Portfolio Simulation - Part III
Previously, we ran a complete simulation to get a distribution for 10-year returns. Now we will use simulation for decision making.
Let's go back to your stock-heavy portfolio with an expected return of 7% and a volatility of 30%. You have the choice of rebalancing your portfolio with some bonds such that the expected return is 4% & volatility is 10%. You have a principal of $10,000. You want to select a strategy based on how much your portfolio will be worth in 10 years. Let's simulate returns for both the portfolios and choose based on the least amount you can expect with 75% probability (25th percentile).
Upon completion, you will know how to use a portfolio simulation for investment decisions.
The portfolio_return()
function is again pre-loaded in the environment.
This exercise is part of the course
Statistical Simulation in Python
Exercise instructions
- Set
avg_return
andvolatility
parameters to 0.07 and 0.3, respectively, for the stock portfolio. - Set
avg_return
andvolatility
parameters to 0.04 and 0.1, respectively, for the bond portfolio. - Calculate the 25th percentile of the distribution of returns for the stock
rets_stock_perc
and bondrets_bond_perc
portfolios. - Calculate and print how much additional returns
additional_returns
you would lose or gain by sticking with stocks instead of going to bonds.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
for i in range(sims):
rets_stock.append(portfolio_return(yrs = 10, avg_return = ____, volatility = ____, principal = 10000))
rets_bond.append(portfolio_return(yrs = 10, avg_return = ____, volatility = ____, principal = 10000))
# Calculate the 25th percentile of the distributions and the amount you'd lose or gain
rets_stock_perc = ____
rets_bond_perc = ____
additional_returns = ____
print("Sticking to stocks gets you an additional return of {}".format(additional_returns))