FINC3017: nail every assessment, not just read the notes
Your complete guide to University of Sydney's investments and portfolio management unit. See where the marks are, work real practice questions, and study with an AI tutor that knows FINC3017.
Sia generates FINC3017 practice questions, walks through risk and modern portfolio theory step by step, and quizzes you on the material the heaviest assessments weight most heavily.
Worked example
Under the CAPM, the risk-free rate is 4% and the expected market return is 10%. A fund has an estimated beta of 0.8 and realised a return of 11% over the same period. What is the fund's Jensen's alpha?
Apply the CAPM (the Security Market Line) to get the required return: E[R] = rf + beta times (E[Rm] − rf).
Jensen's alpha is the realised return minus the CAPM-required return: alpha = 11% - 8.8% = +2.2%.
A positive alpha means the fund beat its CAPM benchmark given its systematic risk, so the answer is +2.2% (option index 2).
The trap: Comparing the realised 11% straight to the market's 10% gives +1.0% and ignores that the fund only carries beta = 0.8 of market risk, so it should be benchmarked below the market. The CAPM-required return is 8.8%, not 10%, so the true risk-adjusted outperformance is +2.2%. Using the raw market return instead of the SML-implied return is the classic alpha error. classic slip!
One exam decides 40% of your grade. Covers Topics 1 to 12; no single-component hurdle stated in the materials reviewed. This whole page is built around that.
Overview
What FINC3017 is, and where it sits
FINC3017 is the University of Sydney Business School's third-year investments and portfolio-management elective in the Discipline of Finance. It builds one integrated toolkit for analysing risky assets: returns and risk statistics, mean-variance portfolio theory and the efficient frontier, the Capital Asset Pricing Model and its empirical tests, consumption-based asset pricing and the stochastic discount factor, multi-factor models and anomalies, active-versus-passive management, performance evaluation, volatility as a traded asset, and the practical world of alternatives and machine learning. The set text is Bodie, Kane and Marcus, Investments, supplemented by a decade of asset-pricing research papers and practitioner readings.
The unit is genuinely quantitative. The early weeks revise returns, statistics and the two-asset portfolio, then the core (Weeks 4 to 6) steps up to n-asset matrix optimisation (the global minimum-variance and tangency portfolios via the covariance matrix inverse), the CAPM and its cross-sectional and time-series tests, and the Arrow-Debreu state-pricing and stochastic-discount-factor framework. The back half (Weeks 7 to 12) is broader and reading-heavy: Fama-French and Carhart factor models, tracking error and the information ratio, betting-against-beta and smart beta, the performance-metric zoo (Sharpe, Treynor, Jensen's alpha, the information ratio, Sortino, M-squared, time-weighted versus money-weighted returns), volatility risk premia and variance swaps, and alternatives and machine learning.
Assessment is two 30% online assignments (an individual one and a group one, both applying investment theory and the group one using Excel) plus a 40% closed-book final. The final is explicitly multiple choice and covers the entire course, mixing quantitative problems with conceptual reasoning, so the unit rewards a single fused map of every formula and every decision rule rather than open-book lookup. It assumes prior finance and statistics and sits as a capstone-style elective for Finance majors heading into asset-management, quantitative and analyst roles.
Official outline: sydney.edu.au · FINC3017 outline. Always treat the official outline and the exam timetable as authoritative.
Difficulty & time commitment
Is FINC3017 hard, and how much time does it take?
FINC3017 is manageable if you keep a weekly rhythm and treat the back half as the main event. Across student reviews the pattern is consistent: it starts gently and steepens, and the heaviest assessment is the part that separates grades.
A read across student reviews and course feedback. See what students say ↓
The difficulty curve and the assessment weighting point the same way: the back half is harder and worth more. Front-loading effort there is the highest-return decision in the unit.
Is this unit for you
Who tends to do well, and who tends to struggle
You will likely do well if
- You are comfortable with the quantitative backbone: matrix algebra (transpose, inverse and matrix multiplication), OLS regression and probability and statistics, which the n-asset optimisation, CAPM tests and factor regressions all lean on.
- You build and re-build the core diagrams from blank axes (the efficient frontier into the CAL and CML, the Security Market Line, the SDF and state-pricing chain, the performance-metric decision tree) until reading the right answer off them is automatic.
- You do the computational Excel tutorials by hand rather than only watching solutions, since Excel is assessable and the group assignment requires it.
- You keep weekly pace across the whole reading list, because the closed-book final covers the entire course and the back half (factor models, performance, volatility, alternatives) is broad and reading-heavy.
You may struggle if
- You are rusty on linear algebra and regression; the matrix optimisation and the cross-sectional asset-pricing tests assume them and the gap shows up fast in Weeks 4 to 6.
- You leave the entire-course closed-book MCQ final to cram, even though it spans twelve weeks of formulas, decision rules and named anomalies that cannot be looked up in the room.
- You memorise formulas instead of being able to re-derive them (the tangency weights, beta, the SDF M = psi over pi, Jensen's alpha, the variance-swap strike) under time pressure.
- You treat the two assignments as the whole game; together they are 60%, but the 40% closed-book exam tests material the assignments never touch, and there is no open book to fall back on.
- Master the Weeks 4 to 6 core early (mean-variance optimisation, the CAPM and its tests, the SDF and state pricing) so the broader back half has a foundation to stand on.
- Re-derive every result rather than memorising it: the tangency and GMV weights from the covariance-matrix inverse, beta as covariance over market variance, the market risk premium as A times sigma squared, the SDF and risk-neutral probabilities, and the performance metrics.
- Build one fused one-page map of every formula plus the which-metric, which-model and name-the-anomaly decision rules, then drill picking the right MCQ answer from it at speed, since the final is closed book.
- Work the computational tutorials in Excel until matrix operations, factor regressions and performance metrics are fast, which directly serves the group assignment and the quantitative MCQs.
Syllabus
The 12 topics, week by week
The exam-weight marker on each topic shows where the marks concentrate. The amber topics carry the highest exam weight.
T1 · Foundations: returns, statistics and utility
BKM intro; Lecture 1Simple versus log returns, arithmetic versus geometric averages, the matrix-algebra primitives used for n-asset portfolios, OLS regression with robust standard errors, and risk preferences via expected utility (CARA and CRRA, the certainty equivalent and the risk premium).
T2 · Asset classes and financial instruments
BKM Ch 2 to 3; Lecture 2Debt, equity and derivatives, index construction (price, value and equal weighting), market and order types, buying on margin and the margin call, short-sale mechanics, and futures versus forwards.
T3 · Risk, return and the two-asset portfolio
BKM Ch 5 to 6; Lecture 3Mean, variance, skewness and kurtosis, the Sharpe ratio and the equity risk premium, annualising returns and volatility by the square-root-of-time rule, portfolio weights with short positions, and the two-asset expected return and standard deviation.
T4 · Modern portfolio theory and the efficient frontier
BKM Ch 7; Lecture 4Mean-variance optimisation, the efficient frontier and the global minimum-variance portfolio, n-asset matrix optimisation, the capital allocation line and the tangency portfolio (CAL slope equals the Sharpe ratio), the two-fund theorem, and the single-index model versus full Markowitz.
T5 · The CAPM and its empirical tests
BKM Ch 9; Fama-MacBeth; Lecture 5The CAPM and the Security Market Line, beta as covariance over market variance, the equilibrium market risk premium, variance decomposition into systematic and idiosyncratic risk, and the empirical tests (portfolio sorts, time-series and two-stage cross-sectional regressions, the GRS test, Fama-MacBeth, Roll's critique).
T6 · Consumption-based asset pricing and the SDF
BKM asset-pricing readings; Lecture 6The state-space (Arrow-Debreu) model and state prices, the stochastic discount factor M = psi over pi and the pricing equation 1 = E[M(1+R)], risk-neutral probabilities, inferring state prices from option butterfly spreads, the Euler equation under CRRA, and the Merton optimal risky share.
T7 · Anomalies, APT and multi-factor models
Fama-French; Carhart; Lecture 7Cross-sectional anomalies (size, value, momentum, PEAD, profitability, investment), the Arbitrage Pricing Theory, the Fama-French three- and five-factor models and the Carhart four-factor model, and the factor-zoo cautions (data mining, overfitting, out-of-sample failure). Assignment 1 is due this week.
T8 · Active versus passive, tracking error and allocation
BKM Ch 24; Lecture 8Fama-MacBeth in a multifactor setting, state-dependent risk premia, non-performance metrics (fees, turnover, liquidity), tracking error and the information ratio, the fundamental law of active management (IR equals IC times the square root of breadth), and fixed-fraction versus volatility-managed allocation.
T9 · Anomalies and smart beta, betting-against-beta
Frazzini-Pedersen; Arnott et al; Lecture 9Rules-based factor investing and the smart-beta pipeline (factor scoring via Z-scores, construction and weighting), single and multi-factor ETFs and factor overlays, betting-against-beta and beta-neutral position sizing, alternative weighting schemes, and backtesting pitfalls.
T10 · Performance evaluation: skill versus luck
BKM Ch 24; Lecture 10Time-weighted versus money-weighted (IRR) returns, the performance-metric set (Sharpe, M-squared, Treynor, Jensen's alpha, the information ratio, Sortino) and when each applies, market-timing tests (Treynor-Mazuy and Henriksson-Merton), performance attribution and style analysis, and skill versus luck under survivorship bias.
T11 · Volatility as an asset, options and variance swaps
VIX methodology; Lecture 11Volatility as a traded, mean-reverting, insurance-like asset, the physical versus risk-neutral measure and the volatility risk premium, the VIX, the Black-Scholes Greeks (Delta, Vega, Theta, Gamma), straddles and butterflies, and the variance swap and its fair strike.
T12 · Practical and current issues: alternatives and ML
Practitioner readings; Lecture 12The alternatives universe (hedge funds, private equity, real estate, commodities, infrastructure, crypto) and its characteristics, the private-equity life cycle, and machine learning in finance (supervised versus unsupervised, the type-I versus type-II error budget, crash prediction). Assignment 2 is due this week.
How it's assessed
Assessment structure
| Component | Weight | Format & timing |
|---|---|---|
| Assignment 1 (Individual) | 30% | Individual, online submission. Apply investment theory to construct and evaluate portfolios, interpret academic research, critique asset-pricing models and strategies, and communicate clearly in writing. Assessed on both technical quantitative application and critical discussion. Due Week 7 (13 April 2026, 23:59 in the S1 2026 offering; confirm the S2 date against the current outline). No single-component hurdle stated in the materials reviewed. |
| Assignment 2 (Group) | 30% | Group, online submission. Same outcomes as Assignment 1 plus using Microsoft Excel to solve and analyse investment problems (matrix optimisation, factor regressions and performance metrics). Due Week 12 (25 May 2026, 23:59 in the S1 2026 offering; confirm the S2 date against the current outline). No single-component hurdle stated in the materials reviewed. |
| Final exam | 40% | Closed-book, multiple choice (per the Lecture 1 slide). Covers the entire course with a mix of quantitative and conceptual questions. Duration, venue and number of questions are subject to confirmation closer to the date. Held during the formal final exam period. Covers Topics 1 to 12; no single-component hurdle stated in the materials reviewed. |
- Pass on a weighted average of at least 50%. No single-component hurdle was stated in the unit materials reviewed; confirm against the current unit outline.
- Final exam: closed-book, multiple choice, entire course. Because no notes can be brought in, the quantitative questions (portfolio math, CAPM beta and alpha, the SDF and state prices, factor loadings, performance metrics, variance-swap strikes) and the conceptual ones (name-the-anomaly, which-metric, why the CAPM fails, why volatility is insurance) both have to be answered from memory at speed.
- Calculator policy: Not specified in the materials reviewed. The final is a closed-book multiple-choice exam; check the official exam instructions for the permitted calculator before the day.
This is a coursework unit. Coursework carries 60% of the grade and the final exam is the single heaviest piece at 40%, so steady work across the semester decides your result more than any one sitting. Covers Topics 1 to 12; no single-component hurdle stated in the materials reviewed.
Final exam timing: approx mid-November 2026 (S2 offering, confirm against the official exam timetable). Confirm the exact date and venue on the official exam timetable.
How to actually pass it
A weekly rhythm, two checklists, and the traps to avoid
The unit rewards consistency over cramming, and practice over re-reading. Here is the loop that works, then what to have nailed before each exam.
The weekly loop
Before the mid-semester checklist
- Lock down the quantitative core (returns and statistics, the two-asset portfolio with short positions, the efficient frontier and tangency, the CAPM and Jensen's alpha) so it is automatic before the load broadens.
- Practise the n-asset matrix optimisation in Excel (the covariance-matrix inverse for the GMV and tangency weights) ahead of the group assignment.
- Drill the CAL slope equals the Sharpe ratio and the equilibrium market risk premium equals A times sigma squared until they are instant.
- Rehearse the SDF and state-pricing chain (state prices, M = psi over pi, risk-neutral probabilities, the butterfly-spread AD price) because it is a tutorial-confirmed MCQ target.
Before the final heaviest topics
- Build and rehearse one fused one-page map covering all twelve topics, since the final is closed book and spans the entire course.
- Drill the highest-yield MCQ patterns timed: two-asset return and risk with short positions, tangency and GMV weights, CAPM beta and alpha, SDF state prices, factor-loading interpretation, performance-metric selection, betting-against-beta sizing, and variance-swap strikes.
- Practise the name-the-anomaly and which-metric conceptual questions, because the exam mixes them with the quantitative ones.
- Re-derive each asset-pricing result fast (the SML, the SDF pricing equation, the FF3 versus Carhart versus FF5 factor sets) rather than relying on recognition.
- Convert volatility facts into reflexes: the volatility risk premium is implied minus realised, the variance-swap fair strike equals the risk-neutral expected variance, and the VIX daily move is roughly the VIX over the square root of 252.
The mistakes that cost marks
Benchmarking against the market instead of the SML. Comparing a fund's realised return straight to the market return ignores its beta. Jensen's alpha benchmarks against the CAPM-required return (rf + beta times the market risk premium), so a low-beta fund must clear a lower bar than the market. Using the raw market return is the most common alpha error.
Cramming the closed-book final. The 40% final is closed book and covers the entire course. Nothing can be looked up in the room, so leaving the broad back half (factor models, performance metrics, volatility and alternatives) to the last week rarely works; the unit rewards a single fused map built up weekly.
Treating the assignments as the whole grade. The two 30% assignments are 60% combined, but the 40% closed-book exam tests quantitative and conceptual material the assignments never fully cover. Polishing assignment marks while neglecting exam-style MCQ practice misreads where the remaining marks sit.
Skipping the Excel computational tutorials. Excel is assessable and the group assignment requires it. Watching the computational solutions without doing the matrix operations, factor regressions and performance calculations yourself leaves both the assignment and the quantitative MCQs harder than they need to be.
Teaching team
Who teaches FINC3017
The bios below are factual. The star ratings are not ours: they are impressions from students who have taken the unit, so you can hear from people who sat in the lectures.
Associate Professor Andrew Grant
Associate Professor in the Discipline of Finance, University of Sydney, and coordinator and sole lecturer of FINC3017 for S1 2026. Research in behavioural finance, individual investor decision making and betting markets, focusing on preference- and belief-based asset allocation and asset pricing.
Teaching team as listed in the unit materials reviewed. AskSia does not rate lecturers; star ratings are submitted by students who have taken FINC3017.
Formula & concept sheet
The vocabulary and formulas you must own
- Log return
- r = ln(1 + R) = p_t − p_(t-1). Log returns are additive across time, so the multi-period log return is the sum of the single-period log returns; the geometric average is at most the arithmetic average.
- Sharpe ratio
- Sharpe = (mean return − rf) / sigma: excess return per unit of total risk. Volatility and the Sharpe ratio scale by the square root of time; variance scales linearly with time.
- Two-asset portfolio risk
- sigma_p = sqrt(w1^2 sigma1^2 + w2^2 sigma2^2 + 2 w1 w2 sigma1 sigma2 rho). The diversification benefit grows as the correlation rho falls; negative weights represent short or borrowed positions.
- Tangency portfolio and the CAL
- The capital allocation line runs from rf through the tangency portfolio; its slope equals the Sharpe ratio. The tangency weights are proportional to the covariance-matrix inverse times the excess-return vector, then normalised to sum to one.
- Global minimum-variance portfolio
- w_GMV is proportional to the covariance-matrix inverse times the vector of ones, normalised; its variance is 1 / C where C = 1' Sigma^(-1) 1. It is the leftmost point of the efficient frontier.
- CAPM (Security Market Line)
- E[Ri] = rf + beta_i (E[Rm] − rf), with beta_i = Cov(Ri, Rm) / sigma_m^2. Only systematic risk is priced; idiosyncratic risk is not. The equilibrium market risk premium equals A times sigma_m^2.
- Jensen's alpha
- alpha = realised return − [rf + beta (E[Rm] − rf)], the intercept of the excess-return regression. Alpha equals zero under the CAPM; a positive alpha is abnormal outperformance given systematic risk.
- Stochastic discount factor
- M_s = psi_s / pi_s, where psi_s is the state price and pi_s the physical probability. Any asset prices as p = E[M times payoff], and 1 = E[M(1 + Ri)]. The SDF is high in bad states (high marginal utility), so assets paying off then are valuable and carry lower required returns.
- Risk-neutral probability and the AD butterfly
- Risk-neutral pi-tilde_s = psi_s / B, where B is the risk-free bond price and 1 + rf = 1 / B. An Arrow-Debreu claim paying $1 at level k is replicated by a call butterfly: long c(k-1) + long c(k+1) − 2 short c(k).
- Fama-French and Carhart factors
- FF3: Ri − rf = alpha + bM(Rm − rf) + bS times SMB + bH times HML. Carhart adds MOM (momentum); FF5 adds RMW (profitability) and CMA (investment). A non-zero alpha means the factor model fails to explain the portfolio.
- Tracking error and the information ratio
- Tracking error is the standard deviation of the active return (the residual from regressing on the benchmark). Information ratio IR = alpha / tracking error. The fundamental law of active management is IR = IC times sqrt(breadth).
- Performance metrics
- Sharpe uses total risk, Treynor = (mean − rf) / beta uses systematic risk, Jensen's alpha is versus the CAPM, the information ratio is versus a benchmark, and Sortino uses downside deviation. M-squared converts the Sharpe ratio to a return at market risk.
- Volatility risk premium and the variance swap
- VRP = implied (risk-neutral) volatility − realised (physical) volatility, positive on average, so the seller earns the premium. A variance swap pays (realised variance − strike) times notional; the fair strike equals the risk-neutral expected variance and underlies the VIX construction.
Common acronyms: MPT · GMV · CAL · CML · SML · CAPM · SDF · AD · APT · FF3 · FF5 · MOM · SMB · HML · RMW · CMA · IR · IC · TE · TWR · MWR · VRP · VIX · BAB · CRRA · CARA.
What students say
What students actually say about FINC3017
Recurring themes from student reviews, paraphrased in our own words.
- Described as one of the more technically demanding third-year finance electives, with matrix algebra, regression-based asset-pricing tests and a dense formula set.
- Manageable for students with solid prior finance and statistics; harder for those rusty on linear algebra and OLS regression, which the optimisation and CAPM-test material assumes.
- The content steps up sharply in the portfolio-theory and asset-pricing core (Weeks 4 to 6), then broadens across factor investing, performance and volatility.
- The unit follows Bodie, Kane and Marcus closely and layers on a reading list of asset-pricing papers that the assignments ask students to interpret.
- Students build condensed formula-and-decision maps to prepare for the closed-book multiple-choice final, since notes cannot be brought in.
- Demand for worked walkthroughs of the high-yield quantitative patterns (portfolio optimisation, CAPM alpha, the SDF and state prices, performance metrics) and for clear explanations of the conceptual asset-pricing material.
Recurring student opinions, paraphrased and aggregated, not official course information.
Set texts
The prescribed reading
The syllabus references map straight onto these.
Asset-pricing research and practitioner readings (Fama-French, Fama-MacBeth, Frazzini-Pedersen, smart-beta and volatility readings)
Various.
Where it fits
Prerequisites, related units & why it matters
A third-year finance elective that assumes prior finance and statistics (returns, regression and basic portfolio ideas). Confirm the exact prerequisite units against the current University of Sydney unit outline.
Your FINC3017 study toolkit
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FAQ
Frequently asked questions
Is FINC3017 hard?
It is one of the more demanding third-year finance electives because it is genuinely quantitative: matrix-algebra portfolio optimisation, the CAPM and its cross-sectional tests, the stochastic discount factor and state pricing, factor regressions and variance-swap strikes. The load is spread across two 30% assignments and a 40% closed-book exam rather than concentrated in one sitting, and there is no stated hurdle, so it is very manageable with prior finance and statistics and consistent weekly practice, but it punishes thin coverage on the entire-course final.
How is FINC3017 assessed?
Two 30% online assignments (an individual one and a group one, the group one using Excel to solve investment problems) plus a 40% closed-book final exam. The final is multiple choice and covers the entire course with a mix of quantitative and conceptual questions. You pass on a weighted average of at least 50%, with no single-component hurdle stated in the materials reviewed.
What is the final exam format?
Per the Lecture 1 slide, the final is a closed-book, multiple-choice exam held during the formal exam period that covers the entire course and mixes quantitative and conceptual questions. The duration, venue and number of questions are disclosed closer to the date, so check the official exam timetable and instructions, including the permitted calculator, before the day.
How much maths is involved?
A lot, relative to earlier finance units. You work with matrix algebra for n-asset portfolios (the covariance-matrix inverse for the global minimum-variance and tangency portfolios), CAPM and Fama-MacBeth regressions, the stochastic discount factor and Arrow-Debreu state prices, factor-model regressions, the full performance-metric set, and variance-swap fair strikes. Excel is heavily used and is assessable, especially in the group assignment.
Do I need to be good at Excel?
Yes. The unit states Excel is heavily used and is part of the assessable material, and the group assignment explicitly requires using Excel to solve and analyse investment problems. Practising matrix operations (MMULT, TRANSPOSE, MINVERSE and array formulas), regressions and performance calculations in Excel through the computational tutorials is important.
What textbook does it use?
The set text is Investments by Bodie, Kane and Marcus, available as a free eBook through the University library. It is supplemented by a reading list of asset-pricing research papers (for example Fama-French and Fama-MacBeth) and practitioner readings on smart beta and volatility, which the assignments ask you to interpret and critique.
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