FINC3017 · Investments And Portfolio Management
The Complete Exam Bible · S1 2026Investments and Portfolio Management
FINC3017 Investments and Portfolio Management is the University of Sydney's third-year capstone on how risky assets are priced and how portfolios are built and judged — from Markowitz mean-variance optimisation and the CAPM, through consumption-based asset pricing and the stochastic discount factor, multi-factor models and market anomalies, to performance evaluation, volatility trading and alternatives, all built on the Bodie-Kane-Marcus text.
It is assessed by a 30% individual assignment (due 13 April 2026), a 30% group Excel assignment (due 25 May 2026), and a closed-book, multiple-choice final exam worth 40% that covers the entire course with a mix of quantitative and conceptual questions. 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.
What FINC3017 covers
The whole subject → one exam-ready map. Each topic links to its free chapter guide.
How FINC3017 is assessed
| Component | Weight | Format |
|---|---|---|
| Assignment 1 (Individual) | 30% | Individual; online submission; due 13 April 2026 23:59; apply investment theory to construct/evaluate portfolios, interpret academic research and critique asset-pricing models and strategies; assessed on technical quantitative application AND critical written discussion |
| Assignment 2 (Group) | 30% | Group; online submission; due 25 May 2026 23:59; same outcomes as Assignment 1 PLUS use Microsoft Excel to solve and analyse investment problems |
| Final exam | 40% | Closed-book, multiple-choice (per the Lecture 1 slide); held during the S1 2026 final exam period; covers the ENTIRE course; a mix of quantitative and conceptual questions; duration, venue, number of questions and any pass-the-exam hurdle are subject to confirmation — confirm in your unit outline |
CAPM required return and Jensen's alpha — did the manager add value?
- 3 marks(a) Apply the Security Market Line: E[R_Z] = rf + β(E[R_m] − rf). Substitute the numbers: E[R_Z] = 4% + 0.5 × (10% − 4%) = 4% + 0.5 × 6% = 4% + 3% = 7%.
- 2 marks(b) Jensen's alpha is the realised return minus the CAPM-required return: α = R_Z − E[R_Z] = 9.5% − 7% = +2.5%.
- 1 markInterpret: α = +2.5% > 0, so Fund Z beat the return its systematic risk (β = 0.5) demanded. A low beta paired with a positive alpha is the signature of a defensive or market-neutral strategy that earned return without taking much market risk.
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
Is FINC3017 hard?
FINC3017 is a demanding third-year finance elective: it fuses portfolio math (matrix optimisation, CAPM beta, SDF / state pricing, factor regressions, variance-swap strikes) with a wide conceptual sweep (anomalies, smart beta, active-vs-passive, why CAPM fails, why volatility behaves like insurance), and the set text is the whole of Bodie-Kane-Marcus plus a decade of asset-pricing papers. It is manageable if you keep up week by week, because the closed-book multiple-choice final rewards holding one fused map of every formula and every decision rule in your head, rather than memorising any single derivation.
Is the final exam closed-book, and is it a hurdle?
The Lecture 1 slide states the final is a closed-book, multiple-choice exam worth 40% that covers the entire course with a mix of quantitative and conceptual questions. No single-component hurdle is stated in the available course materials, and the exam date, duration, venue and number of questions are listed as "details will be disclosed closer to the date" — so confirm the exam date and any pass-the-exam rule in your unit outline before you rely on them.
What do the two assignments cover and when are they due?
Assignment 1 is an individual task worth 30%, due 13 April 2026: you apply investment theory to build and evaluate portfolios, interpret academic research and critique asset-pricing models, and you are marked on both quantitative application and critical written discussion. Assignment 2 is a group task worth 30%, due 25 May 2026: the same outcomes plus you must use Microsoft Excel to solve and analyse investment problems.
How much maths and Excel does the course assume?
A lot. Week 1 sets up returns (simple vs log), matrix algebra (transpose, inverse, Cholesky) for n-asset portfolios, OLS with White standard errors, and expected-utility risk aversion (CARA/CRRA). From there you optimise portfolios with Σ⁻¹, derive the CAPM and SDF, run factor regressions and price variance swaps. Assignment 2 is explicitly Excel-based (MMULT, MINVERSE, array formulas), so being fluent in matrix functions and regression in Excel is a real advantage.
What is the single most examinable idea across the whole unit?
The chain that links risk to required return: diversification pushes you to the efficient frontier, a risk-free asset gives the CAL/CML and a single tangency portfolio (two-fund theorem), and in equilibrium the CAPM prices each asset on the SML by its beta. Everything later either tests that chain (consumption-based SDF, multi-factor models that add factors when the CAPM alpha is non-zero) or judges a portfolio against it (Sharpe / Treynor / Jensen / information ratio). Expect MCQs that ask you to pick the right model or the right performance metric for a described situation.
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.
