Learning, Portfolio Complexity and Informational Asymmetry in Forecasts of Sell-Side Analysts

Lima, Melquiades Pereira de

doi:10.15728/bbr.2017.14.2.1

Melquiades Pereira de Lima Júnior^†*Author for correspondence: †. Doutor. Institution: Instituto Federal de Educação do Rio Grande do Norte IFRN. Rua Pedro de Souza nº 141, Condomínio Solar Portal do Potengi apartamento 413, Bloco E, Bom Pastor, Natal - RN, Brazil. E-mail: melquiades.pereira@gmail.com

Instituto Federal de Educação do Rio Grande do Norte IFRN

*Author for correspondence: †. Doutor. Institution: Instituto Federal de Educação do Rio Grande do Norte IFRN. Rua Pedro de Souza nº 141, Condomínio Solar Portal do Potengi apartamento 413, Bloco E, Bom Pastor, Natal - RN, Brazil. E-mail: melquiades.pereira@gmail.com

Metrics	Mean	Standard Deviation	Median	Minimum	Maximum	Amplitude
PFE	-0.70	2.75	-0.20	-117.85	0.95	118.80
PAFE	0.83	2.72	0.30	0.00	117.85	117.85
REV	0.01	0.21	0.00	-0.96	22.73	23.69
CREV	0.01	0.17	0.00	-0.70	6.16	6.86
CRR	0.06	0.47	0.00	-0.97	30.65	31.62
CMAR	0.03	0.45	0.01	-1.70	30.72	32.42
EXPGEN	30.68	22.16	26.00	0.00	95.00	95.00
EXPSETOR	79.77	99.77	43.00	0.00	716.00	716.00
EXPASSET	14.76	14.65	10.00	0.00	154.00	154.00
NSETOR	2.98	2.92	2.00	0.00	16.00	16.00
NASSET	6.01	5.51	5.00	0.00	46.00	46.00

Period	N	PFE	PAFE	REV	CREV	CRR	CMAR	EXPGEN	EXPSETOR	EXPASSET	NSETOR	NASSET
2005	2017	0.04	0.27	0.01	0.00	0.21	-0.15	5.48	8.36	4.21	1.33	3.11
2006	3060	0.00	0.27	0.03	0.06	0.16	-0.15	14.15	23.45	9.04	1.66	3.61
2007	4502	-0.79	0.89	0.03	0.05	-0.07	-0.14	18.44	30.67	9.42	1.92	4.35
2008	7163	-1.68	1.73	-0.02	-0.01	0.02	0.01	23.56	44.34	11.07	2.31	5.04
2009	7884	-0.08	0.35	0.03	0.01	0.26	-0.03	27.47	63.44	13.20	2.73	5.19
2010	10888	-0.50	0.60	0.02	0.02	0.01	0.08	32.55	83.68	14.43	3.07	5.83
2011	12843	-0.67	0.77	0.00	0.00	0.03	0.05	36.96	102.29	17.47	3.41	6.59
2012	14191	-0.94	1.07	0.01	0.00	0.04	0.12	39.95	121.22	19.72	3.88	8.04

	PFE	PAFE	REV	CREV	CRR	CMAR	EXPGEN	EXPSETOR	EXPASSET	NSETOR
PFE
PAFE	-0.70^* ***** p < 0.01.
REV	0.02^* ***** p < 0.01.	-0.03^* ***** p < 0.01.
CREV	0.15^* ***** p < 0.01.	-0.16^* ***** p < 0.01.	0.13^* ***** p < 0.01.
CRR	0.44^* ***** p < 0.01.	-0.46^* ***** p < 0.01.	0.00	0.00
CMAR	0.23^* ***** p < 0.01.	-0.28^* ***** p < 0.01.	0.01^ p < 0.05;	0.06^* ***** p < 0.01.	0.70^* ***** p < 0.01.
EXPGEN	-0.02^* ***** p < 0.01.	-0.02^* ***** p < 0.01.	-0.01^ p < 0.05;	-0.04^* ***** p < 0.01.	-0.01^* * p < 0.1;	0.11^* ***** p < 0.01.
EXPSETOR	-0.01^ p < 0.05;	-0.05^* ***** p < 0.01.	-0.01^* ***** p < 0.01.	-0.05^* ***** p < 0.01.	0.00	0.10^* ***** p < 0.01.	0.86^* ***** p < 0.01.
EXPASSET	0.01^* * p < 0.1;	-0.04^* ***** p < 0.01.	0.00	-0.05^* ***** p < 0.01.	-0.02^* ***** p < 0.01.	0.07^* ***** p < 0.01.	0.63^* ***** p < 0.01.	0.74^* ***** p < 0.01.
NSETOR	0.00	-0.08^* ***** p < 0.01.	-0.04^* ***** p < 0.01.	-0.01^* ***** p < 0.01.	0.01^* ***** p < 0.01.	0.08^* ***** p < 0.01.	0.27^* ***** p < 0.01.	0.39^* ***** p < 0.01.	0.16^* ***** p < 0.01.
NASSET	-0.01^* * p < 0.1;	-0.04^* ***** p < 0.01.	-0.03^* ***** p < 0.01.	-0.01^* ***** p < 0.01.	0.01	0.11^* ***** p < 0.01.	0.38^* ***** p < 0.01.	0.33^* ***** p < 0.01.	0.15^* ***** p < 0.01.	0.61^* ***** p < 0.01.

Hypotheses	1	2	3
Dependent Variable	PAFE	PFE	CMAR
EXPGEN	0.011^* ***** p < 0.01.	-0.011^* ***** p < 0.01.	0.0001
EXPGEN	(0.001)	(0.001)	(0.0001)
EXPSETOR	-0.002^* ***** p < 0.01.	0.003^* ***** p < 0.01.	0.00002
EXPSETOR	(0.0003)	(0.0002)	(0.00004)
EXPASSET	-0.002^* * p < 0.1;	0.002^* * p < 0.1;	-0.0003
EXPASSET	(0.001)	(0.001)	(0.0002)
NSETOR	0.038^* ***** p < 0.01.	-0.044^* ***** p < 0.01.	-0.001
NSETOR	(0.005)	(0.005)	(0.001)
NASSET	-0.027^* ***** p < 0.01.	0.028^* ***** p < 0.01.	-0.001
NASSET	(0.003)	(0.003)	(0.001)
log(VOLM)	0.223^* ***** p < 0.01.	-0.227^* ***** p < 0.01.	-0.011^ p < 0.05;
log(VOLM)	(0.045)	(0.045)	(0.004)
CONFLICT	-0.371^* ***** p < 0.01.	0.565^* ***** p < 0.01.	-0.118^* ***** p < 0.01.
CONFLICT	(0.053)	(0.050)	(0.043)
IBOV	-2.892^* ***** p < 0.01.	3.056^* ***** p < 0.01.	-0.374^* ***** p < 0.01.
IBOV	(0.249)	(0.258)	(0.049)
REVGRADE	0.161^ p < 0.05;	-0.144^ p < 0.05;	0.005
REVGRADE	(0.063)	(0.066)	(0.011)
EXPGEN: CONFLICT	0.004^* ***** p < 0.01.	-0.004^* ***** p < 0.01.	0.004^* ***** p < 0.01.
EXPGEN: CONFLICT	(0.002)	(0.001)	(0.002)
EXPSETOR: CONFLICT	0.0004^* * p < 0.1;	-0.001^* ***** p < 0.01.	-0.0004^* * p < 0.1;
EXPSETOR: CONFLICT	(0.0002)	(0.0002)	(0.0002)
EXPASSET: CONFLICT	-0.004^* * p < 0.1;	0.004^ p < 0.05;	-0.005^* ***** p < 0.01.
EXPASSET: CONFLICT	(0.002)	(0.002)	(0.001)
NSETOR: CONFLICT	0.007	-0.010	0.018^* ***** p < 0.01.
NSETOR: CONFLICT	(0.010)	(0.011)	(0.004)
NASSET: CONFLICT	-0.005	0.003	0.004^ p < 0.05;
NASSET: CONFLICT	(0.005)	(0.006)	(0.002)
Observations	62,006	62,006	62,006
R²	0.030	0.032	0.011
Adjusted R²	0.029	0.032	0.011

Hypotheses	4	5	6
	CMAR	log(VOLM)	PAFE
REV	0.074	-0.014	-0.254
REV	(0.056)	(0.104)	(0.193)
lag(REV, 1)	0.024	0.276	-0.206
lag(REV, 1)	(0.039)	(0.194)	(0.262)
CREV	-0.174^* ***** p < 0.01.	-0.036	-0.235
CREV	(0.044)	(0.207)	(0.800)
lag(CREV, 1)	-0.162^* ***** p < 0.01.	-0.005	-0.541
lag(CREV, 1)	(0.055)	(0.166)	(0.402)
REVGRADE	0.052^* * p < 0.1;	0.024	0.235
REVGRADE	(0.028)	(0.068)	(0.188)
CREVGRADE	-0.056^* ***** p < 0.01.	0.366^* ***** p < 0.01.	0.456^* ***** p < 0.01.
CREVGRADE	(0.007)	(0.052)	(0.074)
log(VOLM)	-0.020^* ***** p < 0.01.		0.410^* ***** p < 0.01.
log(VOLM)	(0.006)		(0.069)
CONFLICT	-0.013	-0.209^* ***** p < 0.01.	-0.288^* ***** p < 0.01.
CONFLICT	(0.025)	(0.025)	(0.045)
IBOV	-0.585^* ***** p < 0.01.	-0.171	-2.480^* ***** p < 0.01.
IBOV	(0.058)	(0.129)	(0.353)
Observations	23.668	23.668	23.668
R²	0.015	0.017	0.044
Adjusted^R2	0.015	0.017	0.044

{CRR}_{ijt} = \{\begin{matrix} {LP}_{ijt (t - n)} / {LP}_{ijt} - 1 \\ 0 \\ {LP}_{ijt} / {LP}_{ij (t = n)} - 1 \end{matrix} \begin{matrix} caso 1 \leq {REC}_{ijt} < 3 \\ caso {REC}_{ijt} = 3; \\ caso 3 < {REC}_{ijt} \leq 5; \end{matrix}

\begin{matrix} {CMAR}_{ijt} = β_{0} + β_{1} {REV}_{ijt} + β_{2} lag ({REV}_{ijt}, 1) + β_{3} {CREV}_{ijt} + β_{4} lag ({CREV}_{ijt}, 1) + \\ \dots β_{5} {REVGRADE}_{ijt} + β_{6} {CREVGRADE}_{ijt} + \log (VOLM) + ε \end{matrix}

\begin{matrix} \log {(VOLM)}_{ijt} = β_{0} + β_{1} {REV}_{ijt} + β_{2} lag ({REV}_{ijt}, 1) + β_{3} {CREV}_{ijt} + β_{4} lag ({CREV}_{ijt}, 1) + \\ \dots β_{5} {REVGRADE}_{ijt} + β_{6} {CREVGRADE}_{ijt} + ε \end{matrix}

\begin{matrix} {PAFE}_{ijt} = β_{0} + β_{1} {REV}_{ijt} + β_{2} lag ({REV}_{ijt}, 1) + β_{3} {CREV}_{ijt} + β_{4} lag ({CREV}_{ijt}, 1) + \\ \dots β_{5} {REVGRADE}_{ijt} + β_{6} {CREVGRADE}_{ijt} + ε \end{matrix}

Hypothesis	Test 1	Test 2	Test 3	Test 4	Test 5
1	520970000	19.151	3988.1	417.69	71806
sig.	^* ***** p < 0.01.	^* ***** p < 0.01.	^* ***** p < 0.01.	^* ***** p < 0.01.	^* ***** p < 0.01.
2	521700000	20.539	8428.5	431.79	69535
sig.	^* ***** p < 0.01.	^* ***** p < 0.01.	^* ***** p < 0.01.	^* ***** p < 0.01.	^* ***** p < 0.01.
3	693160000	13.298	66.6	301.6	123410
sig.	^* ***** p < 0.01.	^* ***** p < 0.01.	^* ***** p < 0.01.	^* ***** p < 0.01.	^* ***** p < 0.01.
4	190450000	4.6524	72.511	31.027	49491
sig.	^* ***** p < 0.01.	^* ***** p < 0.01.	^* ***** p < 0.01.	^* ***** p < 0.01.	^* ***** p < 0.01.
5	475990000	3.3982	39.036	36.116	452.19
sig.	^* ***** p < 0.01.	^* ***** p < 0.01.	^* ***** p < 0.01.	^* ***** p < 0.01.	^* ***** p < 0.01.
6	670380000	2.5054	34.392	19.931	55581
sig.	^* ***** p < 0.01.	^* ***** p < 0.01.	^* ***** p < 0.01.	^* ***** p < 0.01.	^* ***** p < 0.01.

[1] *Author for correspondence: †. Doutor. Institution: Instituto Federal de Educação do Rio Grande do Norte IFRN. Rua Pedro de Souza nº 141, Condomínio Solar Portal do Potengi apartamento 413, Bloco E, Bom Pastor, Natal - RN, Brazil. E-mail: melquiades.pereira@gmail.com

	PFE	PAFE	REV	CREV	CRR	CMAR	EXPGEN	EXPSETOR	EXPASSET	NSETOR
PFE
PAFE	-0.70^* ***** p < 0.01.
REV	0.02^* ***** p < 0.01.	-0.03^* ***** p < 0.01.
CREV	0.15^* ***** p < 0.01.	-0.16^* ***** p < 0.01.	0.13^* ***** p < 0.01.
CRR	0.44^* ***** p < 0.01.	-0.46^* ***** p < 0.01.	0.00	0.00
CMAR	0.23^* ***** p < 0.01.	-0.28^* ***** p < 0.01.	0.01^ p < 0.05;	0.06^* ***** p < 0.01.	0.70^* ***** p < 0.01.
EXPGEN	-0.02^* ***** p < 0.01.	-0.02^* ***** p < 0.01.	-0.01^ p < 0.05;	-0.04^* ***** p < 0.01.	-0.01^* * p < 0.1;	0.11^* ***** p < 0.01.
EXPSETOR	-0.01^ p < 0.05;	-0.05^* ***** p < 0.01.	-0.01^* ***** p < 0.01.	-0.05^* ***** p < 0.01.	0.00	0.10^* ***** p < 0.01.	0.86^* ***** p < 0.01.
EXPASSET	0.01^* * p < 0.1;	-0.04^* ***** p < 0.01.	0.00	-0.05^* ***** p < 0.01.	-0.02^* ***** p < 0.01.	0.07^* ***** p < 0.01.	0.63^* ***** p < 0.01.	0.74^* ***** p < 0.01.
NSETOR	0.00	-0.08^* ***** p < 0.01.	-0.04^* ***** p < 0.01.	-0.01^* ***** p < 0.01.	0.01^* ***** p < 0.01.	0.08^* ***** p < 0.01.	0.27^* ***** p < 0.01.	0.39^* ***** p < 0.01.	0.16^* ***** p < 0.01.
NASSET	-0.01^* * p < 0.1;	-0.04^* ***** p < 0.01.	-0.03^* ***** p < 0.01.	-0.01^* ***** p < 0.01.	0.01	0.11^* ***** p < 0.01.	0.38^* ***** p < 0.01.	0.33^* ***** p < 0.01.	0.15^* ***** p < 0.01.	0.61^* ***** p < 0.01.

Brasil

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Learning, Portfolio Complexity and Informational Asymmetry in Forecasts of Sell-Side Analysts

ABSTRACT