UNIVERSIDADE FEDERAL DA PARAÍBA
remove(list=ls())
par(mfrow=c(1,1))
options(scipen=999)
options(max.print = 100000)
require(fPortfolio)
library(tseries)
library(fBasics)
library(whitestrap)
library(tidyverse)
library(knitr)
library(xts)
library(MASS)
library(kableExtra)
library(gridExtra)2024/03/07 a 2024/07/17
# Intervalo de Tempo dos dados
dataini <- "2024-04-24"
datafim <- "2024-07-22"Retorno, média dos Retornos, Desvio Padrão, Coeficiente de Variação e Beta Mercado.
# Lista de Empresas que serão Requeridas
empresas <- list(
ibov = "^BVSP",
vivo = "vivt3.sa",
totvs = "tots3.sa",
weg = "wege3.sa",
tim = "tims3.sa",
localiza = "rent3.sa",
transmissao_paulista = "trpl3.sa",
cemig = "cmig3.sa",
engie = "egie3.sa",
taesa = "taee3.sa",
cpfl = "cpfe3.sa",
cyrela = "cyre3.sa",
aliansce = "alos3.sa",
ccr = "ccro3.sa",
multiplan = "mult3.sa",
eztc = "eztc3.sa",
suzano = "suzb3.sa",
vibra = "vbbr3.sa",
klabin = "klbn3.sa",
csn_mineracao = "cmin3.sa",
gerdau = "ggbr3.sa",
slc = "slce3.sa",
fleury = "flry3.sa",
hypera = "hype3.sa",
minerva = "beef3.sa",
arezo = "arzz3.sa"
)
# Loop de busca para obter os dados e calcular os retornos
retornos <- lapply(empresas, function(ticker) {
data <- na.omit(get.hist.quote(instrument = ticker, quote = "Close", start = dataini, end = datafim))
diff(log(data))
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time series ends 2024-07-19# Checar o tamanho da amostra para 'vivo'
length(retornos$vivo)[1] 60# Calcular indicadores de desempenho
calcular_indicadores <- function(retorno) {
risco <- sd(retorno) / mean(retorno)
retorno_medio <- mean(retorno) * 100
volatilidade <- (sd(retorno) * 100) * sqrt(252)
list(risco = risco, retorno_medio = retorno_medio, volatilidade = volatilidade)
}
# Calcular indicadores para todas as empresas
indicadores <- lapply(retornos, calcular_indicadores)
# Criar data frame final com os resultados
df2 <- data.frame(
Empresas = c("Ibov", "Vivo", "Totvs", "Weg", "Tim", "Localiza",
"Transmissão Paulista", "Cemig", "Engie", "Taesa", "CPFL", "Cyrela",
"Aliansce", "CCR", "Multiplan", "EZTec", "Suzano",
"Vibra Energia", "Klabin", "CSN Mineração", "Gerdau", "Slc", "Fleury",
"Hypera", "Minerva", "Arezo"),
Retorno = sapply(indicadores, `[[`, "retorno_medio"),
Risco = sapply(indicadores, `[[`, "risco")
# Volatilidade = sapply(indicadores, `[[`, "volatilidade")
)
# Usar kable para criar a tabela e kableExtra para formatá-la
kable(df2[,-1], format = "html") |>
kable_styling(bootstrap_options = c("striped", "hover", "condensed")) |>
row_spec(0, background = "#2166AC") |> # Cabeçalho da tabela em azul
row_spec(2:6, background = "#C7EAE5") |> # Linhas em diferentes tons de azul
row_spec(7:11, background = "#92C5DE") |>
row_spec(12:16, background = "#D1E5F0") |>
row_spec(17:21, background = "#E7D4E8") |>
row_spec(22:26, background = "#FFAABB")| Retorno | Risco | |
|---|---|---|
| ibov | 0.0379769 | 18.214420 |
| vivo | 0.0388613 | 42.230186 |
| totvs | -0.0029673 | -503.682618 |
| weg | 0.3544639 | 3.167515 |
| tim | -0.0499303 | -33.052475 |
| localiza | -0.2194780 | -8.653851 |
| transmissao_paulista | -0.0460049 | -30.983748 |
| cemig | 0.1968771 | 8.337587 |
| engie | 0.2263159 | 5.286272 |
| taesa | -0.0622991 | -14.191695 |
| cpfl | -0.0993705 | -14.953551 |
| cyrela | -0.1018746 | -19.862232 |
| aliansce | 0.0070475 | 241.499458 |
| ccr | 0.0148388 | 93.363825 |
| multiplan | 0.0550332 | 30.767545 |
| eztc | 0.0791515 | 29.385511 |
| suzano | -0.2034760 | -13.574925 |
| vibra | 0.0438815 | 40.975063 |
| klabin | 0.0000000 | Inf |
| csn_mineracao | 0.0229436 | 100.080600 |
| gerdau | 0.0469226 | 29.728891 |
| slc | 0.0409822 | 38.199974 |
| fleury | 0.1194938 | 10.966891 |
| hypera | -0.0239141 | -82.878390 |
| minerva | 0.1323446 | 18.689462 |
| arezo | 0.0015944 | 942.785491 |
# Definir as empresas e seus tickers
empresas2 <- c(
ibov = "^BVSP",
vivo = "vivt3.sa",
totvs = "tots3.sa",
weg = "wege3.sa",
tim = "tims3.sa",
localiza = "rent3.sa",
transmissao_paulista = "trpl3.sa",
cemig = "cmig3.sa",
engie = "egie3.sa",
taesa = "taee3.sa",
cpfl = "cpfe3.sa",
cyrela = "cyre3.sa",
aliansce = "alos3.sa",
ccr = "ccro3.sa",
multiplan = "mult3.sa",
eztc = "eztc3.sa",
suzano = "suzb3.sa",
vibra = "vbbr3.sa",
klabin = "klbn3.sa",
csn_mineracao = "cmin3.sa",
gerdau = "ggbr3.sa",
slc = "slce3.sa",
fleury = "flry3.sa",
hypera = "hype3.sa",
minerva = "beef3.sa",
arezo = "arzz3.sa"
)
# Função para calcular beta e p-valor para uma empresa específica
calcular_beta <- function(ticker, index_ticker, dataini, datafim) {
# Obter dados de fechamento para a empresa e o índice
preco_empresa <- na.omit(get.hist.quote(instrument = ticker, quote = "Close", start = dataini, end = datafim, retclass = "zoo"))
preco_indice <- na.omit(get.hist.quote(instrument = index_ticker, quote = "Close", start = dataini, end = datafim, retclass = "zoo"))
# Calcular retornos diários
retorno_empresa <- diff(log(preco_empresa))
retorno_indice <- diff(log(preco_indice))
# Ajustar comprimentos se necessário
n <- min(length(retorno_empresa), length(retorno_indice))
retorno_empresa <- retorno_empresa[1:n]
retorno_indice <- retorno_indice[1:n]
# Calcular beta e p-valor usando regressão linear simples
modelo <- lm(retorno_empresa ~ retorno_indice)
beta <- coef(modelo)[2]
pvalor <- summary(modelo)$coefficients[2, 4]
list(beta = beta, pvalor = pvalor)
}
# Calcular beta e p-valor para cada empresa em relação ao índice Bovespa
resultados <- lapply(empresas2[-1], function(ticker) {
calcular_beta(ticker, empresas2["ibov"], dataini, datafim)
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time series ends 2024-07-19# Criar data frame com os resultados
df_resultados <- data.frame(
Empresa = names(resultados),
Beta = sapply(resultados, function(res) res$beta),
P_valor = sapply(resultados, function(res) res$pvalor)
) %>%
arrange(desc(Beta)) # Ordenar por beta decrescente
# Exibir a tabela de resultados com kable
kable(df_resultados[,-1], caption = "Beta Mercado", format = "html") |>
kable_styling(bootstrap_options = c("striped", "hover", "condensed"))| Beta | P_valor | |
|---|---|---|
| eztc.retorno_indice | 2.0432259 | 0.0000003 |
| cyrela.retorno_indice | 1.8756348 | 0.0000000 |
| localiza.retorno_indice | 1.7458951 | 0.0000000 |
| multiplan.retorno_indice | 1.6489462 | 0.0000000 |
| csn_mineracao.retorno_indice | 1.5730156 | 0.0001311 |
| minerva.retorno_indice | 1.5138134 | 0.0007508 |
| vibra.retorno_indice | 1.4081974 | 0.0000078 |
| hypera.retorno_indice | 1.3559927 | 0.0001341 |
| ccr.retorno_indice | 1.0950261 | 0.0000062 |
| fleury.retorno_indice | 1.0354156 | 0.0000063 |
| vivo.retorno_indice | 0.9462028 | 0.0015976 |
| gerdau.retorno_indice | 0.9109368 | 0.0002914 |
| tim.retorno_indice | 0.8951672 | 0.0031374 |
| cemig.retorno_indice | 0.8416580 | 0.0054265 |
| cpfl.retorno_indice | 0.7892085 | 0.0038814 |
| arezo.retorno_indice | 0.7745893 | 0.0051837 |
| totvs.retorno_indice | 0.7389855 | 0.0074777 |
| engie.retorno_indice | 0.6797449 | 0.0018942 |
| suzano.retorno_indice | 0.6077161 | 0.2457088 |
| aliansce.retorno_indice | 0.6034199 | 0.0589438 |
| taesa.retorno_indice | 0.5920593 | 0.0001938 |
| klabin.retorno_indice | 0.5254745 | 0.0119201 |
| slc.retorno_indice | 0.4423381 | 0.1345073 |
| weg.retorno_indice | 0.2655299 | 0.2116855 |
| transmissao_paulista.retorno_indice | 0.2169622 | 0.4233427 |
# Função para criar histogramas usando 'hist' do R base
criar_histograma <- function(retorno, nome) {
hist(retorno, breaks = 30, main = nome, xlab = "Retorno", ylab = "Frequência", col = "#006860", border = "#40A8A0")
}
# Definir layout para exibir múltiplos histogramas
par(mfrow = c(3, 2))
# Criar histogramas para todas as empresas, agrupando em conjuntos de 5 empresas mais o IBOV
for (i in seq(1, length(empresas) - 1, by = 5)) {
criar_histograma(retornos[[1]], "IBOV")
for (j in i:min(i+4, length(empresas) - 1)) {
criar_histograma(retornos[[j + 1]], names(empresas)[j + 1])
}
# Esperar antes de mostrar o próximo conjunto
if (i + 5 < length(empresas)) {
readline(prompt="Conjunto de 5 Empresas + IBOV")
}
}
Conjunto de 5 Empresas + IBOV
Conjunto de 5 Empresas + IBOV
Conjunto de 5 Empresas + IBOV
Conjunto de 5 Empresas + IBOV