L1, L5, L10

#L1 Intro

library(quantmod)
library(xts)
library(zoo)
library(dplyr)

Define the date range for Beginning of Term (BoT) and End of Term (EoT)

start_date <- as.Date("2023-05-01")
end_date <- as.Date("2023-09-30")

Initialize the empty data frame

table_data <- data.frame(
    Category = character(),
    Beginning_of_Term = character(),
    End_of_Term = character(),
    Value_Beg = character(),
    Value_End = character(),
    stringsAsFactors = FALSE
)

Function to add a row to the table_data with error handling

add_row <- function(category, value_beg, value_end = "") {
    if (!is.null(value_beg) && !is.na(value_beg) && value_beg != "") {
        value_beg_formatted <- format(value_beg, scientific = FALSE, trim = TRUE)
        value_end_formatted <- format(value_end, scientific = FALSE, trim = TRUE)
        
        new_row <- data.frame(
            Category = category,
            Beginning_of_Term = format(start_date, "%d %B %Y"),
            End_of_Term = format(end_date, "%d %B %Y"),
            Value_Beg = value_beg_formatted,
            Value_End = value_end_formatted,
            stringsAsFactors = FALSE
        )
        table_data <<- rbind(table_data, new_row)  # Update the global table_data
    } else {
        cat("Warning: Data for", category, "is missing or incomplete.\n")
    }
}

Function to fetch data with error handling

get_symbol_data <- function(symbol, src, from, to) {
    tryCatch({
        data <- getSymbols(symbol, src = src, from = from, to = to, auto.assign = FALSE, warnings = FALSE)
        if (is.null(data) || nrow(data) == 0) {
            warning(paste("No data returned for symbol:", symbol))
            return(NULL)
        }
        return(data)
    }, error = function(e) {
        warning(paste("Error fetching data for symbol:", symbol, "-", e$message))
        return(NULL)
    })
}

Function to update data and add rows

update_data <- function() { ### Fetch S&P 500 and Nasdaq data

    sp500_data <- get_symbol_data("^GSPC", "yahoo", start_date, end_date)

## Warning in value[[3L]](cond): Error fetching data for symbol: ^GSPC - could not
## find function "getSymbols"

    nasdaq_data <- get_symbol_data("^IXIC", "yahoo", start_date, end_date)

## Warning in value[[3L]](cond): Error fetching data for symbol: ^IXIC - could not
## find function "getSymbols"

    if (!is.null(sp500_data)) {
        sp500_open <- round(as.numeric(first(Op(sp500_data))), 2)
        sp500_close <- round(as.numeric(last(Cl(sp500_data))), 2)
        add_row("S&P 500 Index Level", sp500_open, sp500_close)
    }

    if (!is.null(nasdaq_data)) {
        nasdaq_open <- round(as.numeric(first(Op(nasdaq_data))), 2)
        nasdaq_close <- round(as.numeric(last(Cl(nasdaq_data))), 2)
        add_row("Nasdaq Composite Index Level", nasdaq_open, nasdaq_close)
    }
    prime_rate <- get_symbol_data("DPRIME", "FRED", start_date, end_date)

## Warning in value[[3L]](cond): Error fetching data for symbol: DPRIME - could
## not find function "getSymbols"

    fed_funds_rate <- get_symbol_data("FEDFUNDS", "FRED", start_date, end_date)

## Warning in value[[3L]](cond): Error fetching data for symbol: FEDFUNDS - could
## not find function "getSymbols"

    commercial_paper_rate <- get_symbol_data("DCPN3M", "FRED", start_date, end_date)  # 3-Month Nonfinancial Commercial Paper Rate

## Warning in value[[3L]](cond): Error fetching data for symbol: DCPN3M - could
## not find function "getSymbols"

    cd_rate <- get_symbol_data("TB3MS", "FRED", start_date, end_date)  # 3-Month Treasury Bill Secondary Market Rate

## Warning in value[[3L]](cond): Error fetching data for symbol: TB3MS - could not
## find function "getSymbols"

    tbill_13wk <- get_symbol_data("DTB3", "FRED", start_date, end_date)

## Warning in value[[3L]](cond): Error fetching data for symbol: DTB3 - could not
## find function "getSymbols"

    tbill_26wk <- get_symbol_data("DTB6", "FRED", start_date, end_date)

## Warning in value[[3L]](cond): Error fetching data for symbol: DTB6 - could not
## find function "getSymbols"

Extract and add interest rate data

    if (!is.null(prime_rate)) {
        add_row("Prime Rate", round(as.numeric(first(prime_rate)), 2), round(as.numeric(last(prime_rate)), 2))
    }
    if (!is.null(fed_funds_rate)) {
        add_row("Federal Funds Rate", round(as.numeric(first(fed_funds_rate)), 2), round(as.numeric(last(fed_funds_rate)), 2))
    }
    if (!is.null(commercial_paper_rate)) {
        add_row("Commercial Paper Rate (90 days)", round(as.numeric(first(commercial_paper_rate)), 2), round(as.numeric(last(commercial_paper_rate)), 2))
    }
    if (!is.null(cd_rate)) {
        add_row("Certificate of Deposit Rate (3-month)", round(as.numeric(first(cd_rate)), 2), round(as.numeric(last(cd_rate)), 2))
    }
    if (!is.null(tbill_13wk)) {
        add_row("Treasury Bill Rate (13 weeks)", round(as.numeric(first(tbill_13wk)), 2), round(as.numeric(last(tbill_13wk)), 2))
    }
    if (!is.null(tbill_26wk)) {
        add_row("Treasury Bill Rate (26 weeks)", round(as.numeric(first(tbill_26wk)), 2), round(as.numeric(last(tbill_26wk)), 2))
    }

Fetch bond yield data from FRED

    treasury_long_term_yield <- get_symbol_data("GS10", "FRED", start_date, end_date)

## Warning in value[[3L]](cond): Error fetching data for symbol: GS10 - could not
## find function "getSymbols"

    corporate_bond_yield <- get_symbol_data("BAA", "FRED", start_date, end_date)

## Warning in value[[3L]](cond): Error fetching data for symbol: BAA - could not
## find function "getSymbols"

    high_yield_bond_yield <- get_symbol_data("BAMLH0A0HYM2EY", "FRED", start_date, end_date)

## Warning in value[[3L]](cond): Error fetching data for symbol: BAMLH0A0HYM2EY -
## could not find function "getSymbols"

Extract and add bond yield data

    if (!is.null(treasury_long_term_yield)) {
        add_row("Treasury Long-term Bond Yield", round(as.numeric(first(treasury_long_term_yield)), 2), round(as.numeric(last(treasury_long_term_yield)), 2))
    }
    if (!is.null(corporate_bond_yield)) {
        add_row("Corporate (Master) Bond Yield", round(as.numeric(first(corporate_bond_yield)), 2), round(as.numeric(last(corporate_bond_yield)), 2))
    }
    if (!is.null(high_yield_bond_yield)) {
        add_row("High-yield Corporate Bond Yield", round(as.numeric(first(high_yield_bond_yield)), 2), round(as.numeric(last(high_yield_bond_yield)), 2))
    }

Fetch exchange rates

    gbp_usd <- get_symbol_data("GBPUSD=X", "yahoo", start_date, end_date)

## Warning in value[[3L]](cond): Error fetching data for symbol: GBPUSD=X - could
## not find function "getSymbols"

    jpy_usd <- get_symbol_data("JPYUSD=X", "yahoo", start_date, end_date)

## Warning in value[[3L]](cond): Error fetching data for symbol: JPYUSD=X - could
## not find function "getSymbols"

    mxn_usd <- get_symbol_data("MXNUSD=X", "yahoo", start_date, end_date)

## Warning in value[[3L]](cond): Error fetching data for symbol: MXNUSD=X - could
## not find function "getSymbols"

Extract and add exchange rate data

   if (!is.null(gbp_usd)) {
        gbp_beg <- round(as.numeric(first(Cl(gbp_usd))), 4)
        gbp_end <- round(as.numeric(last(Cl(gbp_usd))), 4)
        add_row("Exchange Rate of the British Pound (in $)", gbp_beg, gbp_end)
    }
    if (!is.null(jpy_usd)) {
        jpy_beg <- round(as.numeric(first(Cl(jpy_usd))), 6)
        jpy_end <- round(as.numeric(last(Cl(jpy_usd))), 6)
        add_row("Exchange Rate of the Japanese Yen (in $)", jpy_beg, jpy_end)
    }
    if (!is.null(mxn_usd)) {
        mxn_beg <- round(as.numeric(first(Cl(mxn_usd))), 6)
        mxn_end <- round(as.numeric(last(Cl(mxn_usd))), 6)
        add_row("Exchange Rate of the Mexican Peso (in $)", mxn_beg, mxn_end)
    }

Fetch futures prices

Treasury Bond Futures (Symbol: ZB=F)

  treasury_futures <- get_symbol_data("ZB=F", "yahoo", start_date, end_date)

## Warning in value[[3L]](cond): Error fetching data for symbol: ZB=F - could not
## find function "getSymbols"

    if (!is.null(treasury_futures)) {
        treasury_beg <- round(as.numeric(first(Cl(treasury_futures))), 2)
        treasury_end <- round(as.numeric(last(Cl(treasury_futures))), 2)
        add_row("Treasury Bond Futures", treasury_beg, treasury_end)
    }

S&P 500 Index Futures (Symbol: ES=F)

    sp500_futures <- get_symbol_data("ES=F", "yahoo", start_date, end_date)

## Warning in value[[3L]](cond): Error fetching data for symbol: ES=F - could not
## find function "getSymbols"

    if (!is.null(sp500_futures)) {
        sp500_fut_beg <- round(as.numeric(first(Cl(sp500_futures))), 2)
        sp500_fut_end <- round(as.numeric(last(Cl(sp500_futures))), 2)
        add_row("S&P 500 Index Futures", sp500_fut_beg, sp500_fut_end)
    }

British Pound Futures (Symbol: 6B=F)

 bp_futures <- get_symbol_data("6B=F", "yahoo", start_date, end_date)

## Warning in value[[3L]](cond): Error fetching data for symbol: 6B=F - could not
## find function "getSymbols"

    if (!is.null(bp_futures)) {
        bp_fut_beg <- round(as.numeric(first(Cl(bp_futures))), 4)
        bp_fut_end <- round(as.numeric(last(Cl(bp_futures))), 4)
        add_row("British Pound Futures", bp_fut_beg, bp_fut_end)
    }

Options Data

Since programmatic access to options data is limited, we’ll manually input the data.

Call Option on a Firm (e.g., Apple Inc. - AAPL)

    call_option_firm <- "AAPL"
    call_option_expiration <- "2024-12-20"
    call_option_strike <- 150  # Example strike price
    call_option_premium <- 10   # Example premium

Get current stock price

    firm_data <- get_symbol_data(call_option_firm, "yahoo", Sys.Date() - 10, Sys.Date() - 1)

## Warning in value[[3L]](cond): Error fetching data for symbol: AAPL - could not
## find function "getSymbols"

    if (!is.null(firm_data)) {
        current_price <- round(as.numeric(last(Cl(firm_data))), 2)
    } else {
        current_price <- "N/A"
    }

Add rows

    add_row(paste("Call Option on", call_option_firm, "- Stock Price"), current_price)
    add_row(paste("Call Option on", call_option_firm, "- Option Premium"), call_option_premium)
    add_row(paste("Call Option on", call_option_firm, "- Strike Price"), call_option_strike)
    add_row(paste("Call Option on", call_option_firm, "- Expiration"), call_option_expiration)

Put Option on a Firm (e.g., Intel Corporation - INTC)

put_option_firm <- "INTC"
    put_option_expiration <- "2024-12-20"
    put_option_strike <- 50
    put_option_premium <- 0.0005

Get current stock price

    firm_data <- get_symbol_data(put_option_firm, "yahoo", Sys.Date() - 10, Sys.Date() - 1)

## Warning in value[[3L]](cond): Error fetching data for symbol: INTC - could not
## find function "getSymbols"

    if (!is.null(firm_data)) {
        current_price <- round(as.numeric(last(Cl(firm_data))), 2)
    } else {
        current_price <- "N/A"
    }

Add rows

    add_row(paste("Put Option on", put_option_firm, "- Stock Price"), current_price)
    add_row(paste("Put Option on", put_option_firm, "- Option Premium"), put_option_premium)
    add_row(paste("Put Option on", put_option_firm, "- Strike Price"), put_option_strike)
    add_row(paste("Put Option on", put_option_firm, "- Expiration"), put_option_expiration)

Currency Options Data

Call Option on a Foreign Currency (Euro)

    currency_call = "Euro (EURUSD)"
    currency_call_expiration = "2024-12-20"
    currency_call_strike = 1.20
    currency_call_premium = 0.05

Get current exchange rate for EURUSD

    eurusd_data <- get_symbol_data("EURUSD=X", "yahoo", Sys.Date() - 10, Sys.Date() - 1)

## Warning in value[[3L]](cond): Error fetching data for symbol: EURUSD=X - could
## not find function "getSymbols"

    if (!is.null(eurusd_data)) {
        eurusd_price <- round(as.numeric(last(Cl(eurusd_data))), 4)
    } else {
        eurusd_price <- "N/A"
    }

Add rows

    add_row(paste("Call Option on", currency_call, "- Currency Value"), eurusd_price)
    add_row(paste("Call Option on", currency_call, "- Option Premium"), currency_call_premium)
    add_row(paste("Call Option on", currency_call, "- Strike Price"), currency_call_strike)
    add_row(paste("Call Option on", currency_call, "- Expiration"), currency_call_expiration)

Put Option on a Foreign Currency (Japanese Yen)

    currency_put = "Japanese Yen (JPYUSD)"
    currency_put_expiration = "2024-12-20"
    currency_put_strike = 0.0090
    currency_put_premium = 0.0005

Get current exchange rate for JPYUSD

    jpyusd_data <- get_symbol_data("JPYUSD=X", "yahoo", Sys.Date() - 10, Sys.Date() - 1)

## Warning in value[[3L]](cond): Error fetching data for symbol: JPYUSD=X - could
## not find function "getSymbols"

    if (!is.null(jpyusd_data)) {
        jpyusd_price <- round(as.numeric(last(Cl(jpyusd_data))), 6)
    } else {
        jpyusd_price <- "N/A"
    }

Add rows

 add_row(paste("Put Option on", currency_put, "- Currency Value"), jpyusd_price)
    add_row(paste("Put Option on", currency_put, "- Option Premium"), currency_put_premium)
    add_row(paste("Put Option on", currency_put, "- Strike Price"), currency_put_strike)
    add_row(paste("Put Option on", currency_put, "- Expiration"), currency_put_expiration)

Display the data frame with proper formatting

print(table_data, right = FALSE)

##    Category                                             Beginning_of_Term
## 1  Call Option on AAPL - Stock Price                    01 May 2023      
## 2  Call Option on AAPL - Option Premium                 01 May 2023      
## 3  Call Option on AAPL - Strike Price                   01 May 2023      
## 4  Call Option on AAPL - Expiration                     01 May 2023      
## 5  Put Option on INTC - Stock Price                     01 May 2023      
## 6  Put Option on INTC - Option Premium                  01 May 2023      
## 7  Put Option on INTC - Strike Price                    01 May 2023      
## 8  Put Option on INTC - Expiration                      01 May 2023      
## 9  Call Option on Euro (EURUSD) - Currency Value        01 May 2023      
## 10 Call Option on Euro (EURUSD) - Option Premium        01 May 2023      
## 11 Call Option on Euro (EURUSD) - Strike Price          01 May 2023      
## 12 Call Option on Euro (EURUSD) - Expiration            01 May 2023      
## 13 Put Option on Japanese Yen (JPYUSD) - Currency Value 01 May 2023      
## 14 Put Option on Japanese Yen (JPYUSD) - Option Premium 01 May 2023      
## 15 Put Option on Japanese Yen (JPYUSD) - Strike Price   01 May 2023      
## 16 Put Option on Japanese Yen (JPYUSD) - Expiration     01 May 2023      
##    End_of_Term       Value_Beg  Value_End
## 1  30 September 2023 N/A                 
## 2  30 September 2023 10                  
## 3  30 September 2023 150                 
## 4  30 September 2023 2024-12-20          
## 5  30 September 2023 N/A                 
## 6  30 September 2023 0.0005              
## 7  30 September 2023 50                  
## 8  30 September 2023 2024-12-20          
## 9  30 September 2023 N/A                 
## 10 30 September 2023 0.05                
## 11 30 September 2023 1.2                 
## 12 30 September 2023 2024-12-20          
## 13 30 September 2023 N/A                 
## 14 30 September 2023 0.0005              
## 15 30 September 2023 0.009               
## 16 30 September 2023 2024-12-20

#L.5 Financial statements and fundamental analysis

Load necessary libraries

library(readxl)
library(edgar)
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following object is masked _by_ '.GlobalEnv':
## 
##     add_row

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(tidyr)
library(finreportr)

We need to give out mail to edgar in order to use it’s data in rpubs studio

options(HTTPUserAgent = "igordros@icloud.com")

In order to download Microsoft annual report, you need to manually download the file from EDGAR sec. Microsofts ticker is MSFT and CIK number is 0000789019. Then you put it into Rpubs.

Load the financial report for Microsoft (Excel file with data)

Financial_Report_Microsoft <- read_excel(“Financial_Report.xlsx”)

Load the financial report data

Financial_Report <- read_excel(“Financial_Report.xlsx”)

View the financial report data in the Viewer

View(Financial_Report)

Download annual report for Bank of America (CIK: 0000070858) - example

BankofAmerica <- GetBalanceSheet('BAC', 2018)

L5.1: Text description about Bank of America’s funding structure

This Bank has the most of its funds because of firstly: interest bearing deposits, then non-interest bearing deposits, then interest bearing deposits (foreign), and lastly non-interest bearing deposits (foreign)

L5.2: Text description about Bank of America’s investments

Bank mainly invests in federal funds and securities. From Consolidated balance sheet from edgar sec (261,131 million of $ worth of sold securities)

L5.3: Bank’s stance on securities market

I believe that the bank already sells securities as we can see in ‘Notes to financial statements’ under ‘securities’ they offer various securities. And there are securities for sale so Bank of America does not want to enter the securities market, because it is already in it.

L5.4: Text description about insurances offered by Bank of America

Bank of America offers various types of insurances. You can find them in the Financial statement as well.

L5.5: Bank’s assets in loans and leases

Bank of America has a significant part of its assets in loans and leases which very often have variable rates. Bank earns on any interest rate rise. On the other side, bank has interest bearing deposits but they mostly have fixed rates. Therefore, I believe that Bank of America has a positive gap between rate-sensitive assets and liabilities.

L5.6: Total Assets, Total Interest Income, and Interest Income Percentage

All of the data are taken from official edgar sec page from 10-k Annual Financial statements.

total_assets <- 2354507000000  # 2,354,507 million USD
total_interest_income <- 66769000000  # 66,769 million USD
interest_income_percentage <- (total_interest_income / total_assets) * 100

Total Assets from ‘Consolidated Balance sheet’ Total interest income from ‘Consolidated Statement of Income’ Percentage = (66,769,000,000 / 2,354,507,000,000) * 100% = interest_income_percentage

L5.7: Total Interest Expense and Interest Expense Percentage

total_interest_expense <- 19337000000  # 19,337 million USD
interest_expense_percentage <- (total_interest_expense / total_assets) * 100

Total interest expense from ‘Consolidated Statement of Income’ Percentage = (19,337,000,000 / 2,354,507,000,000) * 100% = interest_expense_percentage

L5.8: Net Interest Income and Net Interest Margin

net_interest_income <- 47432000000  # 47,432 million USD
average_earning_assets <- (2354507000000 + 2281234000000) / 2  # Average of Total Assets for 2017 and 2018
net_interest_margin <- (net_interest_income / average_earning_assets) * 100

Net interest income from ‘Consolidated Statement of Income’ Average earning assets = (2,354,507,000,000 + 2,281,234,000,000) / 2 Net interest margin = (47,432,000,000 / 2,317,870,500,000) * 100% = net_interest_margin

L5.9: Non-interest Income and Expense Percentages

total_non_interest_income <- 43815000000  # 43,815 million USD
non_interest_income_percentage <- (total_non_interest_income / total_assets) * 100
total_non_interest_expense <- 53381000000  # 53,381 million USD
non_interest_expense_percentage <- (total_non_interest_expense / total_assets) * 100

Total non-interest income from ‘Consolidated Statement of Income’ Total non-interest expense from ‘Consolidated Statement of Income’ Percentage of income = (43,815,000,000 / 2,354,507,000,000) * 100% = non_interest_income_percentage Percentage of expense = (53,381,000,000 / 2,354,507,000,000) * 100% = non_interest_expense_percentage

L5.10: Provision for Credit Losses

Provision for credit losses = 3,282 million USD Total Assets = 2,354,507 million USD Percentage = (3,282,000,000 / 2,354,507,000,000) * 100% = 0.14%

L5.11: Net income calculation

Net income from ‘Consolidated Statement of Income’ = 28,147 million USD Total Assets from ‘Consolidated Balance Sheet’ = 2,354,507 million USD Percentage = (28,147,000,000 / 2,354,507,000,000) * 100% = 1.2%

L5.12: Net income as a percentage of shareholder’s equity

Net income = 28,147 million USD Total shareholder’s equity = 265,325 million USD Percentage = (28,147,000,000 / 265,325,000,000) * 100% = 10.61%

L5.13: Interest income impact

Interest income – positive impact, because bank would earn more on loans and investments.

L5.14: Provision for credit losses impact

Provision for credit losses – as the market condition worsens, bank’s risk on loans and credit losses increases, so the bank needs to increase its provision for losses on the loans which would decrease the demand for those services.

L5.15: S&P 500 mutual fund risk level

The S&P 500 mutual fund is a moderate risk investment due to its diversification across 500 large companies.

L5.16: S&P 500 return calculation for 2023

Return for year 2023 = 26.29% Average return from the last 3 years = (26.29 - 18.11 + 28.71) / 3 = 12.3%

L5.17: S&P 500 dependence on economic conditions

S&P 500 is heavily influenced by fluctuations of stocks of companies included in S&P 500. It is highly dependent on stock market conditions.

L5.18: Vanguard mutual fund fee structure

Vanguard charges $25 for each brokerage account and 0.25% to 1% of transaction value depending on the amount.

L5.19: Expense ratio calculation for Vanguard 500 index fund

The expense ratio is 0.04 = 4%. You can see this on the Vanguard website.

L5.20: Securities firm services

Securities firms offer a wide range of financial services including trading, investment banking, asset management, wealth management, etc. cat(“These companies serve individuals and companies by being an intermediary between buyer and seller on the financial market.

L5.21: JP Morgan’s Risk Factors

In their Financial statement in Part 1, Item 1A, Risk Factors, JP Morgan stated: Regulatory risks, including the impact that applicable laws, rules, and regulations in the highly-regulated and supervised financial services industry, as well as changes to or in the application, interpretation or enforcement of those laws, rules and regulations, can have on JPMorgan Chase’s business and operations.

L5.22: JP Morgan’s Performance in 2024

JP Morgan’s performance in 2024 has been extremely successful due to many factors such as: rising interest rates which increase their margin on loans; high activity in financial markets; good revenues from branches like wealth management or investment banking and strong economic growth.

#L.10 WACC

WACC (weighted average cost of capital) - can be measured at market values and accounting values. You cannot mix them up. They are completely different in a way for counting, so if you mix them up you will get incorrect result that may be misleading. At market values you need both equity and debt of company and the cost of equity and the cost of debt. This measure tells the investors the rate of return they might expect with how much risk they would bare on their investment.

Example:

library(quantmod)

## Loading required package: xts

## Loading required package: zoo

## 
## Attaching package: 'zoo'

## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric

## 
## ######################### Warning from 'xts' package ##########################
## #                                                                             #
## # The dplyr lag() function breaks how base R's lag() function is supposed to  #
## # work, which breaks lag(my_xts). Calls to lag(my_xts) that you type or       #
## # source() into this session won't work correctly.                            #
## #                                                                             #
## # Use stats::lag() to make sure you're not using dplyr::lag(), or you can add #
## # conflictRules('dplyr', exclude = 'lag') to your .Rprofile to stop           #
## # dplyr from breaking base R's lag() function.                                #
## #                                                                             #
## # Code in packages is not affected. It's protected by R's namespace mechanism #
## # Set `options(xts.warn_dplyr_breaks_lag = FALSE)` to suppress this warning.  #
## #                                                                             #
## ###############################################################################

## 
## Attaching package: 'xts'

## The following objects are masked from 'package:dplyr':
## 
##     first, last

## Loading required package: TTR

## Registered S3 method overwritten by 'quantmod':
##   method            from
##   as.zoo.data.frame zoo

Set User Agent (if necessary for your connection)

options(HTTPUserAgent = "igordros@icloud.com")

Load Microsoft stock data

getSymbols("MSFT") 

## [1] "MSFT"

Constants

outstanding_shares <- 30000000 # Number of outstanding shares
market_cap <- last(MSFT$MSFT.Adjusted) * outstanding_shares
cost_of_debt <- 0.03 # 3% 
tax_rate <- 0.25 # 25% 
beta <- 1.1 # Beta of Microsoft's stock 
risk_free_rate <- 0.02 # 2% risk-free rate 
market_risk_premium <- 0.06 # 6% market risk premium 
market_value_of_debt <- 100e9 # $100 billion 

Cost of Equity Calculation

cost_of_equity <- risk_free_rate + beta * market_risk_premium

Total Market Value

total_market_value <- market_cap + market_value_of_debt

Weights of Debt and Equity

weight_of_debt <- market_value_of_debt / total_market_value
weight_of_equity <- market_cap / total_market_value

WACC Calculation

wacc <- (weight_of_debt * cost_of_debt * (1 - tax_rate)) + (weight_of_equity * cost_of_equity)

Output WACC

cat("WACC for Microsoft:", wacc * 100, "%\n")

## WACC for Microsoft: 2.967816 %