Explore UCD

UCD Home >

STAT30320

Academic Year 2025/2026

Investment and Trading (STAT30320)

Subject:
Statistics & Actuarial Science
College:
Science
School:
Mathematics & Statistics
Level:
3 (Degree)
Credits:
5
Module Coordinator:
Dr Michelle Carey
Trimester:
Autumn
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

The purpose of this module is to provide students with a comprehensive understanding of modern financial markets and the instruments traded within them. The module introduces the structure and functioning of key markets including money, bond, mortgage, stock, foreign exchange, and derivative markets with a few towards trading strategies.

Students will develop both theoretical and practical insights into pricing, hedging, and trading across a wide range of financial instruments, including futures, forwards, swaps, options, exotic derivatives, credit derivatives, and energy/commodity products. Emphasis is placed on the application of financial mathematics (e.g., Black–Scholes, binomial models, Greeks, volatility smiles) and the use of trading simulations to reinforce learning through experiential practice.

This does not cover the mathmatical models for the pricing of fincial instruments that is covered in ACM30110 and STAT40930. Students are expected to have prior knowledge of:
Probability and Statistics – including distributions, expectations, variance, and regression.
Calculus and Linear Algebra – differentiation, integration, and matrix methods relevant to financial modelling.
Financial Mathematics – time value of money, interest rate calculations, and familiarity with basic asset pricing.
Stochastic Processes – an understanding of random walks and Brownian motion at an introductory level.
Derivative Basics – awareness of forwards, futures, and simple options (e.g. payoff structures).
A working knowledge of the Black–Scholes model, binomial option pricing, and hedging strategies is particularly important, as the module builds directly on these concepts. This is not an introductory finance module and is therefore not suitable for students without a solid mathematical background.

About this Module

Learning Outcomes:

By the end of the module, students will have gained a strong foundation in the mechanics of trading and investment decisions, equipping them with the knowledge and skills to critically analyze financial instruments, evaluate market dynamics, and apply quantitative methods to real-world trading scenarios.

Indicative Module Content:

This module covers the structure, pricing, and trading of a broad range of financial instruments across global markets. Indicative topics include:

Money and Bond Markets – instruments, pricing, and market functioning

Mortgage and Stock Markets – structure, valuation, and trading mechanisms

Foreign Exchange Markets – currency instruments and trading practices

Derivative Markets – introduction to derivatives and their role in finance

Futures and Forwards – pricing, hedging strategies, and risk management

Interest Rate Futures – structure and applications

Swaps – mechanics and uses in financial markets

Options Markets – stock and currency options, option properties, and trading strategies

Volatility and Smiles – implied volatility surfaces and practical considerations

Credit Derivatives – instruments, markets, and applications

Exotic Options – structures and valuation approaches

Energy and Commodity Derivatives – market structure and trading practices

Practical elements of the course will include the use of a trading simulator to provide experiential learning in market dynamics, strategy implementation, and portfolio management.

Student Effort Hours:
Student Effort Type Hours
Lectures

24

Computer Aided Lab

10

Autonomous Student Learning

90

Total

124


Approaches to Teaching and Learning:
Teaching and learning in this module combine lectures, problem-based learning, and experiential activities to develop both theoretical understanding and practical skills in investment and trading. Core concepts are introduced through lectures, which are supported by worked examples and case-based applications from real financial markets.

Active and task-based learning is a central feature of the module. Students will engage in trading simulations to experience market dynamics, strategy execution, and portfolio management in a controlled environment.

Students will also participate in simulation-based tasks and in discussing case studies, fostering collaborative problem-solving and reflective learning. Critical engagement with financial models and trading strategies will be developed through assignments requiring analytical and evaluative writing.

Use of AI: In line with University policy, AI tools may be used in a limited and transparent way to support learning (e.g., for data analysis, coding, or exploring financial modelling approaches). However, all submitted work must represent the student’s own understanding and analysis, and the inappropriate use of AI to generate solutions or assessments will be considered a breach of academic integrity. Guidance will be provided on appropriate and inappropriate uses of AI within this module.

Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Not applicable to this module.
 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Individual Project: Students use the simulator to build virtual portfolios, test strategies and monitor performance from week 4-10 students submit a report that analyzes what strategies worked and why Week 10 Standard conversion grade scale 40% No
20
No
Reflective Assignment: Each week, students listen to one Thoughts on the Market (Morgan Stanley) or The Markets (Goldman Sachs) podcast and submit a short reflection on the discussion and its implications for trading. Week 2, Week 3, Week 4, Week 5, Week 6, Week 7, Week 8, Week 9, Week 10, Week 11 Standard conversion grade scale 40% No
20
No
Exam (In-person): End of term exam End of trimester
Duration:
2 hr(s)
Standard conversion grade scale 40% No
60
No

Carry forward of passed components
Yes
 

Resit In Terminal Exam
Spring Yes - 2 Hour
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Not yet recorded.

"Options, Futures, and Other Derivatives" by John C. Hull
"Statistics and Data Analysis for Financial Engineering" (2nd ed., 2015) by David Ruppert
"Machine Learning in Asset Pricing" by Stefan Nagel

Name Role
Mr Matteo Bargelloni Tutor
Atefeh Moradi Tutor

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Autumn Lecture Offering 1 Week(s) - Autumn: All Weeks Tues 12:00 - 13:50
Autumn Computer Aided Lab Offering 1 Week(s) - 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 Fri 12:00 - 12:50
Autumn Computer Aided Lab Offering 2 Week(s) - 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 Fri 13:00 - 13:50