Session 1: Investment Foundations - From Financial Management to Investment Decisions#
🤖 AI Copilot Reminder: Throughout this session, you’ll be working alongside your AI copilot to deepen understanding, analyze concepts, and prepare to teach others. Look for the 🤖 symbol for specific collaboration opportunities.
Section 1: The Investment Hook#
The Retirement Reality Check#
Meet Sarah, 22, just graduated with her finance degree. She’s excited about her new job paying $60,000/year and wants to start investing for retirement. Her grandmother gives her some advice: “Put your money in a nice safe CD earning 3% - it’s guaranteed!”
Sarah runs the numbers:
Monthly contribution: $500 (10% of gross income)
CD rate: 3% annually
Time horizon: 43 years until retirement at 65
Your Challenge: Help Sarah Discover the Problem
🤖 AI Copilot Activity: Practice structured communication with your AI copilot:
Step 1 - Set Context: “I’m learning about investment returns and inflation. I need help with a retirement calculation.”
Step 2 - Make Specific Request: “Please calculate the future value if someone saves $500/month at 3% annual return for 43 years. Show me the formula and each step.”
Step 3 - Verify Understanding: “Can you explain why we use monthly compounding? What would change if we used annual compounding?”
Step 4 - Extend Learning: “Now help me understand inflation’s impact. What could $500,000 buy in 1980 versus today? What does this mean for retirement planning?”
Timeline Visualization:
Age 22 ←─────────── 43 years ──────────→ Age 65
\$500/month @ 3% = ?
But what about inflation's impact?
Real purchasing power = ? in today's dollars
Discovery Questions for You:
Calculate Sarah’s retirement nest egg at 3% returns
Research: What could $100,000 buy in 1980 vs. today?
Critical Insight: Why might a “guaranteed” return actually be risky?
Your Mission: Discover why Sarah’s “safe” strategy might actually be the riskiest choice for her career and financial future. What investment principles should every business professional understand?
Career Connection: As future business leaders, you’ll make investment decisions for companies, manage employee 401(k) programs, advise clients, or optimize your own wealth. Understanding risk-return trade-offs is fundamental to business success.
Bridge from Financial Management#
Key Shift: In Financial Management, you calculated with known rates and certain outcomes. In investments, you’ll master decision-making under uncertainty.
Business Reality: Every company faces this same challenge - how to allocate resources when future returns are uncertain but necessary for growth and competitiveness.
Section 2: Foundational Investment Concepts & Models#
Core Investment Definitions#
🤖 AI Copilot Activity: Learn to explore concepts systematically:
Initial Request: “I need to understand three financial concepts: saving, investing, and speculating.”
Clarifying Questions:
“What is the main purpose of each approach?”
“How do they differ in terms of risk and time horizon?”
“Can you give me a specific example of each?”
Critical Thinking: “If someone only saves and never invests, what are the long-term consequences? What about someone who only speculates?”
Investment vs. Speculation vs. Saving - Detailed Definitions
Saving is the practice of preserving capital with minimal risk of loss, prioritizing safety and liquidity over returns. Characteristics include:
Primary Goal: Capital preservation and liquidity
Risk Level: Very low (often FDIC insured up to $250,000)
Expected Returns: Low, often barely keeping pace with inflation
Time Horizon: Short-term (emergency funds) to medium-term (major purchases)
Examples: Savings accounts (0.5-1% annually), Certificates of Deposit (CDs), money market accounts
Trade-off: Safety and accessibility in exchange for limited growth potential
Investment is the strategic commitment of capital with the expectation of generating profitable returns over time while accepting calculated, measured risk. Characteristics include:
Primary Goal: Wealth accumulation and growth over time
Risk Level: Moderate to high, with risk managed through diversification and time horizon
Expected Returns: Higher than inflation, typically 4-10% annually depending on asset mix
Time Horizon: Medium-term (3-5 years) to long-term (decades)
Examples: Stocks, bonds, mutual funds, ETFs, real estate
Trade-off: Accept uncertainty and volatility in exchange for growth potential
Speculation involves high-risk financial decisions focused on short-term price movements rather than fundamental value creation. Characteristics include:
Primary Goal: Quick profits from price volatility
Risk Level: Very high, with possibility of total loss
Expected Returns: Potentially very high or very negative
Time Horizon: Very short-term (days, weeks, months)
Examples: Day trading, options, cryptocurrency, penny stocks
Trade-off: Potential for large gains with high probability of significant losses
The Investment Opportunity Set - Complete Definition
🤖 AI Copilot Activity: Ask your AI copilot: “Explain the concept of the investment opportunity set and how it relates to the risk-return trade-off. Why can’t all investments offer high returns with low risk?”
The Investment Opportunity Set represents the complete universe of all possible investments available to an investor at any given time, each characterized by different combinations of expected risk and return. This concept is fundamental to modern portfolio theory and investment decision-making. Disclaimer: These are not guaranteed and change with market conditions.
Risk-free Assets (typically 0-4% expected annual return)
Definition: Investments with virtually no chance of loss and guaranteed returns
Characteristics: High liquidity, government backing, stable nominal value
Examples: U.S. Treasury bills, FDIC-insured savings accounts, money market funds, CDs
Career Application: Every business needs cash management - from emergency funds to short-term operating capital
The Hidden Risk: What happens when “safe” investments lose purchasing power over time?
Low-Risk Assets (typically around 5% expected annual return)
Definition: Investments with low probability of loss but limited growth potential
Characteristics: Moderate liquidity, some interest rate sensitivity, predictable income
Examples: High-grade corporate bonds, government bonds, stable value funds
Role in Portfolio: Provide income and stability while offering some protection against inflation
Trade-off: Higher returns than risk-free assets but with some volatility and credit risk
Moderate-Risk Assets (typically around 7% expected annual return)
Definition: Investments with balanced risk-return profiles suitable for long-term growth
Characteristics: Moderate volatility, diversification benefits, professional management
Examples: Diversified stock mutual funds, balanced funds, target-date funds, broad market ETFs
Career Application: Foundation of most 401(k) plans and long-term wealth building strategies
Business Logic: Accept short-term uncertainty for long-term growth potential
High-Risk Assets (typically around 10% expected annual return, high volatility)
Definition: Investments with potential for significant gains but substantial risk of loss
Characteristics: High volatility, concentration risk, requires expertise or luck
Examples: Individual stocks, sector-specific funds, real estate, commodities, growth stocks
Role in Portfolio: Potential for outperformance but should be limited allocation
Trade-off: Highest return potential with highest risk of significant losses
Mathematical Foundation: Investment Returns#
🤖 AI Copilot Activity: Before diving into the math, ask your AI copilot: “Help me understand why we need to distinguish between nominal and real returns. Can you provide some historical examples of periods when this distinction was especially important?”
Nominal vs. Real Returns - Complete Mathematical Framework
Nominal Return (R_nominal) is the actual percentage return earned on an investment before adjusting for inflation. This is the return you see quoted in financial statements and investment performance reports.
Formula: R_nominal = (Ending Value - Beginning Value + Income) / Beginning Value
Example: If you invest $1,000 and it grows to $1,070 with $20 in dividends, your nominal return is (1,070 - 1,000 + 20) / 1,000 = 9%
Limitation: Doesn’t account for the loss of purchasing power due to inflation
Real Return (R_real) is the return adjusted for the effects of inflation, representing the actual increase in purchasing power.
Formula: (1 + R_real) = (1 + R_nominal) / (1 + Inflation Rate)
Alternative Formula: R_real ≈ R_nominal - Inflation Rate (approximation for small rates)
Purpose: Shows what your returns actually mean in terms of what you can buy
Practice Calculation Challenge:
🤖 AI Copilot Activity: Before seeing the solution, work with your AI copilot: “Help me set up the real return calculation for a CD earning 3% with 2.5% inflation. What’s the formula, and what does each component represent? Guide me through the calculation step by step.”
Scenario: CD earning 3% nominal return with 2.5% inflation
Your Task: Calculate the real return using the formula
(1 + R_real) = (1 + R_nominal) / (1 + Inflation Rate)
Step 1: Identify your inputs
- R_nominal = ?
- Inflation Rate = ?
Step 2: Apply the formula
(1 + R_real) = (?) / (?) = ?
Step 3: Solve for real return
R_real = ? - 1 = ? = ?% annually
Business Logic Check: If your money grows by 3% but everything costs 2.5% more, what’s your real gain?
Why This Matters - Historical Context:
In the 1970s, CDs earned 6-8% nominal returns, but with 10-14% inflation, real returns were deeply negative
During 2010-2020, stock markets earned 10%+ nominal returns with 2% inflation, providing strong real returns
The Federal Reserve targets 2% inflation, meaning a “risk-free” 2% return provides zero real growth
Risk Premium Concept Risk Premium = Expected Return - Risk-free Rate
This compensates investors for accepting uncertainty. The fundamental relationship: Expected Return = Risk-free Rate + Risk Premium
Time Value of Money with Uncertainty#
In Financial Management, you calculated: FV = PV × (1 + r)^n
In investments, we modify this for uncertainty: E[FV] = PV × (1 + E[r])^n ± Risk Adjustment
Where E[r] is the expected return and risk adjustment reflects the uncertainty around that expectation.
Industry Context: The Investment Process#
Professional investment management follows a systematic process:
Investment Policy: Define objectives, constraints, risk tolerance
Strategic Asset Allocation: Long-term mix across asset classes
Security Selection: Choose specific investments within asset classes
Implementation: Execute trades and manage costs
Monitoring & Rebalancing: Maintain target allocations and adjust as needed
Section 3: The Investment Gym - Partner Practice & AI Copilot Learning#
Solo Practice Problems (10-15 minutes)#
Discovery Challenge: Test Your Understanding
Problem 1: Career Application - You’re a Financial Advisor Three clients ask about their investments. Calculate real returns and explain what this means for their purchasing power:
Bond earning 4% nominal, inflation = 2% → Real return = ?
Stock earning 10% nominal, inflation = 3% → Real return = ?
REIT earning 8% nominal, inflation = 4% → Real return = ?
Problem 2: Sarah’s Career Impact Analysis Research current market conditions, then calculate (Calculate by working with your AI copilot):
FV at 3% nominal (conservative approach) = \( ? nominal, \) ? real
Stocks at 7%: \( ? nominal, \) ? real
Detailed Calculation for Sarah’s Retirement: Using the future value of an annuity formula: FV = P * (((1 + r)^n - 1) / r) Where: P = Monthly contribution = $500 n = Number of months = 43 years * 12 months/year = 516 months
Scenario 1: CD at 3% annually Monthly rate (r) = 3% / 12 = 0.0025 FV = $500 * (((1 + 0.0025)^516 - 1) / 0.0025) FV = $500 * ((3.6888 - 1) / 0.0025); May have rounding error in calculation FV = $500 * (2.6888 / 0.0025) FV = $500 * 1075.52 FV = $?
Note that
Scenario 2: Stocks at 7% annually Monthly rate (r) = 7% / 12 = 0.00583333; May have rounding error in calculation FV = $500 * (((1 + 0.00583333)^516 - 1) / 0.00583333) FV = $500 * ((19.006 - 1) / 0.00583333) FV = $500 * (18.006 / 0.00583333) FV = $500 * 3086.74 FV = $ ?
Critical Thinking: Which approach better serves Sarah’s career goals and lifestyle expectations?
AI Copilot Learning Phase (10-15 minutes)#
AI Collaboration Prompt: “Act as a portfolio manager and help me understand why the investment opportunity set matters for retirement planning. I need to explore: 1) How do different asset classes perform over long time horizons? 2) What historical evidence shows the relationship between risk and return? 3) How should a 22-year-old think about risk differently than a 55-year-old? Help me frame this analysis so I can teach my peer partner the key insights.”
Student Task: Work with AI to understand the concept, then prepare to teach your partner:
The mathematical relationship between risk and return
Why time horizon affects investment choices
How to explain real vs. nominal returns with a clear example
Reciprocal Teaching Component (15-20 minutes)#
Structured Roles:
Financial Analyst: Explain the investment logic and risk-return relationship
Code Reviewer: Understand how we would implement return calculations in Python
Skeptic: Challenge assumptions and ask “what if” questions
Teaching Requirements: Each student must explain to their partner:
Financial Logic: Why does Sarah need to accept more risk for retirement success?
Mathematical Foundation: How to calculate real returns and why it matters
Investment Reasoning: What factors should influence Sarah’s asset allocation decision?
Collaborative Challenge Problem (15-20 minutes)#
Scenario: You’re financial advisors to three clients:
Client A: Age 25, stable job, 40-year horizon
Client B: Age 45, approaching peak earnings, 20-year horizon
Client C: Age 60, planning retirement in 5 years
Challenge: Using the investment opportunity set concept, recommend an appropriate risk level for each client and justify your reasoning. Consider:
Time horizon for recovery from market downturns
Inflation impact over different periods
Career earnings trajectory and contribution ability
Robinhood Integration#
🤖 AI Copilot Activity: Before exploring the platform, ask your AI copilot: “Help me understand how to interpret the financial data I’ll find on investment platforms. What do interest rates, bond yields, and stock market ratios tell us about current economic conditions? How should beginning investors use this information in their decision-making?”
Platform Activity:
Open Robinhood app/website (paper trading account)
Look up current rates for:
High-yield savings account (proxy for risk-free rate)
10-year Treasury bond yield
S&P 500 current P/E ratio and historical returns
Compare these rates to current inflation expectations
Discussion Questions:
What risk premium does the stock market currently offer over bonds?
How do current real returns compare to historical averages?
Debrief Discussion (10 minutes)#
Key Insights to Surface:
Risk and return are fundamentally linked in efficient markets
Time horizon is crucial for risk tolerance decisions
Inflation is a hidden risk that erodes purchasing power
The “riskiest” strategy might be taking no risk at all
Section 4: The Investment Coaching - Your DRIVER Learning Guide#
Coaching Scenario: “Should Sarah Choose Safety or Growth?”#
Sarah (our 22-year-old graduate) must decide between:
Option A: CD at 3% annually (guaranteed)
Option B: Diversified stock portfolio averaging 7% annually (historical average, not guaranteed)
Let’s apply the full DRIVER framework:
Define & Discover#
🤖 DRIVER Stage 1: Structured Prompt Starters
Step 1 - Context Exploration Prompt: “Act as a financial planner specializing in retirement planning. Help me explore the context of Sarah’s investment decision. What economic and market factors should a 22-year-old consider when planning for retirement 43 years away? What has historically been true about long-term investing that should influence her thinking?”
Step 2 - Problem Framing Prompt: “Help me frame Sarah’s specific investment decision systematically: 1) What are the critical variables affecting a 43-year investment horizon? 2) What assumptions should we make explicit about inflation, market returns, and Sarah’s risk tolerance? 3) What success criteria define a ‘good’ retirement outcome for someone starting at age 22? 4) What constraints or limitations should we consider?”
Step 3 - Verification and Refinement Prompt: “Review my problem framing for Sarah’s retirement decision. Is this framework complete and logical? What important considerations might I be missing? How can I make this analysis more robust and realistic?”
Problem Framing:
Objective: Accumulate sufficient retirement assets to maintain lifestyle
Constraints: $500/month contribution, 43-year horizon, cannot recover from major late-career losses
Variables: Expected returns, inflation rate, volatility, contribution growth
Success Criteria: Real purchasing power of at least 70% of final working income
Student Documentation: “A clearly defined investment question: Should Sarah prioritize capital preservation or growth, and what data do we need to make this decision analytically?”
Represent#
🤖 DRIVER Stage 2: Structured Prompt Starters
Step 1 - Visualization Planning Prompt: “Help me create a logical visual structure for Sarah’s retirement investment analysis. I need to map the flow from inputs (contributions, time horizon, risk tolerance) to outputs (retirement wealth, probability of success). What would be the most effective way to visualize this decision process?”
Step 2 - Model Structure Prompt: “Help me design the logical framework for comparing Sarah’s investment options. What are the key steps in analyzing a 43-year retirement scenario? How should I structure the comparison between conservative (CD) and growth (stock) strategies?”
Step 3 - Logic Verification Prompt: “Review my logical structure for Sarah’s retirement decision analysis. Does this framework capture the key trade-offs between safety and growth? What am I missing in terms of risk assessment or scenario planning? How can I make this analysis more comprehensive?”
Visual Mapping Example:
Investment Decision Framework:
Time Horizon (43 years)
↓
Risk Capacity Analysis
↓
Expected Return Scenarios
↓
Monte Carlo Simulation → Probability of Success
↓
Decision: Risk Level
Logic Documentation:
Pseudocode for retirement analysis:
1. Define contribution schedule (\$500/month, growing with income)
2. Set investment scenarios (conservative, moderate, aggressive)
3. Calculate future values for each scenario
4. Adjust for inflation to get real purchasing power
5. Compare to retirement income needs
6. Assess probability of meeting goals
Implement#
🤖 DRIVER Stage 3: Structured Prompt Starters
Step 1 - Implementation Planning Prompt: “Help me plan the implementation of Sarah’s retirement analysis. I need to create Python code that calculates future values for different investment scenarios and adjusts for inflation. What functions and data structures should I design? What financial formulas will I need to implement correctly?”
Step 2 - Code Development Prompt: “Help me implement the retirement calculation code step by step. Start with the basic future value of annuity formula, then add inflation adjustment. Make sure the code is well-commented and handles edge cases. Focus on making the financial logic clear and verifiable.”
Step 3 - Code Review and Enhancement Prompt: “Review my retirement analysis code for both technical accuracy and financial logic. Are there any errors in my calculations? How can I make the code more robust, readable, and educational? What additional features would make this analysis more comprehensive?”
⚠️ CODE LEARNING NOTE: The following code is intentionally simplified for educational purposes and may contain incomplete logic or potential errors. Your job is to work with your AI copilot to:
Understand each line of code and its financial purpose
Verify the mathematical calculations against financial theory
Identify any limitations or potential improvements
Test the code with different inputs to ensure it behaves correctly
Enhance the code to make it more robust and complete
Remember: Learning comes from analyzing and improving the code, not just copying it!
Code Example:
import numpy as np
import matplotlib.pyplot as plt
def retirement_projection(monthly_contribution, annual_return, years, inflation_rate):
"""
Calculate retirement accumulation with inflation adjustment
Parameters:
monthly_contribution: Monthly investment amount
annual_return: Expected annual return (decimal)
years: Investment time horizon
inflation_rate: Annual inflation rate (decimal)
"""
months = years * 12
monthly_return = annual_return / 12
# Future value of annuity formula
if monthly_return == 0:
nominal_fv = monthly_contribution * months
else:
nominal_fv = monthly_contribution * (((1 + monthly_return)**months - 1) / monthly_return)
# Adjust for inflation
real_fv = nominal_fv / ((1 + inflation_rate)**years)
return nominal_fv, real_fv
# Sarah's scenarios
monthly_contrib = 500
years = 43
inflation = 0.025
# Scenario A: CD at 3%
cd_nominal, cd_real = retirement_projection(monthly_contrib, 0.03, years, inflation)
# Scenario B: Stocks at 7%
stock_nominal, stock_real = retirement_projection(monthly_contrib, 0.07, years, inflation)
print(f"CD Option - Nominal: ${cd_nominal:,.0f}, Real: ${cd_real:,.0f}")
print(f"Stock Option - Nominal: ${stock_nominal:,.0f}, Real: ${stock_real:,.0f}")
print(f"Real purchasing power difference: ${stock_real - cd_real:,.0f}")
Financial Logic Explanation:
We use the future value of annuity formula since Sarah makes regular monthly contributions
The inflation adjustment shows purchasing power in today’s dollars
The 4% difference in returns (7% vs 3%) compounds dramatically over 43 years
AI Collaboration Prompt: “Help me enhance this retirement calculation code to include: 1) Monte Carlo simulation for uncertainty, 2) Visualization of accumulation over time, 3) Sensitivity analysis for different contribution amounts. Focus on making the code clear for investment analysis purposes.”
Robinhood Integration: “Use Robinhood to find current yields on treasury bills and the historical performance of broad market ETFs like VTI or SPY to validate our assumed returns of 3% and 7%.”
Validate#
🤖 DRIVER Stage 4: Structured Prompt Starters
Step 1 - Validation Planning Prompt: “Act as a quantitative analyst and help me design comprehensive validation tests for this retirement projection model. What historical benchmarks should I compare against? What are the most important edge cases to test? How do professional financial planners validate their retirement models?”
Step 2 - Testing Strategy Prompt: “Help me create specific validation tests for Sarah’s retirement analysis. I need to test: 1) Historical accuracy of our return assumptions, 2) Sensitivity to key variables like inflation and contribution rates, 3) Extreme scenarios like market crashes. What specific tests should I run and what results would indicate the model is reliable?”
Step 3 - Results Interpretation Prompt: “Help me interpret the validation results for my retirement model. What do the test outcomes tell me about the reliability of my analysis? What limitations should I acknowledge? How should I communicate the uncertainty in my conclusions to Sarah?”
Verification Methods:
Historical Backtesting: Compare our 7% stock assumption to actual S&P 500 returns over rolling 43-year periods
Cross-Reference: Check our inflation assumption (2.5%) against historical CPI data
Sensitivity Analysis: Test how results change with ±2% return assumptions
Sanity Check: Does the final accumulation make sense for retirement needs?
Stress Testing: Model scenarios like 2008 financial crisis or 1970s stagflation
Quality Assurance Standards:
All assumptions clearly documented and justified
Code tested with known inputs to verify calculation accuracy
Results benchmarked against professional retirement planning tools
Evolve#
🤖 DRIVER Stage 5: Structured Prompt Starters
Step 1 - Pattern Recognition Prompt: “Help me identify the core analytical patterns from Sarah’s retirement analysis that apply to other investment decisions. What is the fundamental framework we used? How does this time value + uncertainty approach extend to different types of investment problems?”
Step 2 - Application Extension Prompt: “Now that I understand this long-term compounding analysis framework, help me identify other investment contexts where this same approach applies. Consider bond valuation, real estate investing, education funding, and business valuation. What are the similarities and differences?”
Step 3 - Integration and Advancement Prompt: “Help me connect this foundational analysis to more advanced investment concepts. How does this time horizon and risk framework prepare me for portfolio construction, asset allocation, and more sophisticated investment strategies? What should I learn next to build on this foundation?”
Pattern Recognition: This time value + uncertainty framework applies to:
Bond Valuation: Future cash flows discounted at uncertain rates
Real Estate: Rental income and appreciation over time
Education Investments: Costs vs. lifetime earning benefits
Business Valuation: Uncertain future profits discounted to present
Career Decisions: Human capital investments with uncertain returns
Insurance Decisions: Premium costs vs. uncertain future benefits
Forward Connections: “Understanding how time horizon affects risk tolerance is crucial for Session 2’s vehicle selection and Session 3’s risk quantification, where we’ll determine optimal asset allocation across the investment opportunity set.”
Reflect#
🤖 DRIVER Stage 6: Structured Prompt Starters
Step 1 - Learning Synthesis Prompt: “Act as an investment mentor and help me consolidate the key lessons from this retirement analysis. What fundamental investment principles did we demonstrate? What was most important about the process we followed? How did this analysis change my understanding of risk and return?”
Step 2 - Application Planning Prompt: “Help me identify how I can apply this analytical framework to my own financial decisions and future learning. What specific next steps should I take? What other DRIVER applications would strengthen my investment analysis skills? How does this foundation prepare me for more complex investment decisions?”
Step 3 - Meta-Learning Reflection Prompt: “Help me reflect on my learning process during this DRIVER analysis. What aspects of the framework were most valuable? Which steps were most challenging? How can I improve my analytical thinking and AI collaboration for future investment problems?”
Synthesis Guidance: Key insights from Sarah’s analysis:
Time Horizon Risk: For long-term goals, inflation risk may exceed market risk
Compounding Power: Small differences in returns create enormous wealth differences over decades
Risk Redefinition: The “safe” choice may be the riskiest when adjusted for purchasing power
Analytical Framework: Systematic analysis beats intuition for complex financial decisions
AI Collaboration: Structured prompts enhance learning and analytical rigor
Next Applications: “Apply this same analytical framework to evaluate a 30-year mortgage vs. renting decision, considering both the time value of money and uncertainty in housing appreciation.”
Section 5: The Investment Game - Financial Detective Work#
Part A: Recognition Scenarios (15 minutes)#
Scenario Recognition: For each situation, identify whether this requires our DRIVER investment analysis framework:
Situation: College student choosing between a part-time job and unpaid internship
Does this need investment analysis? Why or why not?
Situation: 30-year-old deciding between contributing to 401(k) or paying off student loans
Investment decision? What’s the core trade-off?
Situation: Retiree choosing between municipal bonds and corporate bonds
Apply the risk-return framework - what’s the key analysis needed?
Part B: Full DRIVER Application (30 minutes)#
Case Study: The Graduate School Decision
Alex, 24, works as a financial analyst earning $55,000. Considering MBA programs:
Option 1: Continue working, night MBA over 3 years ($80,000 total cost)
Option 2: Full-time MBA, 2 years ($120,000 cost + $110,000 lost wages)
Post-MBA salary expectation: $95,000 starting, growing to $150,000+ over career
Your Challenge: Apply the complete DRIVER framework to analyze this as an investment decision.
🤖 Assignment Reminder: Work closely with your AI copilot throughout this analysis, using the structured prompts provided for each DRIVER stage.
Primary Deliverable: YouTube Video Presentation (8-12 minutes)
Your main assignment is a YouTube video presentation that demonstrates mastery of both financial logic and coding implementation.
Required Video Components:
Financial Analysis Section (4-6 minutes):
Clear explanation of how you framed Alex’s MBA investment decision using TVM and risk analysis
Demonstration of your financial model comparing both MBA options
Timeline visualization showing costs, benefits, and breakeven analysis
Investment recommendation with clear justification based on quantitative evidence
Technical Implementation Section (4-6 minutes):
Step-by-step walkthrough of your Python code for the investment analysis
Explanation of each major function and calculation methodology
Demonstration of code execution with real assumptions and sensitivity analysis
Discussion of limitations and potential improvements to your model
Integration & Conclusion (1-2 minutes):
How the financial model results inform your investment recommendation
What this analysis teaches about human capital investment decisions
Connection to broader investment principles from the session
Video Production Requirements:
Screen recording showing your code execution and results
Clear audio explanation of both financial concepts and technical implementation
Professional presentation suitable for investment industry communication
Upload to YouTube (can be unlisted) and submit link
Written Supplement: AI Collaboration Reflection (200 words)
🤖 AI Copilot Activity: As you prepare your reflection, ask your AI copilot: “Help me think about how AI collaboration has enhanced my understanding of investment foundations. What specific insights did I gain through our discussions? How has working with AI changed my approach to financial analysis and problem-solving?”
Along with your video, submit a brief written reflection addressing:
Most Valuable Prompt: Which specific AI prompt from this session was most helpful for your learning? Copy the exact prompt and explain why it was effective.
Prompt Improvement: How would you modify or improve that prompt for future use?
Learning Process: How did working with your AI copilot change your understanding of the investment concepts compared to working alone?
Why Video Format? Video presentations provide an excellent opportunity to demonstrate your understanding of both financial concepts and technical implementation. This format allows you to showcase your analytical thinking, communication skills, and ability to integrate theory with practice - all valuable skills for investment professionals.
Section 6: Reflect & Connect - Investment Insights Discussion#
Individual Reflection (5 minutes)#
Reflection Prompts:
How did the retirement projection change your thinking about “safe” vs. “risky” investments?
What was most challenging about explaining investment concepts to your peer partner?
Which part of the DRIVER framework felt most natural? Most difficult?
Pair Discussion (10 minutes)#
Structured Questions:
Share one insight about risk that surprised you in today’s analysis
Discuss: “When is the ‘safe’ choice actually the risky choice?”
Compare your explanations: What made financial concepts clear vs. confusing to explain?
Class Synthesis (10 minutes)#
Key Takeaways to Surface:
Investment decisions are TVM problems complicated by uncertainty
Time horizon fundamentally changes risk assessment
The biggest risk for young investors might be taking too little risk
Clear explanation requires understanding both the math and the market context
Forward-Looking Connections: Today’s foundation in risk-return trade-offs and time horizon analysis prepares us for Session 2, where we’ll explore the actual vehicles and markets where these investment decisions get implemented.
Section 7: Looking Ahead - From Investment Foundations to Investment Vehicles#
Skills Developed Today#
Applied TVM concepts to uncertain investment scenarios
Analyzed real vs. nominal returns and inflation impact
Used the investment opportunity set framework for decision-making
Explained complex financial concepts clearly to peers
Bridge to Session 2#
Now that we understand WHY we need to take investment risk and HOW time horizon affects our decisions, Session 2 addresses WHERE to implement these decisions. We’ll explore:
The Investment Implementation Challenge: “I know I need a diversified portfolio with higher expected returns than CDs, but where exactly do I put my money? Robinhood shows thousands of options - individual stocks, ETFs, mutual funds, bonds. How do I choose?”
Pattern Evolution Preview#
The risk-return framework from today becomes the lens for evaluating specific investment vehicles in Session 2. We’ll see how market structure creates the “rules of the game” that determine how our investment theories translate into practice.
Preparation for Next Session#
Review your Robinhood account setup
Think about this question: “If you had $1,000 to invest today, what specific steps would you take?”
Section 8: Appendix - Investment Solutions & Implementation Guide#
Solutions to Practice Problems#
Real vs. Nominal Returns:
Bond: (1.04/1.02) - 1 = 1.96% real return
Stock: (1.10/1.03) - 1 = 6.80% real return
REIT: (1.08/1.04) - 1 = 3.85% real return
Future Value Calculations:
Sarah’s Retirement Calculation (Python
numpy.fv): CD at 3% Annually, Monthly Contributions ($500), Payments at the end of each month, year 22 to year 65 (516 months):
import numpy as np
rate = 0.03 / 12 # Monthly interest rate
nper = 43 * 12 # Total number of periods (months)
pmt = -500 # Payment per period (negative for outflow)
pv = 0 # Present value (starting amount)
fv = np.fv(rate, nper, pmt, pv)
print(f"{fv:.4f}")
Result:
Sarah will have $525,388.63 at retirement if she saves $500/month at 3% annual interest for 43 years (payments at the end of each month, no rounding at any step).
Assessment Rubrics#
DRIVER Application Rubric:
Define & Discover (25 points)
Excellent (23-25): Clear problem framing, all variables identified, measurable success criteria
Good (20-22): Problem mostly clear, most variables identified, general success criteria
Needs Work (15-19): Vague problem statement, missing key variables or success criteria
Inadequate (0-14): No clear problem identification or success criteria
Represent (20 points)
Excellent (18-20): Clear visual representation, logical flow, appropriate pseudocode
Good (16-17): Mostly clear representation, minor gaps in logic
Needs Work (12-15): Some visualization, but unclear logical structure
Inadequate (0-11): No meaningful visual representation or logical planning
Implement (25 points)
Excellent (23-25): Working code, clear comments, correct financial calculations
Good (20-22): Mostly working code, some documentation, minor calculation errors
Needs Work (15-19): Partially working code, limited documentation
Inadequate (0-14): Non-functional code or major calculation errors
Validate (15 points)
Excellent (14-15): Multiple validation methods, clear identification of limitations
Good (12-13): Some testing, awareness of assumptions
Needs Work (9-11): Limited testing or validation
Inadequate (0-8): No systematic validation
Evolve (10 points)
Excellent (9-10): Clear pattern recognition, meaningful connections to other contexts
Good (8): Some pattern recognition, connections made
Needs Work (6-7): Limited pattern recognition
Inadequate (0-5): No meaningful pattern recognition
Reflect (5 points)
Excellent (5): Clear insights, specific next applications identified
Good (4): Some insights, general applications
Needs Work (2-3): Limited reflection
Inadequate (0-1): No meaningful reflection
Video Presentation Assessment#
Financial Explanation (40% of grade)
Investment reasoning and theoretical foundation clearly explained
Assumptions justified with market context
Risk-return analysis demonstrates understanding
Technical Explanation (40% of grade)
Code logic explained step-by-step
Data flow and calculations clearly demonstrated
Technical choices justified
Integration & Communication (20% of grade)
Clear connection between financial theory and implementation
Professional presentation suitable for investment industry
Effective use of visuals and examples
Implementation Guide for Instructors#
Robinhood Setup:
Students should use paper trading accounts initially
Focus on data lookup rather than actual trading
Emphasize platform as research tool, not just trading interface
AI Collaboration Management:
Provide example prompts but encourage student customization
Require documentation of AI interaction process
Emphasize validation of AI outputs against course material
Common Student Challenges:
Difficulty connecting TVM concepts to uncertain scenarios
Confusion between nominal and real returns
Tendency to focus on calculations rather than investment logic
Extension Resources#
Academic References:
Bodie, Kane, Marcus: “Investments” - Chapter 1 (Investment Environment)
CFA Institute: Level I readings on risk and return concepts
Industry Applications:
Morningstar.com: Asset class historical returns
FRED Economic Data: Inflation and interest rate data
Vanguard Research: Time horizon and asset allocation studies
Next Session Preparation:
Review basic market structure concepts
Explore Robinhood interface for different investment types
Read about ETF vs. mutual fund structures