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In this course, you’ll explore quantitative concepts and techniques essential for financial analysis and investment decision-making. We’ll cover descriptive statistics to convey key data attributes - such as central tendency, location, and dispersion - and explore the characteristics of return distributions. Additionally, we’ll examine probability theory and its role in quantifying risk to support informed investment decisions. This course will also introduce machine learning algorithms and the role of big data in an investment context. This course will use R and SAS software for data analysis.
The objectives of the course are:1. Critically analyse and apply advanced concepts of probability distributions to solve complex problems in quantitative analysis.2. Evaluate and implement sophisticated sampling and estimation methodologies in the context of data-driven decision-making.3. Demonstrate expertise in regression analysis by constructing, interpreting, and critically assessing statistical models.4. Apply advanced time-series analysis techniques to address real-world forecasting and analytical challenges in various domains.5. Critically assess and employ machine learning techniques for the analysis and interpretation of large-scale, complex datasets.
Subject to Approval of the Head of Department
ECON213
Students must attend one activity from each section.
Tom Coupe
The ‘45% rule’ does not apply to this course. That is, student does not need to reach 45% weighted average across invigilated assessments. Please refer to https://learn.canterbury.ac.nz/course/view.php?id=7744 for further information. There will be online quizzes after most classes (between 25 and 30). Your quiz mark will be based on the average of your best 20 quiz results. Missing an occasional quiz is thus not a problem. However, consistently missing quizzes means you will forfeit relatively easy points toward your final grade.Guidelines for the Use of AI in CourseworkThe use of AI may or may not be permitted in courses. Within a course, permission may vary by assignment. It is the responsibility of the student to inform themselves about assessment conditions and submit work that is their own and that properly acknowledges the work of other people and tools, including generative artificial intelligence tools.It is important to familiarise yourself with the UC Misconduct Procedure Guide for Students. Examples of academic misconduct include, but are not limited to:Where a student uses a generative artificial intelligence (AI) tool for an assessment in a manner that is not expressly permitted or fails to acknowledge the use of a generative AI tool as instructed.For this class, you can use LLMs to HELP you with everything (just like you can ask your colleagues for advice on everything). Use LLMs wisely and ask for explanations, not just answers!The only exception is the term test and the final test, where part is closed everything, and another part is open to help from LLMs (but not from your colleagues!)Assessment In Te Reo MāoriIn recognising that Te Reo Māori is an official language of New Zealand, the University provides for students who may wish to use Te Reo Māori in their assessment. If you intend to submit your work in Te Reo Māori you are required to do the following: Read the Assessment in Te Reo Māori Policy and ensure that you meet the conditions set out in the policy. This includes, but is not limited to, informing the Course Coordinator 1) no later than 10 working days after the commencement of the course that you wish to use Te Reo Māori and 2) at least 15 working days before each assessment due date that you wish to use Te Reo Māori.
CFA Institute; Quantitative Investment Analysis ; 4th;
Domestic fee $1,206.00
International Postgraduate fees
* All fees are inclusive of NZ GST or any equivalent overseas tax, and do not include any programme level discount or additional course-related expenses.
For further information see Department of Economics and Finance .