Does the stock market lead the economy?
Antonio Houde
3/5/2020
- Q1.
stock.pricesImport S&P500 since 1999. - Q2. Prepare the imported stock prices for merge.
- Q3
economic.dataImport real U.S. GDP growth since 2000. - Q4. Prepare the imported economic data for merge.
- Q5. Merge the two data sets.
- Q6. Plot the relaionship between stock returns and GDP growth?
- Q7. Describe the relationship between the stock markets and economic growth.
- Q8 Hide the messages, the code and its results on the webpage.
- Q9 Display the title and your name correctly at the top of the webpage.
- Q10 Use the correct slug.
# Load packages
library(tidyquant)
library(tidyverse) stock.prices
# Import NASDAQ since 2009.
stocks <- tq_get("^IXIC", get = "stock.prices", from = "2009-01-01")
stocks## # A tibble: 2,824 x 8
## symbol date open high low close volume adjusted
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 ^IXIC 2009-01-02 1579. 1636. 1572. 1632. 1438410000 1632.
## 2 ^IXIC 2009-01-05 1621. 1640. 1605. 1628. 1816580000 1628.
## 3 ^IXIC 2009-01-06 1642. 1666. 1636. 1652. 2137640000 1652.
## 4 ^IXIC 2009-01-07 1622. 1625. 1588. 1599. 2020170000 1599.
## 5 ^IXIC 2009-01-08 1590. 1617. 1584. 1617. 1968160000 1617.
## 6 ^IXIC 2009-01-09 1617. 1617. 1570. 1572. 1907390000 1572.
## 7 ^IXIC 2009-01-12 1573. 1573. 1528 1539. 1763590000 1539.
## 8 ^IXIC 2009-01-13 1537. 1558. 1527. 1546. 1965570000 1546.
## 9 ^IXIC 2009-01-14 1522. 1529. 1485. 1490. 1919980000 1490.
## 10 ^IXIC 2009-01-15 1489. 1522. 1457. 1512. 2507870000 1512.
## # … with 2,814 more rows# Calculate returns
stock_returns <-
stocks %>%
# Calculate yearly returns
tq_transmute(select = adjusted, mutate_fun = periodReturn, period = "yearly") %>%
# Create a new variable, year
mutate(year = year(date)+1) %>%
# Drop date
select(-date)
stock_returns## # A tibble: 12 x 2
## yearly.returns year
## <dbl> <dbl>
## 1 0.390 2010
## 2 0.169 2011
## 3 -0.0180 2012
## 4 0.159 2013
## 5 0.383 2014
## 6 0.134 2015
## 7 0.0573 2016
## 8 0.0750 2017
## 9 0.282 2018
## 10 -0.0388 2019
## 11 0.352 2020
## 12 -0.235 2021economic.data
# Import real U.S. GDP growth since 2010.
econ <- tq_get("A191RL1A225NBEA", get = "economic.data", from = "2010-01-01")
econ_decimal <-
econ %>%
mutate(year = year(date), # Create a new variable, year
GDPgrowth = price/100) %>% # Convert price to decimal number
# Drop date
select(-date)
econ_decimal## # A tibble: 10 x 4
## symbol price year GDPgrowth
## <chr> <dbl> <dbl> <dbl>
## 1 A191RL1A225NBEA 2.6 2010 0.026
## 2 A191RL1A225NBEA 1.6 2011 0.016
## 3 A191RL1A225NBEA 2.2 2012 0.022
## 4 A191RL1A225NBEA 1.8 2013 0.018
## 5 A191RL1A225NBEA 2.5 2014 0.025
## 6 A191RL1A225NBEA 2.9 2015 0.0290
## 7 A191RL1A225NBEA 1.6 2016 0.016
## 8 A191RL1A225NBEA 2.4 2017 0.024
## 9 A191RL1A225NBEA 2.9 2018 0.0290
## 10 A191RL1A225NBEA 2.3 2019 0.023Merge
# Merge the two data sets.
data_merged <-
econ_decimal %>%
left_join(stock_returns)
data_merged## # A tibble: 10 x 5
## symbol price year GDPgrowth yearly.returns
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 A191RL1A225NBEA 2.6 2010 0.026 0.390
## 2 A191RL1A225NBEA 1.6 2011 0.016 0.169
## 3 A191RL1A225NBEA 2.2 2012 0.022 -0.0180
## 4 A191RL1A225NBEA 1.8 2013 0.018 0.159
## 5 A191RL1A225NBEA 2.5 2014 0.025 0.383
## 6 A191RL1A225NBEA 2.9 2015 0.0290 0.134
## 7 A191RL1A225NBEA 1.6 2016 0.016 0.0573
## 8 A191RL1A225NBEA 2.4 2017 0.024 0.0750
## 9 A191RL1A225NBEA 2.9 2018 0.0290 0.282
## 10 A191RL1A225NBEA 2.3 2019 0.023 -0.0388Plot
# Visualize the relaionship between stock returns and GDP growth
data_merged %>%
ggplot(aes(yearly.returns, GDPgrowth)) +
geom_point() +
scale_x_continuous(label = scales::percent) +
scale_y_continuous(label = scales::percent) +
geom_smooth(method = "lm")
Q1. stock.prices Import S&P500 since 1999.
Hint: Google tidyquant::tq_get() to find example codes.
stocks <- tq_get("^GSPC", get = "stock.prices", from = "1999-01-01")
stocks## # A tibble: 5,339 x 8
## symbol date open high low close volume adjusted
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 ^GSPC 1999-01-04 1229. 1249. 1219. 1228. 877000000 1228.
## 2 ^GSPC 1999-01-05 1228. 1246. 1228. 1245. 775000000 1245.
## 3 ^GSPC 1999-01-06 1245. 1272. 1245. 1272. 986900000 1272.
## 4 ^GSPC 1999-01-07 1272. 1272. 1258. 1270. 863000000 1270.
## 5 ^GSPC 1999-01-08 1270. 1278. 1262. 1275. 937800000 1275.
## 6 ^GSPC 1999-01-11 1275. 1276. 1253. 1264. 818000000 1264.
## 7 ^GSPC 1999-01-12 1264. 1264. 1238. 1240. 800200000 1240.
## 8 ^GSPC 1999-01-13 1240. 1248. 1205. 1234. 931500000 1234.
## 9 ^GSPC 1999-01-14 1234. 1237. 1210. 1212. 797200000 1212.
## 10 ^GSPC 1999-01-15 1212. 1243. 1212. 1243. 798100000 1243.
## # … with 5,329 more rowsQ2. Prepare the imported stock prices for merge.
Hint: Calculate yearly returns; create a new variable, year; and drop date.
stock_returns <-
stocks %>%
# Calculate yearly returns
tq_transmute(select = adjusted, mutate_fun = periodReturn, period = "yearly") %>%
# Create a new variable, year
mutate(year = year(date)+1) %>%
# Drop date
select(-date)
stock_returns## # A tibble: 22 x 2
## yearly.returns year
## <dbl> <dbl>
## 1 0.196 2000
## 2 -0.101 2001
## 3 -0.130 2002
## 4 -0.234 2003
## 5 0.264 2004
## 6 0.0899 2005
## 7 0.0300 2006
## 8 0.136 2007
## 9 0.0353 2008
## 10 -0.385 2009
## # … with 12 more rowsQ3 economic.data Import real U.S. GDP growth since 2000.
Hint: Find the symbol in FRED. Select in the list of related variables, Percent Change from Preceding Period, Annual, Not Seasonally Adjusted.
# Import real U.S. GDP growth since 2010.
econ <- tq_get("A191RL1A225NBEA", get = "economic.data", from = "2010-01-01")
econ_decimal <-
econ %>%
mutate(year = year(date), # Create a new variable, year
GDPgrowth = price/100) %>% # Convert price to decimal number
# Drop date
select(-date)
econ_decimal## # A tibble: 10 x 4
## symbol price year GDPgrowth
## <chr> <dbl> <dbl> <dbl>
## 1 A191RL1A225NBEA 2.6 2010 0.026
## 2 A191RL1A225NBEA 1.6 2011 0.016
## 3 A191RL1A225NBEA 2.2 2012 0.022
## 4 A191RL1A225NBEA 1.8 2013 0.018
## 5 A191RL1A225NBEA 2.5 2014 0.025
## 6 A191RL1A225NBEA 2.9 2015 0.0290
## 7 A191RL1A225NBEA 1.6 2016 0.016
## 8 A191RL1A225NBEA 2.4 2017 0.024
## 9 A191RL1A225NBEA 2.9 2018 0.0290
## 10 A191RL1A225NBEA 2.3 2019 0.023Q4. Prepare the imported economic data for merge.
Hint: Create a new variable, year; convert price to decimal number; and drop date.
# Import real U.S. GDP growth since 2010.
econ <- tq_get("A191RL1A225NBEA", get = "economic.data", from = "2010-01-01")
econ_decimal <-
econ %>%
mutate(year = year(date), # Create a new variable, year
GDPgrowth = price/100) %>% # Convert price to decimal number
# Drop date
select(-date)
econ_decimal## # A tibble: 10 x 4
## symbol price year GDPgrowth
## <chr> <dbl> <dbl> <dbl>
## 1 A191RL1A225NBEA 2.6 2010 0.026
## 2 A191RL1A225NBEA 1.6 2011 0.016
## 3 A191RL1A225NBEA 2.2 2012 0.022
## 4 A191RL1A225NBEA 1.8 2013 0.018
## 5 A191RL1A225NBEA 2.5 2014 0.025
## 6 A191RL1A225NBEA 2.9 2015 0.0290
## 7 A191RL1A225NBEA 1.6 2016 0.016
## 8 A191RL1A225NBEA 2.4 2017 0.024
## 9 A191RL1A225NBEA 2.9 2018 0.0290
## 10 A191RL1A225NBEA 2.3 2019 0.023Q5. Merge the two data sets.
Hint: Google dplyr::left_join() to find example codes.
# Merge the two data sets.
data_merged <-
econ_decimal %>%
left_join(stock_returns)
data_merged## # A tibble: 10 x 5
## symbol price year GDPgrowth yearly.returns
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 A191RL1A225NBEA 2.6 2010 0.026 0.235
## 2 A191RL1A225NBEA 1.6 2011 0.016 0.128
## 3 A191RL1A225NBEA 2.2 2012 0.022 -0.0000318
## 4 A191RL1A225NBEA 1.8 2013 0.018 0.134
## 5 A191RL1A225NBEA 2.5 2014 0.025 0.296
## 6 A191RL1A225NBEA 2.9 2015 0.0290 0.114
## 7 A191RL1A225NBEA 1.6 2016 0.016 -0.00727
## 8 A191RL1A225NBEA 2.4 2017 0.024 0.0954
## 9 A191RL1A225NBEA 2.9 2018 0.0290 0.194
## 10 A191RL1A225NBEA 2.3 2019 0.023 -0.0624Q6. Plot the relaionship between stock returns and GDP growth?
Hint: See the code in 4.2.1 Scatterplot in the textbook.
# Visualize the relaionship between stock returns and GDP growth
data_merged %>%
ggplot(aes(yearly.returns, GDPgrowth)) +
geom_point() +
scale_x_continuous(label = scales::percent) +
scale_y_continuous(label = scales::percent) +
geom_smooth(method = "lm")
Q7. Describe the relationship between the stock markets and economic growth.
As the yearly return percentage increases, gdp increases as well.
Q8 Hide the messages, the code and its results on the webpage.
Hint: Use message, echo and results in the chunk options. Refer to the RMarkdown Reference Guide.