Are soft commodities markets affected by the Halloween effect?

https://doi.org/10.17221/216/2021-AGRICECONCitation:

Krawiec M., Górska A. (2021): Are soft commodities markets affected by the Halloween effect? Agric. Econ. – Czech, 67: 491–499.

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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.

References:
Arendas P. (2017): The Halloween effect on the agricultural commodities markets. Agricultural Economics – Czech, 63: 441–448. https://doi.org/10.17221/45/2016-AGRICECON
 
Arendas P., Malacka V., Schwarzova M. (2018): A closer look at the Halloween effect: The case of the Dow Jones industrial average. International Journal of Financial Studies, 6: 1–12. https://doi.org/10.3390/ijfs6020042
 
Bloomberg (2020): KC1 Comdty, QC1 Comdty, SB1 Comdty, CT1 Comdty, JO1 Comdty, OR1 Comdty. [Dataset]. Bloomberg. Available at https://www.bloomberg.com/markets/commodities (accessed Sept 24, 2020).
 
Borowski K. (2015a): Analysis of selected seasonality effects in markets of frozen concentrated orange juice future contracts. Journal of Capital Market and Behavioral Finance, 1: 7–30. https://doi.org/10.5539/ijef.v7n9p15
 
Borowski K. (2015b): Analysis of selected seasonality effects in markets of rubber future contracts quoted on Tokyo commodity exchange. International Journal on Economics and Finance, 7: 15–30.
 
Borowski K. (2015c): Analysis of sell-in-May-and-go-away strategy on the markets of 122 equity indices and 39 commodities. International Journal of Economics and Finance, 7: 119–129. https://doi.org/10.5539/ijef.v7n12p119
 
Bouman S., Jacobsen B. (2002): The Halloween indicator: Sell in May and go away. The American Economic Review, 92: 1618–1635. https://doi.org/10.1257/000282802762024683
 
Boya C.M. (2019): From efficient markets to adaptive markets: Evidence from the French stock exchange. Research in International Business and Finance, 49: 156–165. https://doi.org/10.1016/j.ribaf.2019.03.005
 
Burakov D., Freidin M. (2018): Is the Halloween effect present on the market of agricultural commodities? Agris On-line Papers in Economics and Informatics, 10: 23–32.
 
Burakov D., Freidin M., Solovyev Y. (2018): The Halloween effect on energy markets: An empirical study. International Journal of Energy Economics and Policy, 8: 121–126.
 
Caporale G.M., Zakirova V. (2017): Calendar anomalies in the Russian stock market. Russian Journal of Economics, 3: 101–108. https://doi.org/10.1016/j.ruje.2017.02.007
 
Carrazedo T., Dias Curto J., Oliveira L. (2016): The Halloween effect in European sectors. Research in International Business and Finance, 37: 489–500. https://doi.org/10.1016/j.ribaf.2016.01.003
 
Eller R., Sagerer Ch. (2008): An overview of commodity sectors. In: Fabozzi F.J., Füss R., Kaiser D.G. (eds.): The Handbook of Commodity Investing. Hoboken, New Jersey, USA, John Wiley & Sons: 681–711.
 
Engle R.F. (1982): Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50: 987–1007. https://doi.org/10.2307/1912773
 
Fama E.F. (1965): Random walks in stock market prices. Financial Analyst Journal, 21: 55–59. https://doi.org/10.2469/faj.v21.n5.55
 
Füss R., Hoppe Ch., Kaiser D.G. (2008): Review of commodity futures benchmarks. In: Fabozzi F.J., Füss R., Kaiser D.G. (eds.): The Handbook of Commodity Investing. Hoboken, New Jersey, USA, John Wiley & Sons: 169–202.
 
Gordon J. (1985): The Distribution of Daily Changes in Commodity Futures Prices. Technical Bulletin, 1702. Economic Research Service, United States Department of Agriculture (USDA). Available at https://naldc.nal.usda.gov/download/CAT87201608/PDF (accessed May 7, 2021).
 
Gujarati D.N. (2003): Basic Econometrics. New York, USA, McGraw-Hill: 856–862.
 
Guo B., Luo X., Zhang Z. (2014): Sell in May and go away: Evidence from China. Finance Research Letters, 11: 362–368. https://doi.org/10.1016/j.frl.2014.10.001
 
Haggard S.K., Witte D.H. (2010): The Halloween effect: Trick or treat? International Review of Financial Analysis, 19: 379–387. https://doi.org/10.1016/j.irfa.2010.10.001
 
Kenourgios D., Samios Y. (2021): Halloween effect and active fund management. The Quarterly Reviev of Economics and Finance, 80: 534–544. https://doi.org/10.1016/j.qref.2021.04.006
 
Krawiec M., Górska A. (2019): Calendar effects in soft commodity markets: A further investigation of weak-form efficiency. In: Vision 2025: Education Excellence and Management of Innovations through Sustainable Economic Competitive Advantage: Proceedings of the 34th International Business Information Management Association Conference (IBIMA), Madrid, Spain, Nov 13–14, 2019: 489–499.
 
Lokare S. (2007): Commodity derivatives and price risk management: An empirical anecdote from India. Reserve Bank of India Occasional Papers, 28: 27–77.
 
Maddala G.S. (2005): Introduction to Econometrics. Chichester, United Kingdom, John Wiley & Sons: 260–261.
 
Ramanathan R. (2002): Introductory Econometrics with Applications. Mason, Ohio, USA, South-Western Thomson Learning: 401–406.
 
Rosini L., Shenai V. (2020): Stock returns and calendar anomalies on the London Stock Exchange in the dynamic perspective of the adaptive market hypothesis: A study of FTSE100 & FTSE250 indices over a ten year period. Quantitative Finance and Economics, 4: 121–147.
 
Rossi M. (2018): Efficient market hypothesis and stock market anomalies: Empirical evidence in four European countries. The Journal of Applied Business Research, 34: 183–190. https://doi.org/10.19030/jabr.v34i1.10111
 
Sabuhoro J., Larue B. (1997): The market efficiency hypothesis: The case of coffee and cocoa futures. Agricultural Economics, 16: 171–184. https://doi.org/10.1111/j.1574-0862.1997.tb00452.x
 
Statista (2021): Industry Overview. [Database]. Statista.com. Available at https://www.statista.com/statistics/275397/caoutchouc-production-in-leading-countries/ (accessed Sept 20, 2021).
 
Wackerly D.D., Mandenhall W., Scheaffer R.L. (2008): Mathematical Statistics with Application. 7th Ed. Duxbury, Massachusetts, USA, Thomson Brooks/Cole: 520–525.
 
Wild C.J., Seber G.A.F. (2000): Chance Encounters: A First Course in Data Analysis and Inference. New York, USA, John Wiley: 1–10.
 
Zahng J., Lai Y., Lin J. (2017): The day-of-the-week effects on stock markets in different countries. Finance Research Letters, 20: 47–62. https://doi.org/10.1016/j.frl.2016.09.006
 
Zhang C.Y., Jacobsen B. (2013): Are monthly seasonals real? A three century perspective. Review of Finance, 17: 1743–1785. https://doi.org/10.1093/rof/rfs035
 
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