RegisteredSecurities(): Download a full list of registered securities with ISIN and CFI codes

opendatauzb::RegisteredSecurities() collects a list of securities from the Central Securities Depository database.

RegisteredSecurities()

getSecurities(): Download the securities listed on the Republican Stock Exchange “Toshkent”

opendatauzb::getSecurities() provides a data frame with the list of securities from the Republican Stock Exchange “Toshkent” database

getSecurities()

## 5106 securities are found. Out of which are:

## # A tibble: 4 x 2
## # Groups:   Type [4]
##   Type      n
##   <chr> <int>
## 1 BND      11
## 2 FCT     492
## 3 RPO    4425
## 4 STK     178

ipo(): Obtain schedule of public offerings (IPO/SPO/PO (the government shares))

opendatauzb::ipo() returns a data frame with public offerings in last five years.

ipo()

currentBidsAsks(): Watch current Bids and Asks for stocks

opendatauzb::currentBidsAsks() returns current bid and ask prices on the Republican Stock Exchange “Toshkent” database.

currentBidsAsks()

Market Benchmark

getMarketIndex(sector = "all")

## Uzbekistan Composite Index (Sector: All) will be downloaded for 01.01.2020-12.09.2020

getMarketIndex(sector = "finance",
               from = "01.01.2018",
               to = "30.06.2020")

## Uzbekistan Composite Index (Sector: Finance) will be downloaded for 01.01.2018-30.06.2020

getTicker(): Load stock market prices for certain timeframe

opendatauzb::getTicker() obtains stock market prices by security code over the period given:

getTicker("UZ7011340005")

## New names:
## * `` -> ...9
## * `` -> ...10

## UZ7011340005 has been downloaded for 01.01.2020-12.09.2020

getTicker("UZ7011340005", 
          from = "01.01.2018", 
          to = "30.06.2020")

Creating a data frame with stock prices

For this example, we use opendatauzb package’s functions and other packages (quantmod, PerformanceAnalytics, tidyquant) for financial modelling to evaluate the given set of stocks and portfolio performance. we have chosen 20 stocks, including Kvarts, Trustbank, Hamkorbank, UzSQB, ToshkentVino and others.

Using getTicker function, we download stock prices for each of those stocks. Note: if arguments from and to are not provided, by default, both getMarketIndex() and getTicker() functions download stock prices starting from 01.01.2019.

asset_group <- c("UZ704532K019", "UZ7045320007", "UZ7025870005", "UZ7038380000", "UZ7025770007",
                 "UZ7015030008", "UZ7043200003", "UZ703756K015", "UZ7037560008", "UZ7016400002",
                 "UZ7004510002", "UZ7023760000", "UZ703348K011", "UZ7033480003", "UZ7035340007",
                 "UZ7028090007", "UZ701134K017", "UZ7011340005", "UZ701655K011", "UZ7016550004")

assets <- asset_group %>%
  lapply(getTicker, from = "01.01.2019", to = "12.08.2020") %>%
  bind_rows()

Having loaded stock prices, we calculate monthly returns using a tidyquant::tq_transmute() function from tidyquant package. Please refer to the link for more information on using tidyquant package.

As Uzbek Stock Market databases do not provide adjusted closing prices, for this example, we use closing prices. However, in real-world practice, we highly recommend calculating adjusted closing prices.

Ra <- assets %>% 
  group_by(symbol) %>%
  tq_transmute(select     = "Closed Price",
               mutate_fun = periodReturn,
               period     = "monthly",
               col_rename = "Ra")

The output presents only first six rows of calculated monthly returns.

Market Index (Uzbekistan Composite Index, UCI)

After collecting data for our stocks, we download overall market index as baseline prices for our analysis.

# Baseline prices
Rb <- getMarketIndex("all", from = "01.01.2019", to = "12.08.2020")  %>%
  tq_transmute(select     = price,
               mutate_fun = periodReturn,
               period     = "monthly",
               col_rename = "Rb")

## Uzbekistan Composite Index (Sector: All) will be downloaded for 01.01.2019-12.08.2020

Building the Capital Asset Pricing Model (CAPM)

To evaluate if the stock is valued fairly considering associated risks and expected returns, we employ CAPM model. We use tidyquant::tq_performance() function from tidyquant package.

RaRb_capm <- RaRb %>%
  tq_performance(Ra = Ra,
                 Rb = Rb,
                 Rf = 0.15,
                 performance_fun = table.CAPM)

Calculating Sharpe ratio for individual stocks

We estimate Sharpe ratio for all our stocks to see returns on investment per amount of taken risks. As a rule of thumb, the Sharpe ratio greater than 3 is classified as Excellent, an interval 2.00-2.99 is Very Good, 1.00-1.99 range is Acceptable, the ratio less than 1 is classified as Bad or Sub-optimal.

RaRb %>%
  tq_performance(Ra = Ra,
    Rb = NULL,
    performance_fun = SharpeRatio,
    Rf = 0.15) %>% 
  formattable(list("ESSharpe(Rf=15%,p=95%)" = formatter("span", 
                                                        style = x ~ ifelse(x >= 3, 
                                                                           style(color = "green", 
                                                                                 font.weight = "bold"), 
                                                                           NA))))

(Single) Portfolio performance

Based on the ratios we calculated above, we choose two stocks for our portfolio: UZ7015030008 (MSBU) and UZ7035340007 (UZKB).

wts <- c(rep(0, 5),
         0.35, #6
         rep(0, 8),
         0.65, #15
         rep(0, 5))

portfolio_returns_monthly <- Ra %>%
  tq_portfolio(assets_col   = symbol,
               returns_col  = Ra,
               weights      = wts,
               col_rename   = "Ra")

left_join(portfolio_returns_monthly, 
          Rb,
          by = c("Date"="date")) %>% 
  tq_performance(Ra = Ra, Rb = Rb, performance_fun = table.CAPM) %>% 
  gather() %>%
  kable() %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))

Portfolio growth

On condition that our initial assumptions hold, let us see how 100,000 UZS investment in these stocks would grow since 1 January 2019.

portfolio_growth_monthly <- Ra %>%
  tq_portfolio(assets_col   = symbol,
               returns_col  = Ra,
               weights      = wts,
               col_rename   = "investment.growth",
               wealth.index = TRUE) %>%
  mutate(investment.growth = investment.growth * 100000)

As the graph below shows, the initial investment of 100,000 UZS in these 2 stocks in 1 January 2019 is worth around 2.2M UZS as of 30 June 2020 without inflation adjustment.

Note: These estimates are for only illustrative purposes of the practical use of opendatauzb package. The real returns on investment may rely heavily on the assumptions drawn earlier and other exogenous shocks to Uzbek Stock Market.