MSc in Financial Economics Open Day September 2026

Main ideas, concepts, and lessons conveyed

1) Event purpose and structure (Open Day agenda)

The video is an online MS in Financial Economics (MFE) Open Day. It’s recorded and shared later so prospective students can rewatch it.

A structured agenda is followed:

• Short introductions
• A taster lecture by a senior finance professor (Ken)
• Career development presentation by the Career Development Center (Ben)
• Alumni experience and Q&A (Reuben)
• Admissions overview (Akbar + Alex)
• Additional admissions Q&A to address applicant questions

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2) Program landscape: MFE vs the new MAFE (Masters in Applied Financial Economics)

The program team explains a newly launched parallel track.

MAFE (new)

• Format: Part-time
• Duration: 2 years (while working)
• Teaching style: Significant online teaching
• Structure: Four residential periods across two years
• Career focus: More professional/project-oriented
• Target audience: Typically more advanced in their careers
  • Usually 2+ years work experience
• Projects: More professionally oriented projects than the MFE

MFE (existing)

• Format: Full-time
• Duration: 9 months
• Location: In Oxford for the duration
• Teaching style: On-site, spanning three terms
• Target audience: Usually early career
  • Typically 0–2 years experience
  • Often about ~6 months internship experience

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3) MFE program timeline and academic structure

The academic and career rhythm is outlined term-by-term.

Pre-arrival (July)

• Career events on weekends
• Goal: help students “hit the ground running” for early job application timing

Term 1 (starts October; “Miklmas”)

• Careers week in early September:
  • workshops
  • industry advisors
  • London employer trek
• Launch week includes an early short course:
  • Ethics and Finance
• Core course structure:
  • Five core courses early in the program, with Ethics and Finance first

Term 2 (after Christmas; “Hillary”)

• Continuation of core courses

Term 3 (Trinity)

• Elective selection, which can be:
  • MFE-only, or
  • Joint electives with:
    • MBA
    • MLF (Master in Law and Finance)
• Alternatively, students can choose:
  • an individual, more research-based project
  • noted as common for those considering a doctorate

Program completion (July)

• End-of-course ceremony and fellow dinner

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4) Taster lecture: How finance “thinks” + applying it to AI market valuation

A. Purpose of financial markets (core framing)

Financial markets are presented as serving to:

• allow saving
• provide capital to fund businesses
• match savers with entrepreneurs so projects deliver what they promise

B. Investment decision rule: discounted cash flow + positive NPV

Key concepts:

• Investors purchase assets to receive future streams of uncertain cash flows
• Discount rates convert future cash flows into present value
• The principle:
  • invest in positive NPV (Net Present Value) projects
  • with limited resources, select the highest NPV project(s)

Present value concept (as described):

• Present value = sum of future cash flows discounted by dividing by ((1 + r)^t) for each time period (t)

C. “Risk/return” goal: highest expected return, lowest risk

Finance targets:

• high return
• low risk

This is expressed as aiming for the “northwest quadrant”:

• vertical axis: expected return
• horizontal axis: return variation/volatility (σ)

D. Three big ideas of modern portfolio finance (as taught)

1. Diversification (Modern Portfolio Theory; Markowitz)

   • Asset returns are not perfectly correlated
   • Diversification can reduce risk and improve the risk/return profile

2. Combine a risk-free asset with a tangency/market portfolio

   • Risk-free return represented by T-bills
   • Efficient frontier: a line through the risk-free rate that touches the frontier leads to the market portfolio

3. Only paid for systematic (market) risk

   • Cost of capital depends on each asset’s exposure to market risk
   • Expected returns depend on each stock’s contribution to that risk (via beta)

E. Estimating the “AI market” size via valuation logic (Nvidia as a proxy)

The professor uses CAPM and a discounted cash flow style approach.

Step-by-step approach described

• Choose a representative company: Nvidia
• Use CAPM to estimate cost of equity:
  • beta measures systematic market risk
  • inputs:
    • risk-free rate (described as using Treasury bonds, not T-bills)
    • expected market return
    • equity risk premium = market expected return − risk-free rate
  • Nvidia beta ≈ 2.22 (from an information service provider)
• Compute tax-adjusted WACC:
  • debt is cheaper than equity
  • debt adjustment via ((1 - \text{tax rate}))
• Use projected free cash flows to estimate enterprise/project value up to a future year:
  • discount rate used ≈ 14%
  • terminal value computed via a growing perpetuity
• Terminal growth implications:
  • the implied required long-run growth is discussed (noted as potentially too high under conservative long-run growth assumptions)
• “Better numbers” / scenario range:
  • revenue and free cash flow projected out to ~2036
  • varying revenue and margin assumptions to create a range
  • net income margin observations inform free cash flow proportions

Key numeric takeaways stated

• Implied revenues by ~2036 (as a range, based on net margin assumptions), approximately:
  • $1.8T to $7.3T
• AI spending/services mapping (based on assumptions tying Nvidia activity to AI infrastructure):
  • total AI sales in 2036 estimated around ~$27.6T
  • “value added” estimated around ~$14T
• Comparison to global GDP:
  • AI value added as % of GDP by 2036:
    • ~8.3% in the “market” case
  • a more optimistic scenario discussed:
    • potentially ~17.4% of GDP

Lesson embedded in the valuation exercise

• Market-implied futures can be inferred from current pricing, but conclusions depend heavily on:
  • discount rates
  • margins / free cash flow assumptions
  • terminal growth assumptions

F. Broader course connection: macro-finance + boom/bust cycles + “Minsky moments”

The taster lecture connects finance theory to systemic investment dynamics:

• how booms become busts
• Hyman Minsky:
  • new technology → speculative finance → excess investment → Minsky moment (correction)
• a BIS (Bank for International Settlements) working paper is referenced as an example of applied analysis

What BIS-style analysis highlights

• the “AI boom” compared to historical booms/innovations
• network effects and policy responses creating contagion
• overinvestment and financing frictions amplifying downturns

G. A model explanation: overinvestment, productivity shocks, and financing failures

The speaker describes a stylized multi-period model conceptually.

Model structure (described)

• Three-period model
• Coalitions invest in AI compute during early periods
• A productivity draw happens in period 2:
  • productivity ratio (r) is between 0 and 2
  • (r > 1) good; (r < 1) bad
• Coalitions can observe productivity outcomes before further investment

Why overinvestment occurs (incentives/game theory)

• Incentive to invest too much in period 1:
  • ability to lock clients
  • period 1 investments are more productive (more time to run models; learning + better timing)
• Result:
  • Nash equilibrium leads to more investment than socially optimal
• Busts can trigger:
  • fire sales
  • amplified losses due to leverage/borrowing

Fire sales mechanism (described)

• Assets initially bought by highest-valuation users
• In crisis:
  • they must sell
  • forced sellers sell to lower-valuation buyers
  • prices fall further, creating a feedback loop
• Banks require collateral coverage (ratio mentioned conceptually)
• Falling collateral values → forced selling → further price declines
• “Bailout” occurs only when speculators step in

Circular financing mechanism (described)

• Banks/lenders have limited information about startup viability
• Supplier confidence (e.g., Nvidia) can improve lending access
• If startups fail, the supplier’s investment is also impaired
• This can create a “death spiral” through the financing network

Network contagion

• Failure of one coalition can cascade to others
• A weak node (example suggested as OpenAI in the speaker’s interpretation) could trigger broader collapse

Lesson embedded

• Even if AI investment increases productivity, financial structure (risk, leverage, liquidity, incentives) can still produce severe downside outcomes.

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5) Career development presentation: how Oxford supports MFE students (Ben Leslie)

A. Outcomes and employer destinations (high level)

• The MF cohort is said to achieve “outstanding outcomes” despite job-market cycles.
• Employer examples mentioned:
  • Bank of America
  • Citi
  • Goldman Sachs (smaller number in the described year)
  • Strategy consulting firms (McKinsey, Bain, BCG)
• Destinations vary year-to-year, spanning finance, consulting, and other areas.

B. Career Development Center structure and roles

The team is described as:

• Led by John Madrick
• Coaching roles also include Ben Leslie and Jazelle
• Division by focus:
  • careers coaching + employer engagement for finance-focused students
  • a consulting careers team led by Justice Adu
  • fintech employer engagement specialty led by Sean Tumi
  • operational support by Grace White

C. Core support activities

• ~75% of time on:
  • ongoing careers coaching conversations
  • helping students:
    • decide career direction
    • articulate interests
    • identify target roles
    • apply and refine applications
    • practice and receive feedback

Employer engagement

• Relationship building across:
  • bulge-bracket banks
  • private markets (private equity, infrastructure, real estate, venture capital)
  • corporate banking
  • retail
  • fintech
• Proactively cultivate new relationships to match hiring needs

D. Industry advisers for interviews and technical prep

Students access “industry advisers” (contracted consultants rather than full-time employees), supporting:

• mock interviews
• technical interview preparation across private markets and venture capital contexts

E. Digital platforms and resources

Main tools mentioned:

• OSCAR (internal business school platform)
  • 1:1 appointments with coaches
  • employer posting event space
• Oxford Careers Connect
  • wider university platform with additional events/opportunities
  • noted as aligned with MF interests
• Library digital resources:
  • economist/FT digital access
  • PitchBook
  • BlueMile terminal (library access)
• Additional resources:
  • Wall Street Prep
  • case coach for consulting test prep
  • a careers networking overlay/tooling mentioned as “Careers OS” (LinkedIn-based)

F. Timeline of career preparation events

Pre-arrival (July)

• Digital workshop series:
  • two weeks
  • three weekends
• Workshop leaders include business school staff and industry advisers
• Includes employer presentations and alumni insights

Launch week (arrival in Oxford; early October)

• Orientation + cohort cohesion
• Activities include:
  • communication studies workshops
  • London firm visits
  • employer sampling based on student preferences (first and second choice employers)

Miklmas term (first term while in Oxford)

• Numerous employer events via Oxford Careers Connect:
  • breakfasts, lunches, evening networking, formal presentations
  • multiple companies across the term

Careers fairs

• Students are encouraged to attend even though the team doesn’t run them
• Purpose:
  • meet more employers in a condensed format
  • connect with recruiters and desks

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6) Alumni perspective (Reuben Bach Nilson)

A. Background and motivations

• Alumni: Reuben Bach Nilson
• Current role: Associate at Providence Strategic Growth (growth equity)
• Prior:
  • McKinsey (business analyst)
  • Equity Partners (analyst)
• Studied at: Copenhagen Business School (International Business)
• Motivations for MFE:
  • quantitative/theoretical foundation and clarity about model assumptions/outputs
  • Oxford “experience” value

B. Favorite courses and learning benefits

• Economics course (micro + macro across terms)
• Stochastic calculus/options (Trinity)
• Benefits claimed:
  • learning to simplify complex models
  • understanding assumptions well enough to adapt to new exam conditions

C. Social/college life advice

• College: Kellogg College
• Advice:
  • attend socials across colleges
  • engage with university societies (he joined the Scandinavian society)
• Reminder:
  • Hillary is academically heavy while recruiting happens earlier, so planning is required
• “Must do”:
  • attend university events like May Day and Burns Day
  • balance academics with life activities

D. Application advice

• Contact alumni to understand target profiles and how to frame your application
• Adjust applications to align with program profile expectations (especially if you have professional experience)

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7) Admissions overview (Akbar + Alex + live Q&A)

A. Incoming cohort characteristics (MFE)

• Cohort size: 105
• Female participation: 39% (increase noted)
• Average age: 22
• GMAT focus score:
  • average around 715 over 805
• Diversity:
  • 28 nationalities
  • 93% international students

B. What they look for (selection framework)

Admissions criteria: three components

1. Intellect
   • academic ability
   • independence across the academic journey
   • fit to MFE demands
2. Career plan
   • strong analytical/quantitative orientation
   • clear essays highlighting internships/work experience
3. Soft skills
   • communication, collaboration, leadership, stamina
   • stamina emphasized because the MFE is intense across academics and career-related activities in a 9-month program

C. Application requirements

Materials required

• Online application form
• Academic transcripts
• GMAT or GRE score (no preference)
• English proficiency evidence:
  • possible English waiver if eligible
• One-page CV
• 500-word essay (fit + what attracted you to the MFE)
• Three professional or academic references
  • can be mixed
  • special case: if completing an undergraduate degree, a current-institution reference is acceptable
• Application fee: £75

D. Tests and waivers

• GMAT/GRE:
  • no preference between GMAT and GRE
  • GMAT 10th edition phased out but still considered if previously taken
  • no minimum scores, but competitive averages emphasized
• English:
  • English waiver possible for nationals/residents of majority English-speaking countries
  • academic waiver possible for degrees taught in English within a defined timeframe
  • if no waiver: IELTS (TOEFL noted as no longer accepted; phased out)

E. Kira Talent online assessment

• Trigger: after submitting the application
• Timing: link sent within ~24 hours
• Format:
  • pre-recorded questions
  • 6 questions total
  • webcam required
  • interview-like competency/behavioral focus
• Question types:
  • motivation/competency verbal questions (randomized set)
  • one question on financial and economic trends (typically last)
• Preparation time:
  • short prep interval included during the assessment
  • advised not to read from a script

F. Application stages, deadlines, and scholarships

• Applications expected to open mid-September (exact dates pending finalization)
• Deadlines across three stages:
  • October, January, March
• After each deadline:
  • an additional week to complete Kira assessments
  • referees upload supporting documents
• Scholarship eligibility:
  • stage 1 and 2 applicants are eligible
  • stage 3 applicants are not (as stated)

G. Scholarship and funding guidance

Funding sources mentioned:

• UK government loans (postgraduate loan)
• scholarships (examples named):
  • Reach Oxford (Roelly) Scholarship
  • Oxford Say Scholarship for Women
  • university/college scholarships

Guidance:

• research scholarships early
• scholarship outcomes communicated around May/June

H. College benefits (Oxford structure)

Colleges are described as central to community:

• smaller community connections
• social networking and clubs/societies
• accommodation and facilities (dining hall, libraries)

Mentioned scale:

• over 400 clubs/societies
• 83 sports clubs

Purpose:

• community and a “second home”
• social networking and support beyond the classroom

I. Key Q&A clarifications captured in the talk

• Holistic assessment:

  • overall undergraduate result matters
  • other course/module results considered too
  • plus GMAT/GRE, Kira, references

• GPA equivalency (U.S.):

  • (not provided in the notes)