MSc Finance and Trading

The quantitative foundations of modern financial markets: calculus, linear algebra and probability applied to asset pricing and portfolio construction; hypothesis testing, regression and time-series modelling for market data; and stochastic processes behind derivatives valuation and risk measurement. Game theory covers Nash equilibrium, auction theory, information asymmetry and adverse selection, applied to order-book dynamics, market making and competing market participants.

Assessment Problem sets, a statistical project using real market data, and a written examination.

Outcome Formulate trading problems mathematically, evaluate statistical evidence and reason strategically about other market participants.

Financial economics and advanced pricing theory. Starting from rational investor behaviour under certainty and uncertainty, the module builds towards how prices are determined in asset markets, then examines apparent violations of rational pricing, portfolio allocation and market efficiency. Market structure covers key participants and markets, with a focus on fixed income and foreign exchange, and the trading mechanisms and institutions behind them.

Assessment Coursework (40%) and a written examination (60%).

Outcome Explain how asset prices are determined, critically assess market efficiency and describe how the main global markets are structured.

Econometric techniques used in finance: classical regression and an introduction to time-series methods, with an emphasis on applied work using econometric packages and the principles of AI-based statistics.

Assessment Two applied data projects (100% coursework).

Outcome Carry out and interpret a sound empirical analysis of financial data, and judge when AI-generated analysis can be trusted.

How retail fintechs attract, convert and serve clients in a heavily regulated, competitive market: digital marketing funnels, affiliate and introducing-broker networks, brand positioning, customer lifetime value and retention analytics, alongside financial promotion rules, risk warnings, target-market definitions and restrictions on incentives. Service operations cover service levels, complaints and escalation, treatment of vulnerable customers, and the boundary between account management and investment advice.

Assessment A campaign design project compliant with a chosen regulator’s promotion rules, a role-played client interaction portfolio and a written examination.

Outcome Design client acquisition and service strategies that are commercially effective, compliant and fair to clients.

Technology stacks of modern fintechs: front-end trading platforms, server infrastructure, bridges and gateways, price-feed aggregation, CRM systems, sovereign and hyperscaler stacks and APIs. Order routing, latency and its commercial consequences, platform administration, uptime and incident management, and cybersecurity threats specific to trading systems.

Assessment A platform configuration project in the virtual trading lab and a written examination.

Outcome Evaluate a brokerage technology stack and explain its commercial and risk consequences.

Where prices come from and how brokerage product economics are built: liquidity providers, prime and prime-of-prime arrangements, best bid/offer construction, spread models, swap and financing charges, and margin and leverage across FX, indices, commodities, equities and crypto-assets. Treasury covers payment providers, deposits and withdrawals, chargebacks and payment fraud, multi-currency liquidity planning, and the client money regime: segregation, trust arrangements, reconciliation and lessons from insolvency cases.

Assessment A product pricing model, a reconciliation and treasury planning exercise, and a written examination.

Outcome Analyse the full revenue and cost structure of a brokerage product and explain how client assets are safeguarded while managing working capital.

The dealing desk: order types and execution logic, slippage and requotes, execution policies and best-execution obligations, surveillance for abusive strategies, and the regulatory limits of dealer intervention. A-book, B-book and hybrid execution models, with their commercial and conflict-of-interest implications. Practical sessions simulate dealing decisions in volatile markets.

Assessment A simulated dealing exercise in the virtual trading lab and a written examination.

Outcome Assess execution arrangements and defend choices that balance commercial results, client outcomes and regulation.

Market, credit, liquidity and operational risk, how to quantify them, and how to decide what is acceptable. How a broker measures and manages risk from client positions: net exposure, value-at-risk and stress testing, hedging with liquidity providers, toxic-flow classification, negative balance protection and margin-call mechanics. Historic stress events, including the 2015 Swiss franc de-pegging, serve as case studies.

Assessment A risk report on a simulated client book (40%) and a written examination (60%).

Outcome Build and defend a risk management framework for a leveraged brokerage book.

Futures and forward markets and their pricing, constructing hedges, option markets, and stochastic processes in financial modelling. International finance covers the complexities of operating across countries and the key theories used to understand international financial developments.

Assessment Coursework (40%) and a written examination (60%).

Outcome Price and use derivatives for hedging, and analyse international financial developments and their effect on trading businesses.

Client onboarding: customer and enhanced due diligence, identity verification technology, sanctions and politically exposed person screening, source of funds, appropriateness testing for leveraged products and client categorisation. Anti-money laundering typologies, transaction monitoring and suspicious activity reporting. A comparison of regulatory regimes, including the UK, the EU, Australia, Cyprus and offshore jurisdictions; product intervention measures, reporting, conduct rules, complaints and compensation schemes, and the compliance function’s role in governance.

Assessment An onboarding case portfolio, a comparative regulatory analysis and a written examination.

Outcome Assess client files against regulatory standards, identify financial crime risk and map a firm’s obligations across jurisdictions.

Time-series econometrics for real-world problems, including practical work in R; machine learning for financial data and how to avoid over-fitting; designing, back-testing, deploying and maintaining automated trading systems; cloud and distributed deployment; and model risk and regulatory requirements for algorithmic trading.

Assessment A practical project building and evaluating a trading model (70%) and a written report (30%).

Outcome Apply and critically evaluate machine learning and econometric methods in trading, and explain how to deploy them responsibly.

An independent piece of academic research. Where possible you write on a topic commissioned by your employer, so the thesis addresses a real problem in the firm while meeting the university’s standards. You choose either:

1. an applied research project: company or business analysis, financial analysis, valuation or a business case, with cross-checks and a professional-standard report and presentation; or
2. a research-based thesis: a theoretical or empirical study in finance or trading.

Alongside the full thesis you submit a one-page executive summary, which teaches the art of being concise and can accompany job applications. Each student has a university supervisor and may have an industry mentor arranged by us. Thesis seminars run online, and the thesis and maturity test are assessed under the university’s degree regulations.