Measuring Risk and Expected Return

Risk, Return & CAPM: A Visual Guide

Decoding Investment

A Visual Guide to Risk, Return, and CAPM

The Fundamental Trade-Off

In finance, risk and return are intrinsically linked. To achieve higher potential returns, an investor must be willing to accept a greater level of uncertainty. This principle governs all investment decisions.

Low Risk
High Return

Quantifying Total Return

An investment’s performance is measured by its Holding Period Return (HPR), which combines income generated (like dividends) and the change in the asset’s price (capital gain).

Visualizing Risk & Expected Return

Risk is quantified by Standard Deviation (σ), measuring return volatility. For many assets, returns are assumed to follow a normal distribution (a bell curve) around the Expected Return E(R).

The Power of Diversification

Modern Portfolio Theory shows that combining different assets in a portfolio can reduce total risk. This process, known as diversification, is often called “the only free lunch in finance.”

As more stocks are added to a portfolio, firm-specific (unsystematic) risk is dramatically reduced. However, market-wide (systematic) risk remains, as it affects all assets.

Systematic Risk

Market-wide risk that cannot be diversified away (e.g., interest rates, recession).

Unsystematic Risk

Company or industry-specific risk that can be eliminated through diversification (e.g., a product recall).

Pricing Risk: The Capital Asset Pricing Model (CAPM)

E(Ri) = Rf + βi [E(Rm) – Rf]

The CAPM provides a formula to calculate the required return for an asset. It states that the return equals the risk-free rate plus a premium for the asset’s systematic risk.

β > 1

Aggressive

More volatile than the market. Higher potential returns and losses.

β = 1

Market

Moves in line with the overall market.

β < 1

Defensive

Less volatile than the market. Lower potential returns and losses.

The Security Market Line (SML)

The SML is the graphical representation of the CAPM. It plots the expected return of an asset against its systematic risk (Beta). It provides a benchmark to determine if an asset is fairly priced.

Underpriced (Good Buy)
Fairly Priced
Overpriced (Potential Sell)

Beyond CAPM: Advanced Perspectives

Multi-Factor Models

While CAPM uses a single factor (market risk), models like the Fama-French Three-Factor Model propose that other factors also explain returns.

CAPM
Market Risk (β)
Fama-French
Market Risk (β)
Company Size (SMB)
Value Premium (HML)

Challenges in Real Estate

Applying CAPM to illiquid assets like real estate is complex. Beta is difficult to calculate accurately due to infrequent trading and reliance on appraisal-based data, which can understate true volatility and risk.

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An infographic based on the foundational work in finance by Sharpe (1964), Markowitz (1952), Fama & French (1993), and others.

Built for educational purposes.

Risk & Return — Educational Infographic (Sharpe, Markowitz, Fama–French)

Risk & Return — An Educational Infographic

Based on foundational work by Sharpe (1964), Markowitz (1952), Fama & French (1993), and others.
Built for educational purposes.

#RiskReturn #MPT #CAPM #Diversification Finance for Academia

The Inseparable Link Between Risk and Return

In finance, risk and return are two sides of the same coin. Higher potential return requires accepting higher risk—there is no way around this trade-off.

Risk is the uncertainty around future outcomes—the chance that realized returns differ from what is expected (Bodie, Kane & Marcus, 2018). Most investors are risk-averse: they do not avoid all risk, but they demand compensation for bearing it.

Principle: More risk → higher required return.

Classroom intuition: people accept an uncertain coin-flip only when the upside is sufficiently large.

Concept sketch

Expected Return Risk (volatility)

Illustrative only: not real data.

How Do We Measure Return?

Holding Period Return (HPR) summarizes the total payoff over the holding interval, with two components:

1) Income (Dividend/Coupon) Yield

Cash income generated by the asset relative to its price.

2) Capital Gain Yield

Price appreciation (or depreciation) over the period.

Decomposition: Total Return = Income Yield + Capital Gain Yield

Expected Return, E(R), is a probability-weighted average of possible outcomes (Hillier et al., 2016). It represents the long-run average if the investment were repeated many times.

Quantifying Risk: Standard Deviation (σ)

Standard deviation measures the dispersion/volatility of returns around the expected value.

  • High σ: wide swings → higher risk.
  • Low σ: stable outcomes → lower risk.

In many applications, returns are approximated as normally distributed, enabling probabilistic statements about ranges of outcomes.

Volatility snapshot

Asset A (low σ)

Asset B (high σ)

Relative volatility (illustrative)

The Power of Diversification

Modern Portfolio Theory (Markowitz, 1952) shows that combining assets with imperfect correlations can reduce total portfolio risk without sacrificing expected return.

Two Risk Types

  • Unsystematic (Idiosyncratic) Risk: firm/industry-specific shocks (product recall, legal events). Diversifiable.
  • Systematic (Market) Risk: economy-wide shocks (rates, recessions, inflation). Not diversifiable.
Key insight: Diversification largely removes unsystematic risk, but systematic risk remains and must be priced.

Risk decomposition (illustrative)

Total Risk # of Holdings Few More Many
Unsystematic (diversifiable) Systematic (market) — remains

References

  1. Bodie, Z., Kane, A. and Marcus, A.J. (2018) Investments. 11th edn. New York: McGraw-Hill Education.
  2. Hillier, D., Ross, S., Westerfield, R., Jaffe, J. and Jordan, B. (2016) Corporate Finance. 3rd European edn. London: McGraw-Hill.
  3. Markowitz, H. (1952) ‘Portfolio Selection’, The Journal of Finance, 7(1), pp. 77–91.
  4. Sharpe, W.F. (1964) ‘Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk’, The Journal of Finance, 19(3), pp. 425–442.
  5. Fama, E.F. and French, K.R. (1993) ‘Common Risk Factors in the Returns on Stocks and Bonds’, Journal of Financial Economics, 33(1), pp. 3–56.
Categories Finance for Academia

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