Category: s24-geo411

Lesson 18: Retirement Portfolio Simulation Presentations

Review:

  • Schedule
    • Fri 26 Apr
      • Election Simulation project work session
    • Wed 1 May
      • No class – Labor Day
    • Fri 3 May
      • Review for Final Exam
      • Election Simulation Presentations
    • Mon 6 May
      • Final Exam

Presentation:

  • Student Retirement Portfolio Presentations

Assignment:

  • Final Project
    • This will be worth 40% of the Final Exam score
    • Build upon your Election Model, using the same State you were assigned
    • Use your predicted Trump % + the actual 2016 and 2020 Trump %
      • The average of the 3 values should be your estimated 2024 Trump %
      • Calculate the standard deviation (using your predicted value to increase variance)
    • Create a Monte Carlo Simulation to generate a probabilistic election forecast
      • Simulate the 2024 Trump % vote
      • Simulate a 3rd party % using a similar approach (i.e., 2016+2020 average)
      • Generate Biden % = 100% – (Trump % + 3rd Party %)
      • Simulation count, n=1000
      • Your simulation results should allow you to say that “Trump has a XX% chance to win”
    • Prepare a Spreadsheet file
      • You’ll use a Google Sheets file to document and explain your simulation
      • Make it easy to understand and navigate
    • Demonstrate your simulation
      • Present your simulation in class on Fri 3 May
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Lesson 16: Retirement Savings Portfolio Simulation

Review:

  • Stock market simulation
  • Combined with bonds

Project Assignment:

  • Build upon your Stock + Bond Market Simulation
  • Simulation Requirements
    • 40-year time horizon
    • Simulation count, n=100
    • Ability to set/modify simulation parameters by changing cell values (not by editing formula)
      • initial balance
      • annual contribution
      • stock/bond % allocation
      • financial target, e.g., $1,000,000
    • Functional start button to launch automation of simulation
    • Display at end of simulation
      • probability (%) of reaching financial target
      • distribution of end balances on a histogram
      • calculation of Max, Min, Mean, Median and Standard Deviation of end balances
  • Run 4 simulation scenarios
    • Scenario 1
      • Beginning balance = 10,000
      • Annual contribution = 1,000
      • Stock/Bond allocation = 60%/40%
      • Target = $1,000,000
    • Scenario 2
      • Beginning balance = 10,000
      • Annual contribution = 1,000
      • Stock/Bond allocation = 80%/20%
      • Target = $1,000,000
    • Scenario 3
      • Beginning balance = 3,000
      • Annual contribution = 3,000
      • Stock/Bond allocation = 60%/40%
      • Target = $2,000,000
    • Scenario 4
      • Beginning balance = 3,000
      • Annual contribution = 3,000
      • Stock/Bond allocation = 80%/20%
      • Target = $2,000,000
    • For each scenario, answer the following questions:
      • What is the Max, Min, Mean, Median and Standard Deviation of end balances
      • What is the probability of reaching the target?
  • Prepare a Spreadsheet file
    • You’ll use a Google Sheets file to document and explain your simulation
    • Make it easy to understand and navigate
    • Be ready to demonstrate to me privately in class
  • Optional Extra Credit Opportunity
    • Present your Simulation spreadsheet to the entire class
    • ~3 minute presentation
    • During class on Wed 24 Apr
    • Earn up to +2% bonus, applied to overall grade
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Lesson 15: Simulation of Continuous Variables

Review:

  • Discrete Simulation
  • Automation in Sheets

Presentation:

  • Random Variables
    • Discrete Random Variables
      • Probability distribution table
      • Dice Simulation
      • =RANDBETWEEN()
    • Continuous Random Variables
      • Assume normal distribution
      • =norminv(rand(),$B$2,$B$3)
  • Stock Market Simulation

Activity/Assignment:

  • Stock Market Simulation in Sheets
  • Modifications Part 1
    • Generate a 40-year simulation (instead of 30)
    • Modify the simulation to add $1,000 to the balance each year
    • Make a histogram of your ending balances
  • Modifications Part 2
    • Add a “fixed income” (bonds) component to your simulation
    • Assumptions
      • bond market returns are normally distributed
      • mean = 7.88%
      • standard deviation = 6.86%
    • Your “portfolio” should be 60% stocks and 40% bonds
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Lesson 14: Discrete Simulation with Automation

Review:

  • Exam 2
  • Discrete vs Continuous probability/simulation
    • This week – discrete
    • Next week – continuous
  • Lucky 7

Presentation:

Activity/Assignment:

  • Simulate Chuck-a-luck where the player rolls 3 dice
    • Here are the rules
    • Apply the following simplifying assumptions
      • If sum of 3 dice >10, player wins the “Big” (1 to 1 odds)
      • If the roll is three of a kind, player wins “Any Triple” (30 to 1 odds)
      • All other outcomes, player loses the bet
    • Will the “House” win money over time with these rules? Explain.
    • Start with the Lucky 7 Simulation and build on it.
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Lesson 13: Intro to Probability and Random Numbers for Monte Carlo Simulation

Review:

  • Exam 2
    • Median = 94.0
    • Average = 83.5

Presentation:

  • Monte Carlo Simulation
    • Use a random number generator
    • Approximate the probability distribution
    • Run multiple iterations (like, thousands) and observe distribution of outcomes
  • Probability
    • Topic worthy of its own course
    • Discrete vs Continuous
    • Probability distribution for rolling a pair of dice
    • Probability distribution for height
  • Random Numbers
    • Demonstrate RANDBETWEEN() in Sheets
    • Demonstrate RAND() in Sheets
  • Simulation
    • Individual outcomes are uncertain
    • Distribution of outcomes over many iterations are predictable.
  • Demonstrate Coin Toss Simulation in Sheets
  • Dice Simulation
    • More RANDBETWEEN()
    • Rolling 2 dice
    • RANDBETWEEN(1,12) ≠ sum of two dice
    • Histogram comparison
    • Probability distribution
  • Demonstrate Dice simulation in Sheets

Activity/Assignment:

  • Create your own Dice Simulation
    • Lucky 7 game
    • Roll a 7, you win!
    • Roll anything else (other than a 7), you lose. 
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Review for Exam 2

Presentation:

  • Exam 2 will cover Lessons 6-12, focused entirely on Multiple Regression
  • Prepare a 1-page (front and back) summary of your Election forecasting model
    • Describe how you created the model for your assigned state.
    • Include a regression output table for your final model.
    • What is your predicted % Trump vote?
    • What is your predicted voter turnout (total number of votes cast)?
    • Who is your projected winner?
      • Assume same % vote will go to 3rd party candidates as in 2020.
      • Assume Biden % = 100% – (Trump % + 3rd party %)
  • Bring a printed copy of your 1-page summary to the exam

Key topics:

  • Possible questions about your election model
  • Regression Output Interpretation
    • t-statistics
    • p-values
    • R² measures “goodness of fit” (explains % of variance)
    • p-value measures “significance” (probability of result by chance)
    • rank by significance
  • Multicollinearity
  • Interaction Variables
  • Categorical data
  • Indicator or “Dummy” Variables
  • Index Variables
  • Modeling
  • Residuals
  • Actual vs Predicted
  • Outliers

Activity:

  • Practice Exam

Assignment:

  • Study for Exam 2
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Lesson 12: Forecasting the 2024 US Presidential Election, part 2

Review:

  • Sourcing and pre-processing data
  • Census.gov
  • Secretary of State offices
  • Wikipedia
  • Exam 2
  • Review and Exam next week
  • Election model will be part of Exam 2

Presentation:

  • Assemble demographic and election data for your assigned State
  • Run correlation analyses and initial regression models
  • Narrow your model to significant variables
  • Generate residuals
  • Compare “actual” versus “predicted” using 2020 election results
  • Make adjustments

Activity:

  • Work on assembling your data and building your model

Assignment:

  • Complete assembly of your data set and prepare a “candidate” model for Tue
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Lesson 11: Forecasting the 2024 US Presidential Election

Review:

  • Singapore non-strata housing models
  • Final Exam at 14:30-16:00 on Monday, 6 May in B.5.12/B.5.13
  • Exam 2 next week

Presentation:

Activity:

  • Begin assembling data to generate forecasts for your assigned State
    • Population and demographics from Census
    • Voter Turnout and % Trump vs Clinton (2016) and % Trump vs Biden (2020)
    • Polling results (save this for later)
  • Run correlation and/or initial regression analysis to identify and assess relationships
  • Use Trump vote % as dependent variable

Assignment:

  • Test various demographic variables to include in a forecast model
  • Construct an initial multivariate model to estimate Trump vote % by County in your assigned state
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Lesson 10: Model Construction

Review:

  • Index variables (geo index?)
  • Final exam scheduled for 14:30-16:00 on Mon, 6 May.

Presentation:

  • Real Estate Model using Single Family homes (instead of apartments)

Assignment:

  • Create model and provide regression equation, R-squared, F Stat, t-stats and p-values.
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