Summary of "מכון לב יסודות המימון ב' שעור 1 10.3.2026 עמי מי-טל, רו"ח MBA"

Finance-focused summary (statistics as risk/return framework)

Course / evaluation / logistics (context)

• Course format: Excel-based workflow plus a course website containing:
  • syllabus
  • formula sheet
  • presentations
  • uploaded lesson recordings/files
• Grading:
  • 90% tests
  • 10% submission of exercises
• Exercises:
  • cumulative (each set builds on prior material)
  • points are awarded even if not fully solved
• Test format: “All American” style (likely multiple-choice), 20 questions, 3 hours
• Disclosures: no explicit statements such as “not financial advice” appear (though this is described as educational coursework)

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Core finance methodology covered: mapping macro states → asset returns → expected return & risk metrics

The instructor’s examples convert uncertain economic states into probabilistic asset payoffs, then derive common risk/relationship measures used in investing.

Step-by-step framework (as taught)

1. Define economic scenarios (“world states”) and assign probabilities to each
   • Example probabilities used: 10% / 75% / 10% (deep recession / business-as-usual / recession)
2. For each asset, specify payoff/return under each scenario
3. Compute:
   • Expected return (“life expectancy” / expected value)
     • probability-weighted average of returns
   • Variance
     • probability-weighted squared deviation from the mean
   • Standard deviation (σ)
     • σ = √variance
   • Coefficient of variation (CV)
     • CV = standard deviation ÷ mean (used to compare risk relative to expected return)
   • Covariance between two risky assets
     • measures how returns move together
   • Correlation coefficient (ρ) (“bed coefficient”)
     • covariance normalized by standard deviations, ranging -1 to +1

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Key quantitative example #1: Expected return, variance, standard deviation, CV

Assets / tickers mentioned

• Apple (AAPL)
• “Gross” (transcript unclear; treated as the second asset)

Economic states and probabilities (example)

• Forecast horizon mentioned: 2027 (illustrative “next year” example)
• Probabilities:
  • 10% deep recession
  • 75% business as usual / moderate
  • 10% recession
    • note: sums appear inconsistent due to subtitle/probability transcription errors, but the instructor’s return-table arithmetic is emphasized

Payoff/return assumptions used in the example

Apple

• Deep recession: -40%
• Business as usual: +10%
• Recession: -30% (subtitle order unclear, but included in the weighted-average calculation)
• Instructor’s repeatedly stated expected return for Apple: 6.5%

Second asset (“Gross”)

• Deep recession: -5%
• Business as usual: +5%
• Recession/downturn: +10%
• Expected return for second asset: 5%

Explicit expected return computation result

• Apple expected return: 6.5%
• Second asset expected return: 5%

Risk comparison logic

• “Risk” is explained as uncertainty / spread (not strictly “probability of loss”)
• Apple is described as riskier because payoff swings are larger:
  • Apple shows larger magnitude gaps (e.g., -40% vs +40% style swing discussion)
  • the other asset shows smaller gaps (e.g., roughly -5% to +10% range)

Variance / standard deviation

• Instructor provides a numeric variance for Apple (despite subtitle confusion):
  • Variance(Apple) = 0.03 (stated)
• Standard deviation is taught as:
  • σ = √variance
• A relative comparison is mentioned qualitatively (e.g., Apple being “almost 7 times the risk”), though exact numbers are garbled in the transcript.

Coefficient of variation (CV)

• Example CV interpretation given:
  • one asset: CV ≈ 0.12
  • the other asset: CV ≈ 0.14
• Interpretation:
  • lower CV is preferred when comparing “risk per unit of expected return”
• Instructor conclusion in the CV illustration:
  • prefer the asset with CV = 0.12 (described as “Stock A over Stock B”)
• Later, with corrected-looking numbers, the instructor also shows that the second asset can be better under CV (lower risk per unit of profit than Apple), using stated partial results:
  • Apple: ~2.9 (risk per unit)
  • Gross: ~0.54 (risk per unit)
  • conclusion: second asset better than Apple under CV

Important caution (implicit)

• CV is described as appropriate for a risk-averse investor
• It is emphasized that variance/standard deviation can sometimes lead to similar conclusions, but CV helps when means differ

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Key quantitative example #2: Covariance → correlation coefficient

Covariance definition / intuition

• Covariance measures whether two assets move together:
  • “If bull rises, bear rises; if bull falls, bear falls” → positive relationship
• Notation used:
  • Cov(X, Y) or σ_xy
• Key emphasis:
  • covariance reflects joint movement direction/strength, but is not normalized for scale

Correlation coefficient (“bed coefficient” / “correlation coefficient”)

• Ranging: -1 to +1
  • +1: identical co-movement (strong positive)
  • -1: perfect opposite movement
  • 0: no connection
• Instructor’s Apple-vs-other correlation result:
  • shown as ~0.776 (transcript shows something like “0.0776,” but later treatment suggests a strong positive coefficient in the -1 to +1 range)
• Caveat:
  • correlation is based on historical/estimated data
  • even with positive correlation, next-year outcomes can still diverge:
    • “It doesn’t prevent one stock from doing this next year…”
    • statistics reflect the past / estimates

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Key quantitative example #3: Scenario analysis with percentage returns

Scenario probabilities and instruments

• Two companies/sheets:
  • C = military company
  • D = tourism company
• Initial price for both: 150 shekels (NIS)
• War/adverse state probability: 15%
• Payoffs under war:
  • Stock C: 150 → 250
    • profit: +100 NIS (about +66% on 150)
  • Stock D: 150 → 60
    • loss: -90 NIS (about -60% on 150)

Stable-state percentage conversions (illustrative)

The instructor also illustrates converting price changes into percentages, e.g.:

• Stability example:
  • 150 → 160 (about +6%)
  • 150 → 170 (about +13%)
• Loss example:
  • 150 → 100 (loss of 50, about -33%)

Explicit recommendation/teaching point

• Translate price moves into percentage returns:
  • Profit % = (Price change ÷ Initial price)
• Mentioned assignment formatting:
  • one table requires only the percentages without the raw numbers (due to how the assignment is set up)

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Assignments / exercises (timeline)

• Next week: two exercises required:
  1. Do the “rough and sweet” version (likely Apple and Gross) from the beginning (same type solved in class)
  2. Solve the “military share and the tourism share” (C and D) using the percentages
• An assignment file is provided/uploaded, with partial solutions shown for exercise 2
• Practice exams throughout the semester are mentioned

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Disclosures / disclaimers

• No explicit “not financial advice” disclaimer appears in the subtitles provided
• The instructor repeatedly notes that:
  • results depend on scenario/probability-based assumptions
  • statistics are estimates

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Presenters / sources

• Ema(mi) Mi-Tal, CPA, MBA (עמי מי-טל, רו”ח MBA)

Category

Finance

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