Investments Costs

Investing is hard. People often

get_perf <- function(starting_cash = 1, 
                     perc_perf = 0.2, 
                     perc_manag = 0.02, 
                     manager_skill_down = 0.8,
                     manager_skill_up = 1.2) {
  
  require(BatchGetSymbols)
  
  df_sp500 <- BatchGetSymbols(tickers = '^GSPC', 
                              first.date = '1950-01-01', last.date = Sys.Date())[[2]]
  
  df_invest <- df_sp500 %>%
    mutate(ref_year = lubridate::year(ref.date)) %>%
    group_by(ref_year) %>%
    summarise(last_price = last(price.adjusted)) %>%
    mutate(ret = last_price/lag(last_price) - 1)
  
  df_out <- tibble()
  port_value <- starting_cash
  invest_value <- starting_cash
  for (i_year in df_invest$ref_year[2:nrow(df_invest)]) {
    
    idx <- which(i_year == df_invest$ref_year)
    ret_year <- df_invest$ret[idx]
    invest_value <- invest_value*(1 + ret_year)

    fund_return <- (ret_year>0)*manager_skill_up*ret_year + 
      (ret_year<=0)*manager_skill_down*ret_year
    
    bench_cost <- (fund_return > 0)*(fund_return - ret_year)*perc_perf*port_value
    management_cost <- port_value*perc_manag
    
    port_value <- port_value*(1+fund_return) - bench_cost - management_cost
    
    df_out <- bind_rows(df_out, tibble(i_year, 
                                       ret_investment = ret_year,
                                       fund_return,
                                       bench_cost,
                                       management_cost,
                                       fund_cost = bench_cost + management_cost,
                                       port_value, 
                                       invest_value))
    
  }
  
  df_tab <- tibble(perc_perf, 
                   perc_manag,
                   manager_skill_down,
                   manager_skill_up,
                   total_ret_asset = last(df_out$invest_value)/starting_cash-1,
                   total_fund_return = last(cumprod(1+df_out$fund_return)) - 1,
                   total_ret_portfolio = last(df_out$port_value)/starting_cash-1,
                   total_cash_fund_cost = sum(df_out$fund_cost),
                   total_cash_investor = last(port_value),
                   CAGR_asset = (1+total_ret_asset)^(1/nrow(df_out))-1,
                   CAGR_fund = (1+total_fund_return)^(1/nrow(df_out))-1,
                   CAGR_portfolio = (1+total_ret_portfolio)^(1/nrow(df_out))-1
                   )
                   
  return(df_tab)
  
}

perc_perf <- 0.2
perc_manag <- 0.02
bench <- 0.05
starting_cash <- 1000
manager_skill_down <- 0.5
manager_skill_up <- 1.5

df_tab <- get_perf(starting_cash = starting_cash , 
                   perc_perf = perc_perf, 
                   perc_manag = perc_manag, 
                   manager_skill_down = manager_skill_down,
                   manager_skill_up = manager_skill_up)
## Loading required package: BatchGetSymbols
## Loading required package: rvest
## Loading required package: xml2
## Loading required package: dplyr
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
## 
## 
## Running BatchGetSymbols for:
##    tickers =^GSPC
##    Downloading data for benchmark ticker
## ^GSPC | yahoo (1|1) | Found cache file
## ^GSPC | yahoo (1|1) | Found cache file - Got 100% of valid prices | Looking good!