knitr::opts_chunk$set( collapse = TRUE, echo = FALSE, message = FALSE, warning = FALSE, fig.width = 5, comment = "#>" )
library(MiscImport) library(tidyverse)
portfolio_df = import_boi_institutional_portolio_asset_class() corp_bonds_df = import_boi_corporate_bonds_holdings()
Summing up all the assets we can see the growth in assets under management of institutional investors
portfolio_df %>% group_by(date) %>% summarise(value = sum(value), .groups = "drop") %>% ggplot(aes(x = date, y = value)) + geom_line() + scale_y_continuous(labels = scales::comma_format(scale = 10 ^ -3)) + xlab(NULL) + ylab("ILS billions") + ggtitle("Assets under management of institutional investors")
portfolio_df %>% filter(date == max(date)) %>% group_by(investor_type) %>% summarise(value = sum(value), .groups = "drop") %>% mutate(market_share = value / sum(value)) %>% ggplot(aes(x = market_share, y = reorder(investor_type, market_share))) + geom_col() + scale_x_continuous(labels = scales::percent_format()) + xlab(NULL) + ylab(NULL) + ggtitle("Share of AUM by investor type")
portfolio_df %>% filter(date == max(date)) %>% filter(str_detect(asset_class, "corp")) %>% group_by(investor_type) %>% summarise(value = sum(value), .groups = "drop") %>% mutate(market_share = value / sum(value)) %>% ggplot(aes(x = market_share, y = reorder(investor_type, market_share))) + geom_col() + scale_x_continuous(labels = scales::percent_format()) + xlab(NULL) + ylab(NULL) + ggtitle("Share of corporate bonds AUM by investor type")
corp_bonds_df %>% filter(date == max(date)) %>% mutate(market_share = value / sum(value)) %>% ggplot(aes(x = market_share, y = reorder(investor_type, market_share))) + geom_col() + geom_text(aes(label = paste0(round(market_share * 100),"%")), hjust = -0.25) + scale_x_continuous(labels = scales::percent_format()) + xlab(NULL) + ylab(NULL) + ggtitle("Share of traded corporate bonds by investor type")
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