Composition of portfolios by pairs trading with volatility criteria in the Brazilian market<sup>,</sup>

Cavalcanti, Raphael Silveira Guerra; Santos, Joséte Florencio dos; Santos, Ramon Rodrigues dos; Cunha, Anderson Góis M. da

doi:10.1590/1808-057x202110890

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Revista Contabilidade & Finanças

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Original Article • Rev. contab. finanç. 32 (86) • May-Aug 2021 • https://doi.org/10.1590/1808-057x202110890 copy

Composition of portfolios by pairs trading with volatility criteria in the Brazilian market^* * Paper presented at the XXII Seminários em Administração (SemeAd), São Paulo, SP, Brazil, November 2019. ^,^ The authors would like to thank the National Council for Scientific and Technological Development (CNPq), the Coordination for the Improvement of Higher Education Personnel (CAPES), and the State of Pernambuco Research Foundation (FACEPE) for the financial support in carrying out this research.

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

The objective of this study was to understand how the shares’ volatility affects the portfolios’ dynamics formed using the model of pairs trading in the Brazilian stock market. This article distinguished itself by bringing new evidence about the effects of volatility in the pairs trading model not covered by previous studies, expanding the sample size analyzed in the Brazilian stock market. The chosen theme’s relevance is that investors can use pairs trading or long-short models to build their portfolios. The use of cointegration concepts probabilistically contributes to portfolios’ formation weakly correlated to the market indexes with superior performance. This article impacts the area by contributing new evidence for better use of the model in the analysis of investments. From January 2016 to December 2018, the 90 most liquid assets of Bolsa, Brasil, Balcão (B3) were analyzed, totaling 5,927,400 possible pairs. The Augmented Dickey-Fuller test and subsequent backtesting of the pairs in the proposed period were used to evaluate the cointegration criteria. Statistical analysis was performed by parametric and non-parametric tests and Pearson and Spearman correlation analyses. The results found indicated that the formation of portfolios by pairs trading with dependent assets with the criterion of higher levels of volatility (20 periods) presented a superior performance. These findings can be justified by a better risk and return ratio for the portfolio, measured by the Sharpe Index of the returns obtained concerning the portfolio’s volatility, compared to a portfolio formation based on a random selection of the pairs. In addition, the results also showed a low correlation of returns concerning the market index. Therefore, the application of the statistical cointegration analysis methodology alone does not guarantee results that are different from the market average.

Keywords:
pairs trading; portfolio selection; volatility

Statistics		Gross result (%) (No stop)	Gross result (%) (With a stop in time)	Gross result (%) (With a stop in spread)
Mean		-0.0112	-0.0073	-0.0026
Median		0.0427	0.0405	0.0360
Standard deviation		0.2183	0.1789	0.1566
Minimum		-2.7492	-2.3326	-2.5123
Maximum		1.2043	1.2043	1.2043
KS	Statistics	0.186	0.153	0.112
KS	p-value	< 0.001	< 0.001	< 0.001

	Accumulated returns	Standard deviation	Sharpe index
Portfolio with volatility > Medians	85.20%	0.0430	0.50
Random portfolio	0.66%	0.0424	0.19
Ibovespa	108.56%	0.0144	0.78
CDI	33.26%

Percent range of success of signals*	Frequency	Percent frequency	Average operating term (Positive returns)	Average operating term (Negative returns)
0.00 \|- 0.10	1.211	19.51%	2.6	49.5
0.10 \|- 0.20	231	3.72%	28.0	51.0
0.20 \|- 0.30	334	5.38%	28.9	51.7
0.30 \|- 0.40	308	4.96%	29.2	46.7
0.40 \|- 0.50	333	5.37%	30.5	47.4
0.50 \|- 0.60	539	8.69%	30.2	46.9
0.60 \|- 0.70	508	8.19%	28.8	43.5
0.70 \|- 0.80	462	7.44%	30.7	48.0
0.80 \|- 0.90	511	8.23%	31.2	44.2
0.90 \|- 1.00	1.769	28.50%	34.2	7.7
Total	6.206		27.4**	43.7**

Cointegrated pairs	Number of signs	Signs with positive returns	Signs with negative returns	Percentage of success of signs*	Average term (Positive signs)	Average term (Negative signs)
MULT3 x CYRE3	103	18	85	17.48%	21	80
MULT3 x BRML3	102	51	51	50.00%	33	56
MULT3 x EZTC3	86	44	42	51.16%	41	70
TIMP3 x ALUP11	84	44	40	52.38%	20	39
BBDC4 x BBDC3	83	82	1	98.80%	29	52
MULT3 x BBDC3	82	51	31	62.20%	40	57
BOVA11 x ALUP11	82	64	18	78.05%	36	40
VIVT4 x EZTC3	81	56	25	69.14%	16	54
CVCB3 x VIVT4	80	50	30	62.50%	29	50
BOVA11 x ITUB4	79	68	11	86.08%	37	32
ALUP11 x SBSP3	78	45	33	57.69%	22	24
RAPT4 x ALUP11	78	49	29	62.82%	29	52
PETR4 x PETR3	78	48	30	61.54%	33	75
TIMP3 x BRSR6	78	35	43	44.87%	20	76
BBDC4 x BOVA11	76	65	11	85.53%	27	32
B3SA3 x RAPT4	75	57	18	76.00%	13	28
EZTC3 x CYRE3	75	71	4	94.67%	29	24
MULT3 x ALUP11	74	35	39	47.30%	47	35
TIMP3 x ITSA4	74	59	15	79.73%	19	143
ITSA4 x BBAS3	73	59	14	80.82%	43	38
EQTL3 x EZTC3	72	36	36	50.00%	26	27
ECOR3 x ARZZ3	71	39	32	54.93%	24	68
MULT3 x B3SA3	71	42	29	59.15%	32	58
MULT3 x DTEX3	71	35	36	49.30%	28	43

Universidade de São Paulo, Faculdade de Economia, Administração e Contabilidade, Departamento de Contabilidade e Atuária Av. Prof. Luciano Gualberto, 908 - prédio 3 - sala 118, 05508 - 010 São Paulo - SP - Brasil, Tel.: (55 11) 2648-6320, Tel.: (55 11) 2648-6321, Fax: (55 11) 3813-0120 - São Paulo - SP - Brazil
E-mail: recont@usp.br

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ABEV3	BRAP4	CMIG4	ELET3	GFSA3	KROT3	ODPV3	RENT3	TIMP3
ALPA4	BRFS3	CPFE3	ELET6	GGBR4	LAME4	PCAR4	SANB11	TOTS3
ALUP11	BRKM5	CPLE6	ELPL3	GOAU4	LIGT3	PETR3	SAPR4	TRPL4
ARZZ3	BRML3	CSAN3	EMBR3	GOLL4	LREN3	PETR4	SBSP3	UGPA3
BBAS3	BRSR6	CSNA3	ENBR3	HGTX3	MDIA3	POMO4	SEER3	USIM3
BBDC3	BTOW3	CVCB3	EQTL3	HYPE3	MRFG3	PSSA3	SMLS3	USIM5
BBDC4	B3SA3	CYRE3	ESTC3	ITSA4	MRVE3	QUAL3	SULA11	VALE3
BBSE3	CCRO3	DTEX3	EZTC3	ITUB4	MULT3	RADL3	SUZB3	VIVT4
BEEF3	CESP6	ECOR3	FIBR3	JBSS3	MYPK3	RAIL3	TAEE11	VLID3
BOVA11	CIEL3	EGIE3	FLRY3	KLBN11	NATU3	RAPT4	TIET11	WEGE3