pandemic on the monthly average return and risk pattern of LQ45, as well as the presence of the Sell-in-May-and-Go-Away (SIMAGA) effect and the optimal investment strategy for LQ45. The study uses a descriptive-comparative methodology and employs mathematical and statistical frameworks. The sample consists of LQ45 companies from 1997 to 2022. Data analysis techniques include the Normality Test, Wilcoxon Rank Test, F Test, and investment strategy simulation. The results indicate that COVID-19 did not have a negative effect on the monthly returns and risk patterns of LQ45, except in 2002. Additionally, the SIMAGA phenomenon is not present in LQ45, but the Sell in August-Buy in November (SIABIN) strategy has been identified as the most effective. These findings provide valuable insights for investors in allocating their investments and determining the best strategy for buying stocks. It is important to consider monthly return variance as a key metric for measuring investment risk and its impact on overall returns.
“…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.…”
Section: Discussionmentioning
confidence: 99%
“…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.…”
mentioning
confidence: 99%
“…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.…”
Within the last three decades commodity markets, including soft commodities markets, have become more and more like financial markets. As a result, prices of commodities may exhibit similar patterns or anomalies as those observed in the behaviour of different financial assets. Their existence may cast doubts on the competitiveness and efficiency of commodity markets. It motivates us to conduct the research presented in this paper, aimed at examining the Halloween effect in the markets of basic soft commodities (cocoa, coffee, cotton, frozen concentrated orange juice, rubber and sugar) from 1999 to 2020. This long-time span ensures the credibility of results. Apart from performing the two-sample t-test and the rank-sum Wilcoxon test, we additionally investigate the autoregressive conditional heteroskedasticity (ARCH) effect. Its presence in our data allows us to estimate generalised autoregressive conditional heteroskedasticity [GARCH (1, 1)] models with dummies representing the Halloween effect. We also investigate the impact of the January effect on the Halloween effect. Results reveal the significant Halloween effect for cotton (driven by the January effect) and the significant reverse Halloween effect for sugar. It brings implications useful to the main actors in the market. They may apply trading strategies generating satisfactory profits or providing hedging against unfavourable changes in soft commodities prices.
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