FINC3017 Investments and Portfolio Management

How FINC3017 is assessed

What FINC3017 covers

The whole subject → one exam-ready map. Each topic links to its free chapter guide.

The 30/30/40 split is confirmed by both the Lecture 1 slide and the Canvas Assignment page; the exam date, duration, venue and any hurdle are subject to confirmation ("details will be disclosed closer to the date"). Because the final is closed-book MCQ, marks come from holding the right formula and the right which-model / which-metric reasoning in your head — confirm the exam date and any hurdle in your unit outline.

Worked example · free

CAPM required return and Jensen's alpha — did the manager add value?

Key terms

Efficient frontier

The set of portfolios that give the maximum expected return for each level of risk (the upper branch of the mean-variance opportunity set). Below the global minimum-variance portfolio the lower branch is inefficient. With a risk-free asset, the best portfolio on the frontier is the tangency portfolio.

Capital Market Line (CML) vs Security Market Line (SML)

The CML plots expected return against total risk (σ) and runs from rf through the tangency / market portfolio — it prices efficient portfolios. The SML plots expected return against systematic risk (β) and prices every asset, efficient or not: E[R] = rf + β(E[R_m] − rf).

Beta (β)

An asset's systematic-risk measure, β = Cov(R_i, R_m)/σ_m². Under the CAPM only beta is priced; idiosyncratic risk is diversified away and earns no premium. Beta is the slope of the asset's regression on the market (the characteristic line).

Stochastic discount factor (SDF)

The pricing kernel M = ψ/π that prices every asset through p = E[M·X], equivalently 1 = E[M(1 + R_i)]. M is high in bad states (high marginal utility / low consumption), so assets that pay off in bad times are valuable and carry a lower required return.

Tracking error and information ratio

Tracking error is the standard deviation of a portfolio's return relative to its benchmark, σ(ε). The information ratio IR = α/σ(ε) is active return per unit of tracking error; the fundamental law decomposes it as IR = IC × √breadth (skill times the number of independent bets).

FINC3017 FAQ

Questions students ask in each FINC3017 chapter

When do I use log returns instead of simple returns?

Use log returns when you need to add returns across time, because a multi-period log return is just the sum of the single-period log returns. Use simple (arithmetic) returns when you combine assets within a portfolio at one point in time, because a portfolio's simple return is the weighted sum of the assets' simple returns. Mixing the two up is a classic exam trap. Chapter 01 · Foundations: Returns, Statistics & Utility

Why is a short sale's margin call triggered by a price rise rather than a fall?

When you short, you owe shares, so your liability grows as the price climbs. Your equity is the account balance minus the cost to buy the shares back, and a rising price shrinks that equity. The margin-call price is therefore above your entry price, the mirror image of a long position where the call comes from the price falling. Chapter 02 · Asset Classes, Instruments & Market Mechanics

Why does a portfolio's standard deviation fall below the weighted average of the two assets' volatilities?

Because the covariance term is less than the product of the volatilities whenever correlation is below +1. Variance is the weighted sum of the own-variances PLUS a cross term scaled by ρ, so any ρ < 1 makes the portfolio less risky than a naive average would suggest. Only at ρ = +1 do the volatilities add up linearly with no diversification gain. Chapter 03 · Risk, Return & the Two-Asset Portfolio

What is the difference between the CAL and the CML?

The capital allocation line connects the risk-free asset to ANY chosen risky portfolio, while the capital market line is the specific CAL drawn through the tangency (market) portfolio — the one with the highest Sharpe ratio. Under CAPM equilibrium the tangency portfolio is the market portfolio, so the CML is the best possible CAL everyone can access. Chapter 04 · Modern Portfolio Theory & the Efficient Frontier

Why does only beta, not total volatility, determine an asset's required return under the CAPM?

Because a well-diversified investor can eliminate idiosyncratic risk for free, the market refuses to pay a premium for it. Only the systematic component — how an asset co-moves with the market, measured by beta — cannot be diversified away, so the SML rewards beta alone and an asset's own volatility is irrelevant to its equilibrium expected return. Chapter 05 · The CAPM & its Empirical Tests

What is the intuition behind the stochastic discount factor being high in bad states?

In a bad state your consumption is low, so an extra dollar there is worth a lot — marginal utility is high — and the SDF M is exactly that scaled marginal utility. An asset that pays off in bad states is therefore valuable insurance, gets a high price, and earns a low required return; the SDF formalises this 'pay when it hurts most' logic. Chapter 06 · Consumption-Based Asset Pricing, State Prices & the SDF

What is the difference between the APT and a specific model like Fama-French?

The APT is a general framework that says expected returns are linear in some set of systematic factors, but it stays silent on what those factors are. Fama-French (and Carhart, FF5) are concrete implementations that name the factors — market, size, value, momentum, profitability, investment — and supply the data to estimate the premiums, so they are testable special cases of the APT idea. Chapter 07 · Anomalies, APT & Multi-Factor Models

When should I use the information ratio instead of the Sharpe ratio?

Use the information ratio when you are judging an active manager against a specific benchmark, because it measures benchmark-relative alpha per unit of tracking error. Use the Sharpe ratio when you care about total, standalone risk relative to the risk-free rate. The IR is essentially the benchmark-relative version of the Sharpe ratio. Chapter 08 · Active vs Passive, Tracking Error & Allocation

How is smart beta different from both passive indexing and active management?

Smart beta keeps the passive virtues — transparent, rules-based and low-cost — but deliberately departs from cap-weighting to tilt toward factors like value or low volatility, which is an active decision. So it is a middle ground: the systematic, disclosed rules of indexing combined with the factor bets of active management, without a manager's discretion. Chapter 09 · Factor Investing, Smart Beta & Betting-Against-Beta

When do I use Sharpe versus Treynor?

Use the Sharpe ratio when the portfolio is the investor's entire holding, because total risk (σ) is what they bear. Use the Treynor measure when the portfolio is just one sleeve of a larger, well-diversified portfolio, so only its systematic (beta) risk is relevant. Both reward excess return, but they divide by different risk measures, which can rank funds differently. Chapter 10 · Performance Evaluation: Skill vs Luck

Why does volatility behave like insurance?

Volatility tends to spike exactly when markets fall, so a long-volatility position pays off in crashes — like an insurance policy. Investors will pay up for that protection, which is why implied volatility sits above realised volatility on average (the volatility risk premium) and why being long volatility has a negative expected long-run return: you are paying the insurance premium. Chapter 11 · Volatility as an Asset, Options & Variance Swaps

Why do investors add alternatives despite their high fees and illiquidity?

The main draw is diversification: many alternatives have low correlation with public stocks and bonds, so adding them can lower overall portfolio risk and shift the efficient frontier outward. Investors accept the illiquidity, higher fees and valuation complexity as the price for that diversification and for access to return sources unavailable in public markets — but the exam expects you to weigh both sides. Chapter 12 · Practical & Current Issues: Alternatives & Machine Learning

How to study for the exam

Treat FINC3017 as a single connected map, not twelve separate topics, because the closed-book MCQ final can pull a quantitative or conceptual question from any week. (1) Build the spine first: returns and utility (Week 1) → two-asset risk-return (Week 3) → efficient frontier, CAL/CML and the tangency portfolio (Week 4) → the CAPM and the SML (Week 5). If you can move along that chain fluently, half the exam is reflexes.

(2) For every model, store one formula plus one decision rule — e.g. SML for required return, M = ψ/π for the SDF, IR = α/σ(ε) for active skill — so an MCQ triggers the right tool instantly.

(3) Drill the tutorial-confirmed quant patterns: two-asset μ_p / σ_p with short (negative) weights, tangency / GMV weights, CAPM alpha, Arrow-Debreu state prices and butterfly pricing, Euler-equation two-period consumption, betting-against-beta sizing, and the performance-metric showdown (Sharpe vs Treynor vs Jensen vs IR vs Sortino).

(4) Learn the conceptual decision trees: which performance metric when, which factor model when the alpha is non-zero, and name-the-anomaly with its risk-vs-behavioural explanation. (5) Use the two Excel assignments as live rehearsal — matrix optimisation in Assignment 2 cements the exact n-asset algebra the exam abstracts.

(6) Confirm the exam date and format in your unit outline early, since the source flags them as subject to confirmation.

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