Package 'R4GoodPersonalFinances'

Description

Make optimal decisions for your personal or household finances. Use tools and methods that are selected carefully to align with academic consensus, bridging the gap between theoretical knowledge and practical application. They help you find your own personalized optimal discretionary spending or optimal asset allocation, and prepare you for retirement or financial independence. The optimal solution to this problems is extremely complex, and we only have a single lifetime to get it right. Fortunately, we now have the user-friendly tools implemented, that integrate life-cycle models with single-period net-worth mean-variance optimization models. Those tools can be used by anyone who wants to see what highly-personalized optimal decisions can look like. For more details see: Idzorek T., Kaplan P. (2024, ISBN:9781952927379), Haghani V., White J. (2023, ISBN:9781119747918).

Author(s)

Maintainer: Kamil Wais [email protected] (ORCID) [copyright holder, funder]

Authors:

• Olesia Wais [email protected] (ORCID)

See Also

Useful links:

• https://www.r4good.academy/

• https://r4goodacademy.github.io/R4GoodPersonalFinances/

• https://github.com/R4GoodAcademy/R4GoodPersonalFinances

• Report bugs at https://github.com/R4GoodAcademy/R4GoodPersonalFinances/issues

Description

Calculate Effective Tax Rate

Usage

calc_effective_tax_rate(portfolio, tax_rate_ltcg, tax_rate_ordinary_income)

Arguments

Value

A portfolio object augmented with nested columns with effective tax rates calculations.

Examples

portfolio <- create_portfolio_template()
 portfolio$accounts$taxable <- c(10000, 30000)
 portfolio <- 
   calc_effective_tax_rate(
     portfolio,
     tax_rate_ltcg = 0.20, 
     tax_rate_ordinary_income = 0.40
   )
 portfolio$aftertax$effective_tax_rate

Description

Calculating the Gompertz model parameters for joint survival

Usage

calc_gompertz_joint_parameters(
  p1 = list(age = NULL, mode = NULL, dispersion = NULL),
  p2 = list(age = NULL, mode = NULL, dispersion = NULL),
  max_age = 120
)

Arguments

Value

A list containing:

Examples

calc_gompertz_joint_parameters(
  p1 = list(
    age        = 65,
    mode       = 88,
    dispersion = 10.65
  ),
  p2 = list(
    age        = 60,
    mode       = 91,
    dispersion = 8.88
  ),
  max_age = 110
)

Description

Calculate Gompertz mode for a given life expectancy

Usage

calc_gompertz_mode(life_expectancy, current_age, dispersion, max_age = 120)

Arguments

Value

A numeric. Mode of the Gompertz distribution.

Examples

calc_gompertz_mode(
 life_expectancy = 86,
  current_age    = 25,
  dispersion     = 8.88,
  max_age        = 115
)

Description

Calculating Gompertz model parameters

Usage

calc_gompertz_parameters(
  mortality_rates,
  current_age,
  estimate_max_age = FALSE
)

Arguments

Value

A list containing:

References

Blanchet, David M., and Paul D. Kaplan. 2013. "Alpha, Beta, and Now... Gamma." Journal of Retirement 1 (2): 29-45. doi:10.3905/jor.2013.1.2.029.

Examples

mortality_rates <- 
  dplyr::filter(
    life_tables,
    country == "USA" & 
    sex     == "male" &
    year    == 2022
  )
  
calc_gompertz_parameters(
  mortality_rates = mortality_rates,
  current_age     = 65
)

Description

Calculating Gompertz survival probability

Usage

calc_gompertz_survival_probability(
  current_age,
  target_age,
  mode,
  dispersion,
  max_age = NULL
)

Arguments

Value

A numeric. The probability of survival from 'current_age' to 'target_age' based on the Gompertz distribution with the given parameters.

Examples

calc_gompertz_survival_probability(
  current_age = 65, 
  target_age  = 85, 
  mode        = 80, 
  dispersion  = 10
)

Description

Calculate Life Expectancy

Usage

calc_life_expectancy(current_age, mode, dispersion, max_age = 120)

Arguments

Value

A numeric. Total life expectancy in years.

Examples

calc_life_expectancy(
  current_age = 65, 
  mode        = 80, 
  dispersion  = 10
)

Description

Calculate optimal asset allocation

Usage

calc_optimal_asset_allocation(
  household,
  portfolio,
  current_date = get_current_date()
)

Arguments

Value

The portfolio with additional nested columns:

• allocations$optimal - optimal joint net-worth portfolio allocations

• allocations$current - current allocations

Examples

older_member <- HouseholdMember$new(
  name       = "older",  
  birth_date = "1980-02-15",
  mode       = 80,
  dispersion = 10
)  
household <- Household$new()
household$add_member(older_member)  

household$expected_income <- list(
  "income" = c(
    "members$older$age <= 65 ~ 7000 * 12"
  )
)
household$expected_spending <- list(
  "spending" = c(
    "TRUE ~ 5000 * 12"
  )
)

portfolio <- create_portfolio_template() 
portfolio$accounts$taxable <- c(10000, 30000)

portfolio <- 
  portfolio |> 
  calc_effective_tax_rate(
    tax_rate_ltcg = 0.20, 
    tax_rate_ordinary_income = 0.40
  )

portfolio <- 
  calc_optimal_asset_allocation(
   household = household,
   portfolio = portfolio,
   current_date = "2020-07-15"
  )

portfolio$allocations

Description

Calculates the optimal allocation to the risky asset using the Merton Share formula.

Usage

calc_optimal_risky_asset_allocation(
  risky_asset_return_mean,
  risky_asset_return_sd,
  safe_asset_return,
  risk_aversion
)

Arguments

Details

Can be used to calculate the optimal allocation to the risky asset for vectors of inputs.

Value

A numeric. The optimal allocation to the risky asset. In case of NaN() (because of division by zero) the optimal allocation to the risky asset is set to 0.

See Also

• How to Determine Our Optimal Asset Allocation?

• Haghani V., White J. (2023) "The Missing Billionaires: A Guide to Better Financial Decisions." ISBN:978-1-119-74791-8.

Examples

calc_optimal_risky_asset_allocation(
  risky_asset_return_mean = 0.05,
  risky_asset_return_sd   = 0.15,
  safe_asset_return       = 0.02,
  risk_aversion           = 2
)

calc_optimal_risky_asset_allocation(
  risky_asset_return_mean = c(0.05, 0.06),
  risky_asset_return_sd   = c(0.15, 0.16),
  safe_asset_return       = 0.02,
  risk_aversion           = 2
)

Description

Calculate Portfolio Parameters

Usage

calc_portfolio_parameters(portfolio)

Arguments

Value

A list with the following elements:

• value: The value of the portfolio.

• weights: The weights of assets in the portfolio.

• expected_return: The expected return of the portfolio.

• standard_deviation: The standard deviation of the portfolio.

Examples

portfolio <- create_portfolio_template()
 portfolio$accounts$taxable <- c(10000, 30000)
 calc_portfolio_parameters(portfolio)

Description

Calculates changes in purchasing power over time, taking into account the real interest rate.

Usage

calc_purchasing_power(x, years, real_interest_rate)

Arguments

Details

The real interest rate is the interest rate after inflation. If negative (e.g. equal to the average yearly inflation rate) it can show diminishing purchasing power over time. If positive, it can show increasing purchasing power over time, and effect of compounding interest on the purchasing power.

Value

A numeric. The purchasing power.

See Also

• How to Determine Our Optimal Asset Allocation?

Examples

calc_purchasing_power(x = 10, years = 30, real_interest_rate = -0.02)
calc_purchasing_power(x = 10, years = 30, real_interest_rate = 0.02)

Description

Calculating retirement ruin probability

Usage

calc_retirement_ruin(
  portfolio_return_mean,
  portfolio_return_sd,
  age,
  gompertz_mode,
  gompertz_dispersion,
  portfolio_value,
  monthly_spendings,
  yearly_spendings = 12 * monthly_spendings,
  spending_rate = yearly_spendings/portfolio_value
)

Arguments

Value

A numeric. The probability of retirement ruin (between 0 and 1), representing the likelihood of running out of money during retirement.

References

Milevsky, M.A. (2020). Retirement Income Recipes in R: From Ruin Probabilities to Intelligent Drawdowns. Use R! Series. doi:10.1007/978-3-030-51434-1.

Examples

calc_retirement_ruin(
  age                   = 65,
  gompertz_mode         = 88,
  gompertz_dispersion   = 10,
  portfolio_value       = 1000000,
  monthly_spendings     = 3000,  
  portfolio_return_mean = 0.02,
  portfolio_return_sd   = 0.15
)

Description

Calculates the risk adjusted return for portfolio of given allocation to the risky asset.

Usage

calc_risk_adjusted_return(
  safe_asset_return,
  risky_asset_return_mean,
  risky_asset_allocation,
  risky_asset_return_sd = NULL,
  risk_aversion = NULL
)

Arguments

Value

A numeric. The risk adjusted return.

See Also

• How to Determine Our Optimal Asset Allocation?

• Haghani V., White J. (2023) "The Missing Billionaires: A Guide to Better Financial Decisions." ISBN:978-1-119-74791-8.

Examples

calc_risk_adjusted_return(
  safe_asset_return = 0.02,
  risky_asset_return_mean = 0.04,
  risky_asset_return_sd = 0.15,
  risky_asset_allocation = 0.5,
  risk_aversion = 2
)

calc_risk_adjusted_return(
  safe_asset_return = 0.02,
  risky_asset_return_mean = 0.04,
  risky_asset_allocation = c(0.25, 0.5, 0.75),
  risky_asset_return_sd = 0.15,
  risk_aversion = 2
)

Description

Creates a template for default portfolio with two asset classes:

• GlobalStocksIndexFund

• InflationProtectedBonds

Usage

create_portfolio_template()

Details

The template is used as a starting point for creating a portfolio. The asset classes have some reasonable default values of expected returns and standard deviations of returns. The template assumes no correlations between asset classes in the correlations matrix. Please check and update the template assumptions if necessary.

The nested pretax columns contain default values for parameters needed for calculating effective tax rates. The template assumes only capital gains tax is paid. Please customise this template to your individual situation.

The accounts nested columns have zero values for all assets by default in both taxable and tax-advantaged accounts. The template assumes that there is currently no financial wealth allocated to those accounts. Please customise this template to your individual situation.

The weights nested columns define weights of assets in portfolios representative of the household human capital and liabilities. The template assumes equal weights for all assets for both portfolios. Please customise this template to your individual situation.

Value

A nested tibble of class 'Portfolio' with columns:

• name

• expected_return

• standard_deviation

• accounts

  • taxable

  • taxadvantaged

• weights

  • human_capital

  • liabilities

• correlations

• pretax

  • turnover

  • income_qualified

  • capital_gains_long_term

  • income

  • capital_gains

  • cost_basis

See Also

Possible sources of market assumptions:

• https://elmwealth.com/capital-market-assumptions/

• https://www.obligacjeskarbowe.pl/oferta-obligacji/obligacje-10-letnie-edo/

• https://www.msci.com/indexes/index/664204

• (PDF) https://research.ftserussell.com/Analytics/FactSheets/Home/DownloadSingleIssue?issueName=AWORLDS&isManual=False

Examples

portfolio <- create_portfolio_template()
  portfolio$accounts$taxable <- c(10000, 30000)
  portfolio

Description

Wrapper functions for printing nicely formatted values.

Usage

format_currency(
  x,
  prefix = "",
  suffix = "",
  big.mark = ",",
  accuracy = NULL,
  min_length = NULL,
  ...
)

format_percent(x, accuracy = 0.1, ...)

Arguments

Value

A character. Formatted value.

A character. Formatted value.

See Also

scales::dollar()

scales::percent()

Examples

format_currency(2345678, suffix = " PLN")
format_percent(0.52366)

Description

Get information about the cache

Reset the cache

Set the cache directory

Usage

get_cache_info()

reset_cache()

set_cache(path = file.path(getwd(), ".cache"))

Arguments

Value

Invisibly returns the path to the cache directory or a list containing:

Examples

get_cache_info()


reset_cache()


set_cache()

Description

If R4GPF.current_date option is not set, the current system date is used.

Usage

get_current_date()

Value

A date.

Examples

get_current_date()
# Setting custom date using `R4GPF.current_date` option
options(R4GPF.current_date = as.Date("2023-01-01"))
get_current_date()
options(R4GPF.current_date = NULL) # Reset default date#' Working with cache

get_current_date()

Description

Calculates default Gompertz parameters for a given age, country and sex, based on the package build-in HMD life tables.

Usage

get_default_gompertz_parameters(
  age,
  country = unique(life_tables$country),
  sex = c("both", "male", "female")
)

Arguments

Value

A list containing:

See Also

calc_gompertz_parameters()

Examples

get_default_gompertz_parameters(
  age     = 65,
  country = "USA",
  sex     = "male"
)

Description

The Household class aggregates information about a household and its members.

Value

An object of class Household.

Active bindings

Methods

Public methods

• Household$print()

• Household$get_members()

• Household$add_member()

• Household$set_member()

• Household$set_lifespan()

• Household$get_lifespan()

• Household$calc_survival()

• Household$get_min_age()

• Household$clone()

Method print()

Printing the household object

Usage

Household$print(current_date = get_current_date())

Arguments

Method get_members()

Getting members of the household

Usage

Household$get_members()

Method add_member()

Adding a member to the household It will fail if a member with the same name already exists.

Usage

Household$add_member(household_member)

Arguments

Method set_member()

Setting a member of the household If a member already exists, it will be overwritten.

Usage

Household$set_member(member)

Arguments

Method set_lifespan()

Setting an arbitrary lifespan of the household

Usage

Household$set_lifespan(value)

Arguments

Method get_lifespan()

Getting a lifespan of the household If not set, it will be calculated based on the members' lifespans.

Usage

Household$get_lifespan(current_date = get_current_date())

Arguments

Method calc_survival()

Calculating a survival rate of the household based on its members' parameters of the Gompertz model.

Usage

Household$calc_survival(current_date = get_current_date())

Arguments

Method get_min_age()

Calculating a minimum age of the household members.

Usage

Household$get_min_age(current_date = get_current_date())

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

Household$clone(deep = FALSE)

Arguments

Examples

household <- Household$new()
household$risk_tolerance
household$consumption_impatience_preference
household$smooth_consumption_preference

Description

The HouseholdMember class aggregates information about a single member of a household.

Value

An object of class HouseholdMember.

Active bindings

Methods

Public methods

• HouseholdMember$new()

• HouseholdMember$print()

• HouseholdMember$get_name()

• HouseholdMember$get_birth_date()

• HouseholdMember$calc_age()

• HouseholdMember$get_lifespan()

• HouseholdMember$calc_life_expectancy()

• HouseholdMember$calc_survival_probability()

• HouseholdMember$get_events()

• HouseholdMember$set_event()

• HouseholdMember$clone()

Method new()

Creating a new object of class HouseholdMember

Usage

HouseholdMember$new(name, birth_date, mode = NULL, dispersion = NULL)

Arguments

Method print()

Printing the household member object

Usage

HouseholdMember$print(current_date = get_current_date())

Arguments

Method get_name()

Getting the name of the household member

Usage

HouseholdMember$get_name()

Method get_birth_date()

Getting the birth date of the household member

Usage

HouseholdMember$get_birth_date()

Method calc_age()

Calculating the age of the household member

Usage

HouseholdMember$calc_age(current_date = get_current_date())

Arguments

Method get_lifespan()

Calculating a lifespan of the household member

Usage

HouseholdMember$get_lifespan(current_date = get_current_date())

Arguments

Method calc_life_expectancy()

Calculating a life expectancy of the household member

Usage

HouseholdMember$calc_life_expectancy(current_date = get_current_date())

Arguments

Method calc_survival_probability()

Calculating a survival probability of the household member

Usage

HouseholdMember$calc_survival_probability(
  target_age,
  current_date = get_current_date()
)

Arguments

Method get_events()

Getting the events related to the household member

Usage

HouseholdMember$get_events()

Method set_event()

Setting an event related to the household member

Usage

HouseholdMember$set_event(event, start_age, end_age = Inf, years = Inf)

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

HouseholdMember$clone(deep = FALSE)

Arguments

Examples

member <- HouseholdMember$new(
  name       = "Isabela",
  birth_date = "1980-07-15",
  mode       = 91,
  dispersion = 8.88
)
member$calc_age()
member$calc_life_expectancy()

Description

A data frame based on: HMD. Human Mortality Database. Max Planck Institute for Demographic Research (Germany), University of California, Berkeley (USA), and French Institute for Demographic Studies (France). Available at www.mortality.org.

Usage

life_tables

Format

life_tables

A data frame with 6 columns:

country

Country name

sex

Sex: "male", "female", "both"

year

Year

age

Age

mortality_rate

Mortality rate

life_expectancy

Life expectancy

Source

https://www.mortality.org

Description

If multiple Monte Carlo samples are provided in the scenario argument, the normalized median of expected allocation is plotted.

Usage

plot_expected_allocation(
  scenario,
  accounts = c("all", "taxable", "taxadvantaged")
)

Arguments

Value

A ggplot2::ggplot() object.

Examples

older_member <- HouseholdMember$new(
  name       = "older",  
  birth_date = "2000-02-15",
  mode       = 80,
  dispersion = 10
)  
household <- Household$new()
household$add_member(older_member)  

household$expected_income <- list(
  "income" = c(
    "members$older$age <= 65 ~ 10000 * 12"
  )
)
household$expected_spending <- list(
  "spending" = c(
    "TRUE ~ 5000 * 12"
  )
)

portfolio <- create_portfolio_template() 
portfolio$accounts$taxable <- c(10000, 30000)
portfolio$weights$human_capital <- c(0.2, 0.8)
portfolio$weights$liabilities <- c(0.1, 0.9)
portfolio <- 
  portfolio |> 
  calc_effective_tax_rate(
    tax_rate_ltcg            = 0.20, 
    tax_rate_ordinary_income = 0.40
  )

scenario <- 
  simulate_scenario(
   household    = household,
   portfolio    = portfolio,
   current_date = "2020-07-15"
  )

plot_expected_allocation(scenario)

Description

Plots financial capital, human capital, total capital, and liabilities.

Usage

plot_expected_capital(scenario)

Arguments

Value

A ggplot2::ggplot() object.

Examples

older_member <- HouseholdMember$new(
  name       = "older",  
  birth_date = "2000-02-15",
  mode       = 80,
  dispersion = 10
)  
household <- Household$new()
household$add_member(older_member)  

household$expected_income <- list(
  "income" = c(
    "members$older$age <= 65 ~ 10000 * 12"
  )
)
household$expected_spending <- list(
  "spending" = c(
    "TRUE ~ 5000 * 12"
  )
)

portfolio <- create_portfolio_template() 
portfolio$accounts$taxable <- c(10000, 30000)
portfolio$weights$human_capital <- c(0.2, 0.8)
portfolio$weights$liabilities <- c(0.1, 0.9)
portfolio <- 
  portfolio |> 
  calc_effective_tax_rate(
    tax_rate_ltcg            = 0.20, 
    tax_rate_ordinary_income = 0.40
  )

scenario <- 
  simulate_scenario(
   household    = household,
   portfolio    = portfolio,
   current_date = "2020-07-15"
  )

plot_expected_capital(scenario)

Description

Plot future income structure over household life cycle

Usage

plot_future_income(
  scenario,
  period = c("yearly", "monthly"),
  y_limits = c(NA, NA)
)

Arguments

Value

A ggplot2::ggplot() object.

Examples

older_member <- HouseholdMember$new(
  name       = "older",  
  birth_date = "1980-02-15",
  mode       = 80,
  dispersion = 10
)  
household <- Household$new()
household$add_member(older_member)  

household$expected_income <- list(
  "income" = c(
    "members$older$age <= 65 ~ 7000 * 12",
    "members$older$age > 65 ~ 3000 * 12"
  )
)
household$expected_spending <- list(
  "spending" = c(
    "TRUE ~ 5000 * 12"
  )
)

portfolio <- create_portfolio_template() 
portfolio$accounts$taxable <- c(10000, 30000)
portfolio <- 
  portfolio |> 
  calc_effective_tax_rate(
    tax_rate_ltcg = 0.20, 
    tax_rate_ordinary_income = 0.40
  )

scenario <- 
  simulate_scenario(
   household = household,
   portfolio = portfolio,
   current_date = "2020-07-15"
  )

plot_future_income(scenario, "monthly")

Description

This function plots the future saving rates from a scenario object.

Usage

plot_future_saving_rates(
  scenario,
  aggregation_function = stats::median,
  y_limits = c(NA, NA)
)

Arguments

Value

A ggplot2::ggplot() object.

Examples

older_member <- HouseholdMember$new(
  name       = "older",  
  birth_date = "2000-02-15",
  mode       = 80,
  dispersion = 10
)  
household <- Household$new()
household$add_member(older_member)  

household$expected_income <- list(
  "income" = c(
    "members$older$age <= 65 ~ 10000 * 12"
  )
)
household$expected_spending <- list(
  "spending" = c(
    "TRUE ~ 5000 * 12"
  )
)

portfolio <- create_portfolio_template() 
portfolio$accounts$taxable <- c(10000, 30000)
portfolio$weights$human_capital <- c(0.2, 0.8)
portfolio$weights$liabilities <- c(0.1, 0.9)
portfolio <- 
  portfolio |> 
  calc_effective_tax_rate(
    tax_rate_ltcg            = 0.20, 
    tax_rate_ordinary_income = 0.40
  )

scenario <- 
  simulate_scenario(
   household    = household,
   portfolio    = portfolio,
   current_date = "2020-07-15"
  )

plot_future_saving_rates(scenario)

Description

Plot future spending structure over household life cycle, including discretionary and non-discretionary spending. You can also plot discretionary and non-discretionary spending separately, to see structure of non-discretionary spending and possible levels of discretionary spending over time based on Monte Carlo simulations.

Usage

plot_future_spending(
  scenario,
  period = c("yearly", "monthly"),
  type = c("both", "discretionary", "non-discretionary"),
  discretionary_spending_position = c("bottom", "top"),
  y_limits = c(NA, NA)
)

Arguments

Value

A ggplot2::ggplot() object

Examples

older_member <- HouseholdMember$new(
  name       = "older",  
  birth_date = "1980-02-15",
  mode       = 80,
  dispersion = 10
)  
household <- Household$new()
household$add_member(older_member)  

household$expected_income <- list(
  "income" = c(
    "members$older$age <= 65 ~ 9000 * 12"
  )
)
household$expected_spending <- list(
  "spending" = c(
    "members$older$age <= 65 ~ 5000 * 12",
    "TRUE ~ 4000 * 12"
  )
)

portfolio <- create_portfolio_template() 
portfolio$accounts$taxable <- c(10000, 30000)
portfolio <- 
  portfolio |> 
  calc_effective_tax_rate(
    tax_rate_ltcg = 0.20, 
    tax_rate_ordinary_income = 0.40
  )

scenario <- 
  simulate_scenario(
   household = household,
   portfolio = portfolio,
   # monte_carlo_samples = 100,
   current_date = "2020-07-15"
  )

plot_future_spending(scenario, "monthly")
plot_future_spending(
  scenario, 
  "monthly", 
  discretionary_spending_position = "top"
)
plot_future_spending(scenario, "monthly", "non-discretionary")
# If Monte Carlo samples are present: 
# plot_future_spending(scenario, "monthly", "discretionary")

Description

Plotting the results of Gompertz model calibration

Usage

plot_gompertz_calibration(params, mode, dispersion, max_age)

Arguments

Value

A ggplot2::ggplot() object showing the comparison between actual survival rates from life tables and the fitted Gompertz model.

Examples

mortality_rates <- 
  dplyr::filter(
    life_tables,
    country == "USA" & 
    sex     == "female" &
    year    == 2022
  )
  
params <- calc_gompertz_parameters(
  mortality_rates = mortality_rates,
  current_age     = 65
)

plot_gompertz_calibration(params = params)

Description

Plotting the results of Gompertz model calibration for joint survival

Usage

plot_joint_survival(params, include_gompertz = FALSE)

Arguments

Value

A ggplot2::ggplot() object showing the survival probabilities for two individuals and their joint survival probability.

Examples

params <- calc_gompertz_joint_parameters(
  p1 = list(
    age        = 65,
    mode       = 88,
    dispersion = 10.65
  ),
  p2 = list(
    age        = 60,
    mode       = 91,
    dispersion = 8.88
  ),
  max_age = 110
)

plot_joint_survival(params = params, include_gompertz = TRUE)

Description

Probability of dying at a given age is plotted for each member of a household. Also for each member the life expectancy is shown as dashed vertical line.

Usage

plot_life_expectancy(household)

Arguments

Value

A ggplot object.

Examples

hm1 <- 
 HouseholdMember$new(
   name       = "member1",
   birth_date = "1955-01-01",
   mode       = 88,
   dispersion = 10.65
 )
hm2 <- 
 HouseholdMember$new(
   name       = "member2",
   birth_date = "1965-01-01",
   mode       = 91,
   dispersion = 8.88
 )
household <- Household$new()
household$add_member(hm1)
household$add_member(hm2)

plot_life_expectancy(household = household)

Description

The function plots current versus optimal portfolio allocations for each asset class and for taxable and tax-advantaged accounts.

Usage

plot_optimal_portfolio(portfolio)

Arguments

Value

A ggplot2::ggplot() object.

Examples

older_member <- HouseholdMember$new(
  name       = "older",  
  birth_date = "1980-02-15",
  mode       = 80,
  dispersion = 10
)  
household <- Household$new()
household$add_member(older_member)  

household$expected_income <- list(
  "income" = c(
    "members$older$age <= 65 ~ 7000 * 12"
  )
)
household$expected_spending <- list(
  "spending" = c(
    "TRUE ~ 5000 * 12"
  )
)

portfolio <- create_portfolio_template() 
portfolio$accounts$taxable <- c(10000, 30000)
portfolio$accounts$taxadvantaged <- c(0, 20000)
portfolio <- 
  portfolio |> 
  calc_effective_tax_rate(
    tax_rate_ltcg = 0.20, 
    tax_rate_ordinary_income = 0.40
  )

portfolio <- 
  calc_optimal_asset_allocation(
   household = household,
   portfolio = portfolio,
   current_date = "2020-07-15"
  )

plot_optimal_portfolio(portfolio)

Description

Plots the effect of real interest rates (positive or negative) on the purchasing power of savings over the span of 50 years (default).

Usage

plot_purchasing_power(
  x,
  real_interest_rate,
  years = 50,
  legend_title = "Real interest rate",
  seed = NA
)

Arguments

Value

A ggplot2::ggplot() object.

See Also

• How to Determine Our Optimal Asset Allocation?

Examples

plot_purchasing_power(
  x = 10,
  real_interest_rate = seq(-0.02, 0.04, by = 0.02)
)

Description

Plotting retirement ruin

Usage

plot_retirement_ruin(
  portfolio_return_mean,
  portfolio_return_sd,
  age,
  gompertz_mode,
  gompertz_dispersion,
  portfolio_value,
  monthly_spendings = NULL
)

Arguments

Value

A ggplot2::ggplot() object showing the probability of retirement ruin for different monthly spending levels. If a specific 'monthly_spendings' value is provided, it will be highlighted on the plot with annotations.

Examples

plot_retirement_ruin(
  portfolio_return_mean = 0.034,
  portfolio_return_sd   = 0.15,
  age                   = 65,
  gompertz_mode         = 88,
  gompertz_dispersion   = 10,
  portfolio_value       = 1000000,
  monthly_spendings     = 3000
)

Description

Plots the risk adjusted returns for portfolios of various allocations to the risky asset.

Usage

plot_risk_adjusted_returns(
  safe_asset_return,
  risky_asset_return_mean,
  risky_asset_return_sd,
  risk_aversion = 2,
  current_risky_asset_allocation = NULL
)

Arguments

Value

A ggplot2::ggplot() object.

See Also

• How to Determine Our Optimal Asset Allocation?

• Haghani V., White J. (2023) "The Missing Billionaires: A Guide to Better Financial Decisions." ISBN:978-1-119-74791-8.

Examples

plot_risk_adjusted_returns(
  safe_asset_return              = 0.02,
  risky_asset_return_mean        = 0.04,
  risky_asset_return_sd          = 0.15,
  risk_aversion                  = 2,
  current_risky_asset_allocation = 0.8
)

Description

The plot allows to compare metrics for multiple scenarios.

If scenarios are simulated without Monte Carlo samples, so they are based only on expected returns of portfolio, two metrics are available for each scenario:

• constant discretionary spending - certainty equivalent constant level of consumption that would result in the same lifetime utility as a given series of future consumption in a given scenario (the higher, the better).

• utility of discretionary spending - normalized to minimum and maximum values of constant discretionary spending (the higher, the better).

If scenarios are simulated with additional Monte Carlo samples, there are four more metrics available per scenario:

• constant discretionary spending (for Monte Carlo samples),

• normalized median utility of discretionary spending (for Monte Carlo samples),

• median of missing funds that need additional income or additional savings at the expense of non-discretionary spending, (of yearly averages of Monte Carlo samples),

• median of discretionary spending (of yearly averages of Monte Carlo samples).

Usage

plot_scenarios(scenarios, period = c("yearly", "monthly"))

Arguments

Value

A ggplot2::ggplot() object.

Examples

older_member <- HouseholdMember$new(
  name       = "older",  
  birth_date = "1980-02-15",
  mode       = 80,
  dispersion = 10
)  
household <- Household$new()
household$add_member(older_member)  

household$expected_income <- list(
  "income" = c(
    "is_not_on('older', 'retirement') ~ 7000 * 12"
  )
)
household$expected_spending <- list(
  "spending" = c(
    "TRUE ~ 4000 * 12"
  )
)

portfolio <- create_portfolio_template() 
portfolio$accounts$taxable <- c(100000, 300000)
portfolio <- 
  portfolio |> 
  calc_effective_tax_rate(
    tax_rate_ltcg = 0.20, 
    tax_rate_ordinary_income = 0.40
  )

start_ages <- c(60, 65, 75)
scenarios_parameters <- 
  tibble::tibble(
    member    = "older",
    event      = "retirement",
    start_age = start_ages,
    years     = Inf,
    end_age   = Inf
   ) |> 
  dplyr::mutate(scenario_id = start_age) |> 
  tidyr::nest(events = -scenario_id)

scenarios <- 
  simulate_scenarios(
    scenarios_parameters = scenarios_parameters,
    household            = household,
    portfolio            = portfolio,
    maxeval              = 100,
    current_date         = "2020-07-15"
  )

plot_scenarios(scenarios, "monthly")

Description

Plot survival probabilities for each household members and for the entire household when at least one member is alive. The household joint survival probability is also approximated by a Gompertz model.

Usage

plot_survival(household, current_date = get_current_date())

Arguments

Value

A ggplot object.

Examples

hm1 <- 
 HouseholdMember$new(
   name       = "member1",
   birth_date = "1955-01-01",
   mode       = 88,
   dispersion = 10.65
 )
hm2 <- 
 HouseholdMember$new(
   name       = "member2",
   birth_date = "1965-01-01",
   mode       = 91,
   dispersion = 8.88
 )
hm3 <- 
 HouseholdMember$new(
   name       = "member3",
   birth_date = "1975-01-01",
   mode       = 88,
   dispersion = 7.77
 )
household <- Household$new()
household$add_member(hm1)
household$add_member(hm2)
household$add_member(hm3) 

plot_survival(
 household    = household, 
 current_date = "2020-01-01"
)

Description

Reading HMD life tables

Usage

read_hmd_life_tables(
  path = getwd(),
  files = c("mltper_1x1.txt", "fltper_1x1.txt", "bltper_1x1.txt")
)

Arguments

Value

A data frame containing mortality data with columns:

References

HMD. Human Mortality Database. Max Planck Institute for Demographic Research (Germany), University of California, Berkeley (USA), and French Institute for Demographic Studies (France). Available at www.mortality.org

Examples

## Not run: 
# Download 'txt' files 
# ("mltper_1x1.txt", "fltper_1x1.txt", "bltper_1x1.txt") 
# for a given country to the working directory
# from https://www.mortality.org after registration.

read_hmd_life_tables(path = getwd())

## End(Not run)

Description

Rendering a scenario snapshot

Usage

render_scenario_snapshot(scenario, index = 0, currency = "", big_mark = " ")

Arguments

Value

A gt::gt() object.

Examples

older_member <- HouseholdMember$new(
  name       = "older",  
  birth_date = "1980-02-15",
  mode       = 80,
  dispersion = 10
)  
household <- Household$new()
household$add_member(older_member)  

household$expected_income <- list(
  "income" = c(
    "members$older$age <= 65 ~ 9000 * 12"
  )
)
household$expected_spending <- list(
  "spending" = c(
    "members$older$age <= 65 ~ 5000 * 12",
    "TRUE ~ 4000 * 12"
  )
)

portfolio <- create_portfolio_template() 
portfolio$accounts$taxable <- c(10000, 30000)
portfolio <- 
  portfolio |> 
  calc_effective_tax_rate(
    tax_rate_ltcg = 0.20, 
    tax_rate_ordinary_income = 0.40
  )

scenario <- 
  simulate_scenario(
   household = household,
   portfolio = portfolio,
   current_date = "2020-07-15"
  )
render_scenario_snapshot(scenario)

Description

Run a package app

Usage

run_app(
  which = c("risk-adjusted-returns", "purchasing-power", "retirement-ruin"),
  res = 120,
  shinylive = FALSE
)

Arguments

Value

A shiny::shinyApp() object if shinylive is TRUE. Runs the app if shinylive is FALSE with shiny::runApp().

Examples

run_app("risk-adjusted-returns")
run_app("purchasing-power")
run_app("retirement-ruin")

Description

The function simulates a scenario of household lifetime finances and returns a tibble with nested columns. By default no Monte Carlo samples are generated, and only single sample based on portfolio expected returns are returned with column sample=0. If the additional Monte Carlo samples are generated, they have consecutive IDs starting from 1 in the sample column.

Usage

simulate_scenario(
  household,
  portfolio,
  scenario_id = "default",
  current_date = get_current_date(),
  monte_carlo_samples = NULL,
  seeds = NULL,
  use_cache = FALSE,
  auto_parallel = FALSE,
  debug = FALSE,
  ...
)

Arguments

Value

A tibble with nested columns including:

• scenario_id - (character) ID of the scenario

• sample - (integer) ID of the Monte Carlo sample

• index - (integer) year index starting from 0

• years_left - (integer) years left to the end of the household lifespan

• date - (date) date after index years from the current date

• year - (integer) calendar year

• survival_prob - (double) survival probability of the household

• members - (nested tibble) data of each member in the household

• income - (nested tibble) income streams

• total_income - (double) total income of the household from all income streams

• spending - (nested tibble) non-discretionary spending streams

• nondiscretionary_spending - (double) total non-discretionary spending of the household from all non-discretionary spending streams

• human_capital - (double) human capital of the household

• liabilities - (double) liabilities of the household

• portfolio - (nested tibble) state of investment portfolio

• financial_wealth - (double) financial wealth of the household at the beginning of the year

• net_worth - (double) net worth of the household

• discretionary_spending - (double) optimal discretionary spending of the household

• total_spending - (double) total spending of the household (discretionary + non-discretionary)

• financial_wealth_end - (double) financial wealth of the household at the end of the year

• risk_tolerance - (double) risk tolerance of the household

• smooth_consumption_preference - (double) smooth consumption preference of the household

• consumption_impatience_preference - (double) consumption impatience preference of the household

• time_value_discount - (double) time value discount based on consumption impatience of the household

• discretionary_spending_utility - (double) discretionary spending utility of the household based on the smooth consumption preference

• discretionary_spending_utility_weighted - (double) discretionary spending utility of the household weighted by survival probability and time value discount.

Examples

older_member <- HouseholdMember$new(
  name       = "older",  
  birth_date = "1980-02-15",
  mode       = 80,
  dispersion = 10
)  
household <- Household$new()
household$add_member(older_member)  

household$expected_income <- list(
  "income" = c(
    "members$older$age <= 65 ~ 7000 * 12"
  )
)
household$expected_spending <- list(
  "spending" = c(
    "TRUE ~ 5000 * 12"
  )
)

portfolio <- create_portfolio_template() 
portfolio$accounts$taxable <- c(10000, 30000)
portfolio <- 
  portfolio |> 
  calc_effective_tax_rate(
    tax_rate_ltcg = 0.20, 
    tax_rate_ordinary_income = 0.40
  )

scenario <- 
  simulate_scenario(
   household = household,
   portfolio = portfolio,
   current_date = "2020-07-15"
  )
names(scenario)

Description

Simulate multiple scenarios of household lifetime finances

Usage

simulate_scenarios(
  scenarios_parameters,
  household,
  portfolio,
  current_date = get_current_date(),
  monte_carlo_samples = NULL,
  seeds = NULL,
  auto_parallel = FALSE,
  use_cache = FALSE,
  debug = FALSE,
  ...
)

Arguments

Value

A tibble with nested columns.

Examples

older_member <- HouseholdMember$new(
  name       = "older",  
  birth_date = "1980-02-15",
  mode       = 80,
  dispersion = 10
)  
household <- Household$new()
household$add_member(older_member)  

household$expected_income <- list(
  "income" = c(
    "members$older$age <= 65 ~ 7000 * 12"
  )
)
household$expected_spending <- list(
  "spending" = c(
    "TRUE ~ 5000 * 12"
  )
)

portfolio <- create_portfolio_template() 
portfolio$accounts$taxable <- c(10000, 30000)
portfolio <- 
  portfolio |> 
  calc_effective_tax_rate(
    tax_rate_ltcg = 0.20, 
    tax_rate_ordinary_income = 0.40
  )
start_ages <- c(60, 65, 70)
scenarios_parameters <- 
  tibble::tibble(
    member    = "older",
    event      = "retirement",
    start_age = start_ages,
    years     = Inf,
    end_age   = Inf
   ) |> 
  dplyr::mutate(scenario_id = start_age) |> 
  tidyr::nest(events = -scenario_id)

scenarios_parameters

scenarios <- 
  simulate_scenarios(
    scenarios_parameters = scenarios_parameters,
    household            = household,
    portfolio            = portfolio,
    current_date         = "2020-07-15"
  )
scenarios$scenario_id |> unique()