FINANCE-12 · Research · ENGLISH · PLANNED
Trace cash flows, calculate a transparent model, stress-test assumptions and explain risk.
Study time is an initial activity estimate, excluding extra reading, revision and extended laboratory or project work. Videos and question bodies have not been produced by adding this outline.
Complete these courses or demonstrate equivalent knowledge. These are recommended prerequisites, not enforced enrolment locks.
YouTube playlist: ARK Finance | Quantitative Finance and Research Capstone
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Sign in to save this learning pathPractical outcome: Evaluate an illustrative risk model using synthetic data.
Explain return distributions using a visual and one worked real-world example. Context: Evaluate an illustrative risk model using synthetic data. End with a prediction or check-for-understanding.
Explain time-series models using a visual and one worked real-world example. Context: Evaluate an illustrative risk model using synthetic data. End with a prediction or check-for-understanding.
Explain volatility models using a visual and one worked real-world example. Context: Evaluate an illustrative risk model using synthetic data. End with a prediction or check-for-understanding.
Explain monte carlo simulation using a visual and one worked real-world example. Context: Evaluate an illustrative risk model using synthetic data. End with a prediction or check-for-understanding.
Use the first concepts from this section. Evaluate an illustrative risk model using synthetic data. Produce a labelled diagram, trace or calculation; explain each step and check one changed input.
3–6 questions on the preceding first-half concepts only: recall, visual interpretation, application and one plausible misconception. Explain each answer after submission.
Explain stochastic-process concepts using a visual and one worked real-world example. Context: Evaluate an illustrative risk model using synthetic data. End with a prediction or check-for-understanding.
Explain numerical optimisation using a visual and one worked real-world example. Context: Evaluate an illustrative risk model using synthetic data. End with a prediction or check-for-understanding.
Explain backtesting using a visual and one worked real-world example. Context: Evaluate an illustrative risk model using synthetic data. End with a prediction or check-for-understanding.
Explain overfitting using a visual and one worked real-world example. Context: Evaluate an illustrative risk model using synthetic data. End with a prediction or check-for-understanding.
Evaluate an illustrative risk model using synthetic data. Work without the example, compare two cases, record assumptions and limitations, then self-review correctness, reasoning and clarity.
6–12 questions covering Return distributions, Time-series models, Volatility models and the remaining section concepts, with cumulative retrieval from earlier sections. Include a new transfer problem; 70% unlocks progression.
Practical outcome: Compare competing explanations in selected finance research.
Explain empirical asset pricing using a visual and one worked real-world example. Context: Compare competing explanations in selected finance research. End with a prediction or check-for-understanding.
Explain market microstructure using a visual and one worked real-world example. Context: Compare competing explanations in selected finance research. End with a prediction or check-for-understanding.
Explain behavioural finance using a visual and one worked real-world example. Context: Compare competing explanations in selected finance research. End with a prediction or check-for-understanding.
Explain corporate governance research using a visual and one worked real-world example. Context: Compare competing explanations in selected finance research. End with a prediction or check-for-understanding.
Use the first concepts from this section. Compare competing explanations in selected finance research. Produce a labelled diagram, trace or calculation; explain each step and check one changed input.
3–6 questions on the preceding first-half concepts only: recall, visual interpretation, application and one plausible misconception. Explain each answer after submission.
Explain private markets using a visual and one worked real-world example. Context: Compare competing explanations in selected finance research. End with a prediction or check-for-understanding.
Explain real estate finance using a visual and one worked real-world example. Context: Compare competing explanations in selected finance research. End with a prediction or check-for-understanding.
Explain infrastructure finance using a visual and one worked real-world example. Context: Compare competing explanations in selected finance research. End with a prediction or check-for-understanding.
Explain household finance research using a visual and one worked real-world example. Context: Compare competing explanations in selected finance research. End with a prediction or check-for-understanding.
Compare competing explanations in selected finance research. Work without the example, compare two cases, record assumptions and limitations, then self-review correctness, reasoning and clarity.
6–12 questions covering Empirical asset pricing, Market microstructure, Behavioural finance and the remaining section concepts, with cumulative retrieval from earlier sections. Include a new transfer problem; 70% unlocks progression.
Practical outcome: Defend an original fictional-company analysis with reproducible evidence.
Explain research proposal using a visual and one worked real-world example. Context: Defend an original fictional-company analysis with reproducible evidence. End with a prediction or check-for-understanding.
Explain data provenance using a visual and one worked real-world example. Context: Defend an original fictional-company analysis with reproducible evidence. End with a prediction or check-for-understanding.
Explain model validation using a visual and one worked real-world example. Context: Defend an original fictional-company analysis with reproducible evidence. End with a prediction or check-for-understanding.
Explain valuation report using a visual and one worked real-world example. Context: Defend an original fictional-company analysis with reproducible evidence. End with a prediction or check-for-understanding.
Use the first concepts from this section. Defend an original fictional-company analysis with reproducible evidence. Produce a labelled diagram, trace or calculation; explain each step and check one changed input.
3–6 questions on the preceding first-half concepts only: recall, visual interpretation, application and one plausible misconception. Explain each answer after submission.
Explain stress scenarios using a visual and one worked real-world example. Context: Defend an original fictional-company analysis with reproducible evidence. End with a prediction or check-for-understanding.
Explain ethical review using a visual and one worked real-world example. Context: Defend an original fictional-company analysis with reproducible evidence. End with a prediction or check-for-understanding.
Explain reproducibility package using a visual and one worked real-world example. Context: Defend an original fictional-company analysis with reproducible evidence. End with a prediction or check-for-understanding.
Explain capstone defence using a visual and one worked real-world example. Context: Defend an original fictional-company analysis with reproducible evidence. End with a prediction or check-for-understanding.
Defend an original fictional-company analysis with reproducible evidence. Work without the example, compare two cases, record assumptions and limitations, then self-review correctness, reasoning and clarity.
6–12 questions covering Research proposal, Data provenance, Model validation and the remaining section concepts, with cumulative retrieval from earlier sections. Include a new transfer problem; 70% unlocks progression.
Combine all three sections: Defend an original fictional-company analysis with reproducible evidence. Submit an original worked artifact, evidence or tests, assumptions and a reflection. Rubric: correctness, method, practical usefulness, reproducibility and communication. Self-reviewed, not certified.
6–12 balanced questions sampling every section of Quantitative Finance and Research Capstone, including interpretation, calculation or reasoning, a misconception and a new application. Passing is 70%; review errors before retrying.
Fictional USD budgets, statement analysis, valuation models and risk scenarios. Guided work leads to an independent attempt, with a diagram, explanation, trace, calculation or tested artifact. Question bodies are generated when opened in your private learning path. Programming submissions are stored as text; the platform does not execute submitted code.
Use original or appropriately licensed datasets. Practical science activities use models or supplied data unless a qualified supervisor has reviewed a physical experiment.
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