3 Economic data over time

Code

library(tidyverse)
D <- read_csv("data/macro/inflation_WDI.csv")
oecd <- read_csv("data/macro/oced_codes.csv")

#biggerText <- jzPack::biggerText

# Merge in OECD indicators
D <- left_join(D,oecd)

3.1 Analysis of inflation patterns

In how many countries has inflation exceeded 10% in 2022:

Code

D %>% 
  filter(year == 2022) %>%
  mutate(over10 = ifelse(inflation > 10, 1, 0)) %>%
  count(over10)

# A tibble: 2 x 2
  over10     n
   <dbl> <int>
1      0    82
2      1    45

Code

D %>% 
  filter(year %in% c(2010:2022)) %>%
  mutate(over10 = ifelse(inflation > 10, 1, 0)) %>%
  group_by(year) %>%
  summarise(n = n(),
            over10 = sum(over10)) %>%
  ggplot(aes(x=year,y=over10)) +
  geom_col() +
  labs(y="", x="Year",
       title = "Number of countries with inflation 10%+",
       caption = "Data: WDI")

Code

D %>% 
  filter(year %in% c(2010:2022)) %>%
  mutate(over10 = ifelse(inflation > 10, 1, 0)) %>%
  group_by(year) %>%
  summarise(n = n(),
            prop = sum(over10)/n) %>% 
  ggplot(aes(x=year,
             y=prop)) +
  geom_col() +
  labs(y="", x="Year",
       title = "Proportion of countries where inflation exceeded 10%",
       caption = "Data: WDI")

There are some obvious problems with the x-axis here, so let’s solve that, plus a few minor issues:

Code

barplot_V2 <-  D %>% 
  filter(year %in% c(2010:2022)) %>%
  mutate(over10 = ifelse(inflation > 10, 1, 0)) %>%
  group_by(year) %>%
  summarise(n = n(),
            prop = sum(over10)/n) %>% 
  ggplot(aes(x=year,
             y=prop)) +
  geom_col() +
  labs(y="", x="Year",
       title = "Proportion of countries where inflation exceeded 10%",
       caption = "Data: WDI") +
  scale_x_continuous(breaks = c(2010:2022)) +
  scale_y_continuous(labels = scales::percent) +
  theme(text = element_text(size=15)) +
  theme_bw() 

barplot_V2

We improved the labels of both axes, and increased the font size.

The new version is reasonably good, but there are still unnecessary gridlines, right?

You could either use theme_classic() instead of theme_bw().

Code

barplot_V2 +
  theme_classic()

Or we can keep theme_bw() and make some adjustments:

Code

barplot_V2 +
  theme(panel.grid.minor.x = element_blank(),
        panel.grid.major.x = element_blank())

Code

D %>% 
  filter(!is.na(inflation)) %>%
  mutate(over10 = ifelse(inflation > 10, 1, 0)) %>%
  group_by(year) %>%
  ggplot(aes(x=year,
             y=over10)) +
  geom_col() +
  theme_bw() + 
  theme(text = element_text(size=14)) +
  labs(y="", x="Year",
       subtitle = "Number of countries where inflation exceeded 10%",
       caption = "Data: WDI.")

3.1.1 Zoom in on OECD countries and increase the time horizon

Code

D %>% filter(OECD==1) %>%
  filter(!is.na(inflation)) %>%
  mutate(over10 = ifelse(inflation > 10, 1, 0)) %>%
  group_by(year) %>%
  summarise(n = n(),
            prop = sum(over10)/n) %>% 
  ggplot(aes(x=year,
             y=prop)) +
  geom_col() +
  theme_bw() + 
  theme(text = element_text(size=15)) +
  scale_y_continuous(labels = scales::percent) +
  labs(y="", x="Year",
       title = "Proportion of countries where inflation exceeded 10%\n(OECD countries only)")

Code

D %>% 
  mutate(over5 = ifelse(inflation > 5, 1, 0)) %>%
  group_by(year) %>%
  summarise(n = n(),
            prop = sum(over5)/n) %>% 
  ggplot(aes(x=year,
             y=prop)) +
  geom_line() + geom_point() +
  theme_bw() + 
  theme(text = element_text(size=15)) +
  scale_y_continuous(labels = scales::percent) +
  labs(y="", x="Year",
       subtitle = "Global proportion of countries where inflation exceeded 5%",
       caption = "Data: WDI.")

Code

D %>% filter(OECD==1) %>%
  filter(!is.na(inflation)) %>%
  group_by(year) %>%
  summarise(M = median(inflation)) %>%
  ggplot(aes(x=year,
             y=M)) +
  # geom_point() + geom_line() +
  geom_col() +
  theme_bw() + 
  theme(text = element_text(size=15)) +
  labs(y="", x="Year",
       title = "Median inflation in OECD countries")

3.2 Analysis of stock returns

Get data on stocks prices:

Code

library(tidyquant)
stocks <- tq_get(c("NVDA", "AVGO","^GSPC"),
                 from = "2022-01-15",
                 to = "2024-06-05")

Code

stocks %>%
 ggplot(aes(x = date, y = adjusted, color = symbol)) +
 geom_line() +
 theme_minimal() +
 labs(title = "NVIDIA vs Broadcom Stock Prices",
      y = "Adjusted Price",
      color = "Stock")

This is not informative (we can only see what’s going on for one of the series).

How about this?

Code

stocks %>%
 ggplot(aes(x = date, y = adjusted, color = symbol)) +
 geom_line() +
 facet_grid( ~ symbol)

Or perhaps this?

Code

stocks %>%
 ggplot(aes(x = date, y = adjusted, color = symbol)) +
 geom_line() +
 facet_wrap( ~ symbol,scales="free_y")

Slightly better, but do we really care about price? Price movements - returns and losses - are probably more important to investors.

So, calculate returns

Code

returns <- stocks %>%
 group_by(symbol) %>%
 tq_transmute(select = adjusted,
              mutate_fun = periodReturn,
              period = "daily",
              col_rename = "ret") %>%
 ungroup() %>%
 pivot_wider(names_from = symbol,
             values_from = ret) %>%
 na.omit()

Calculate rolling correlations:

Code

returns$roll_cor <- zoo::rollapply(data = returns[, c("NVDA", "AVGO")],
                              width = 30,
                              FUN = function(x) cor(x[,1], x[,2]),
                              by.column = FALSE,
                              align = "right",
                              fill = NA)

returns$roll_corNVSP <- zoo::rollapply(data = returns[, c("NVDA", "^GSPC")],
                                   width = 60,
                                   FUN = function(x) cor(x[,1], x[,2]),
                                   by.column = FALSE,
                                   align = "right",
                                   fill = NA)

30-Day Rolling Correlation of Daily Returns: NVIDIA vs Broadcom:

Code

returns %>%
 filter(date >= "2022-04-01") %>%
ggplot(aes(x = date, y = roll_cor)) +
 geom_line(color = "blue") +
 geom_hline(yintercept = 0, linetype = "dashed", color = "red") +
 theme_minimal() +
 labs(subtitle = "30-Day Rolling Correlation of Daily Returns: NVIDIA vs Broadcom",
      title = "Do semiconductor industry giants move together?",
      y = "Correlation Coefficient",
      x = "Date") +
 scale_y_continuous(limits = c(-.5, 1))

Code

returns %>%
 filter(date >= "2022-04-01") %>%
 ggplot(aes(x = date, y = roll_corNVSP)) +
 geom_line(color = "blue") +
 geom_hline(yintercept = 0, linetype = "dashed", color = "red") +
 theme_minimal() +
 labs(subtitle = "60-Day Rolling Correlation of Daily Returns: NVIDIA vs SP500",
      title = "Do semiconductor industry giants move together?",
      y = "Correlation Coefficient",
      x = "Date") +
 scale_y_continuous(limits = c(-.5, 1))

Scatterplots of returns

Code

ggplot(returns, aes(x = NVDA, y = AVGO)) +
 geom_point() +
 geom_smooth(method = "lm") +
 labs(title = "Scatterplot of Daily Returns",
      x = "NVIDIA",
      y = "Broadcom") +
 geom_abline(intercept = 0, slope = 1, 
             color = "red3",linetype=2,linewidth=1)

Code

ggplot(returns, aes(x = NVDA, y = AVGO)) +
 geom_point() +
 labs(title = "Scatterplot of Daily Returns",
      x = "NVIDIA",
      y = "Broadcom")

Code

ggplot(returns, aes(x = `^GSPC`, y = AVGO)) +
 geom_point() +
 labs(title = "Scatterplot of Daily Returns",
      x = "S&P500",
      y = "Broadcom")

Code

ggplot(returns, aes(x = `^GSPC`, y = NVDA)) +
 geom_point() +
 labs(title = "Scatterplot of Daily Returns",
      x = "S&P500",
      y = "NVIDIA")

Let’s also look at correlation during different market conditions

Code

returns %>%
 mutate(year = year(date)) %>%
 group_by(year) %>%
 summarize(correlation = cor(NVDA, AVGO)) %>%
 print()

# A tibble: 4 x 2
   year correlation
  <dbl>       <dbl>
1  2022       0.828
2  2023       0.532
3  2024       0.568
4  2025       0.796

Code

returns %>% mutate(ym = floor_date(date,"month")) %>%
 group_by(ym) %>%
 summarize(correlation = cor(NVDA, `^GSPC`)) %>%
 ggplot(aes(x = ym, y = correlation)) +
 geom_col()

--- output: html_document opts_chunk: message: FALSE warning: FALSE editor_options: chunk_output_type: console --- # Economic data over time ```{r} library(tidyverse) D <- read_csv("data/macro/inflation_WDI.csv") oecd <- read_csv("data/macro/oced_codes.csv") #biggerText <- jzPack::biggerText # Merge in OECD indicators D <- left_join(D,oecd) ``` ## Analysis of inflation patterns In how many countries has inflation exceeded 10% in 2022: ```{r} D %>% filter(year == 2022) %>% mutate(over10 = ifelse(inflation > 10, 1, 0)) %>% count(over10) ``` ```{r} D %>% filter(year %in% c(2010:2022)) %>% mutate(over10 = ifelse(inflation > 10, 1, 0)) %>% group_by(year) %>% summarise(n = n(), over10 = sum(over10)) %>% ggplot(aes(x=year,y=over10)) + geom_col() + labs(y="", x="Year", title = "Number of countries with inflation 10%+", caption = "Data: WDI") ``` ```{r} D %>% filter(year %in% c(2010:2022)) %>% mutate(over10 = ifelse(inflation > 10, 1, 0)) %>% group_by(year) %>% summarise(n = n(), prop = sum(over10)/n) %>% ggplot(aes(x=year, y=prop)) + geom_col() + labs(y="", x="Year", title = "Proportion of countries where inflation exceeded 10%", caption = "Data: WDI") ``` There are some obvious problems with the x-axis here, so let's solve that, plus a few minor issues: ```{r} barplot_V2 <- D %>% filter(year %in% c(2010:2022)) %>% mutate(over10 = ifelse(inflation > 10, 1, 0)) %>% group_by(year) %>% summarise(n = n(), prop = sum(over10)/n) %>% ggplot(aes(x=year, y=prop)) + geom_col() + labs(y="", x="Year", title = "Proportion of countries where inflation exceeded 10%", caption = "Data: WDI") + scale_x_continuous(breaks = c(2010:2022)) + scale_y_continuous(labels = scales::percent) + theme(text = element_text(size=15)) + theme_bw() barplot_V2 ``` We improved the labels of both axes, and increased the font size. The new version is reasonably good, but there are still unnecessary gridlines, right? You could either use `theme_classic()` instead of `theme_bw()`. ```{r} barplot_V2 + theme_classic() ``` Or we can keep `theme_bw()` and make some adjustments: ```{r} barplot_V2 + theme(panel.grid.minor.x = element_blank(), panel.grid.major.x = element_blank()) ``` ```{r} D %>% filter(!is.na(inflation)) %>% mutate(over10 = ifelse(inflation > 10, 1, 0)) %>% group_by(year) %>% ggplot(aes(x=year, y=over10)) + geom_col() + theme_bw() + theme(text = element_text(size=14)) + labs(y="", x="Year", subtitle = "Number of countries where inflation exceeded 10%", caption = "Data: WDI.") ``` ------------------------------------------------------------------------ ### Zoom in on OECD countries and increase the time horizon ```{r} D %>% filter(OECD==1) %>% filter(!is.na(inflation)) %>% mutate(over10 = ifelse(inflation > 10, 1, 0)) %>% group_by(year) %>% summarise(n = n(), prop = sum(over10)/n) %>% ggplot(aes(x=year, y=prop)) + geom_col() + theme_bw() + theme(text = element_text(size=15)) + scale_y_continuous(labels = scales::percent) + labs(y="", x="Year", title = "Proportion of countries where inflation exceeded 10%\n(OECD countries only)") ``` ```{r} D %>% mutate(over5 = ifelse(inflation > 5, 1, 0)) %>% group_by(year) %>% summarise(n = n(), prop = sum(over5)/n) %>% ggplot(aes(x=year, y=prop)) + geom_line() + geom_point() + theme_bw() + theme(text = element_text(size=15)) + scale_y_continuous(labels = scales::percent) + labs(y="", x="Year", subtitle = "Global proportion of countries where inflation exceeded 5%", caption = "Data: WDI.") ``` ```{r} D %>% filter(OECD==1) %>% filter(!is.na(inflation)) %>% group_by(year) %>% summarise(M = median(inflation)) %>% ggplot(aes(x=year, y=M)) + # geom_point() + geom_line() + geom_col() + theme_bw() + theme(text = element_text(size=15)) + labs(y="", x="Year", title = "Median inflation in OECD countries") ``` ## Analysis of stock returns Get data on stocks prices: ```{r} #| eval: false #| echo: true library(tidyquant) stocks <- tq_get(c("NVDA", "AVGO","^GSPC"), from = "2022-01-15", to = "2024-06-05") ``` ```{r} #| eval: true #| echo: false library(tidyverse) library(tidyquant) stocks <- read_csv("data/stocks2022-25.csv") ``` ```{r} stocks %>% ggplot(aes(x = date, y = adjusted, color = symbol)) + geom_line() + theme_minimal() + labs(title = "NVIDIA vs Broadcom Stock Prices", y = "Adjusted Price", color = "Stock") ``` This is not informative (we can only see what's going on for one of the series). How about this? ```{r} stocks %>% ggplot(aes(x = date, y = adjusted, color = symbol)) + geom_line() + facet_grid( ~ symbol) ``` Or perhaps this? ```{r} stocks %>% ggplot(aes(x = date, y = adjusted, color = symbol)) + geom_line() + facet_wrap( ~ symbol,scales="free_y") ``` Slightly better, but do we really care about price? Price movements - returns and losses - are probably more important to investors. So, calculate returns ```{r} returns <- stocks %>% group_by(symbol) %>% tq_transmute(select = adjusted, mutate_fun = periodReturn, period = "daily", col_rename = "ret") %>% ungroup() %>% pivot_wider(names_from = symbol, values_from = ret) %>% na.omit() ``` Calculate rolling correlations: ```{r} returns$roll_cor <- zoo::rollapply(data = returns[, c("NVDA", "AVGO")], width = 30, FUN = function(x) cor(x[,1], x[,2]), by.column = FALSE, align = "right", fill = NA) returns$roll_corNVSP <- zoo::rollapply(data = returns[, c("NVDA", "^GSPC")], width = 60, FUN = function(x) cor(x[,1], x[,2]), by.column = FALSE, align = "right", fill = NA) ``` 30-Day Rolling Correlation of Daily Returns: NVIDIA vs Broadcom: ```{r} returns %>% filter(date >= "2022-04-01") %>% ggplot(aes(x = date, y = roll_cor)) + geom_line(color = "blue") + geom_hline(yintercept = 0, linetype = "dashed", color = "red") + theme_minimal() + labs(subtitle = "30-Day Rolling Correlation of Daily Returns: NVIDIA vs Broadcom", title = "Do semiconductor industry giants move together?", y = "Correlation Coefficient", x = "Date") + scale_y_continuous(limits = c(-.5, 1)) ``` ```{r} returns %>% filter(date >= "2022-04-01") %>% ggplot(aes(x = date, y = roll_corNVSP)) + geom_line(color = "blue") + geom_hline(yintercept = 0, linetype = "dashed", color = "red") + theme_minimal() + labs(subtitle = "60-Day Rolling Correlation of Daily Returns: NVIDIA vs SP500", title = "Do semiconductor industry giants move together?", y = "Correlation Coefficient", x = "Date") + scale_y_continuous(limits = c(-.5, 1)) ``` Scatterplots of returns ```{r} ggplot(returns, aes(x = NVDA, y = AVGO)) + geom_point() + geom_smooth(method = "lm") + labs(title = "Scatterplot of Daily Returns", x = "NVIDIA", y = "Broadcom") + geom_abline(intercept = 0, slope = 1, color = "red3",linetype=2,linewidth=1) ``` ```{r} ggplot(returns, aes(x = NVDA, y = AVGO)) + geom_point() + labs(title = "Scatterplot of Daily Returns", x = "NVIDIA", y = "Broadcom") ``` ```{r} ggplot(returns, aes(x = `^GSPC`, y = AVGO)) + geom_point() + labs(title = "Scatterplot of Daily Returns", x = "S&P500", y = "Broadcom") ``` ```{r} ggplot(returns, aes(x = `^GSPC`, y = NVDA)) + geom_point() + labs(title = "Scatterplot of Daily Returns", x = "S&P500", y = "NVIDIA") ``` Let's also look at correlation during different market conditions ```{r} returns %>% mutate(year = year(date)) %>% group_by(year) %>% summarize(correlation = cor(NVDA, AVGO)) %>% print() ``` ```{r} returns %>% mutate(ym = floor_date(date,"month")) %>% group_by(ym) %>% summarize(correlation = cor(NVDA, `^GSPC`)) %>% ggplot(aes(x = ym, y = correlation)) + geom_col() ```