Understand the nature and properties of financial data.

Knowledge and Understanding, Communication.


Develop and interpret statistical models for financial data.

Analysis and Application, Communication.


Demonstrate proficiency in using statistical software (Excel, R) to present and analyze different types of financial data and to draw appropriate conclusions.

Analysis and Application, Problem-solving.


Demonstrate the ability to communicate the results and insights of financial data analysis clearly and effectively.

Analysis and Application, Problem-solving.

(1) Mid-term group report (1,500 words) - weighted at 30% - LOs 1, 2

(2) Final individual presentation (10 minutes presentation) - weighted at 70% - LOs 1, 2, 3 & 4

This module has been developed to acquaint students with fundamental concepts in financial data analytics. Striking a balance between theory and practical applications, it offers students an accessible understanding of financial econometric models and how they can be applied in real-world scenarios. The module provides a hands-on introduction to the analysis of financial data, utilizing statistical software (Excel, R) and case studies to demonstrate the practical implementation of discussed methods.
Commencing with an exploration of the basics of financial data, the module covers summary statistics and various visualization methods associated with them. Following this, subsequent sections delve into fundamental time series analysis and uncomplicated econometric models tailored for financial data. The aim is to provide students with a comprehensive understanding of the intricate relationship between data analytics and decision-making in the financial realm, emphasizing various quantitative methods relevant to finance.
Key topics covered in the module include:
• Financial data and their inherent properties.
• Visualization techniques for financial data.
• Concepts of stationarity, correlation, and autocorrelation functions.
• Linear time series analysis, encompassing exponential smoothing for forecasting and methods for model comparison.
• Diverse approaches to calculating asset volatility.
• Examination of high-frequency financial data and straightforward models for price changes, trading intensity, and realized volatility.

The learning strategy for the module requires students to commit 200 learning hours (including assessment) of which there will be 36 hours of tutor-led learning and 164 hours of independent and self-directed study. During the tutor-led learning hours, students will receive robust support through interactive tutorials where they can ask questions, clarify doubts, and receive personalized guidance on challenging concepts. These tutorials are designed to foster a deeper understanding of the material, with opportunities for collaborative learning and discussions that enhance comprehension and retention.

Action Learning. Learning is achieved by engaging students in activities that have elements of problem solving combined with intentional learning.

Authentic Learning. Students will be presented with activities that are framed around "real-life" contexts in which students will find learning more meaningful and motivating. Thus, they will be more engaged in the process of acquiring knowledge.

You will undertake ‘formative’ assessments during the module to help you monitor your learning and provide you and us with ongoing feedback on your progress, that helps you prepare for the ‘summative assessment(s) during or at the end of the module. The formative assessments may include quizzes, short essays, or problem-solving exercises, with feedback given to help students identify areas for improvement.

The use of AI tools such as ChatGPT can be beneficial in learning, however, students must adhere to the following guidelines: (i) all submitted work must be original and written by the student; (ii) if a student utilizes AI tools to generate ideas or receive guidance, this must be clearly cited in the submitted work, just as any other source would be; (iii) students should be mindful of the ethical implications of using AI in their studies, ensuring that their use of such tools does not constitute academic dishonesty.

Unit feedback:
During semester, students’ feedback on the module will be collated and distributed through University's online platform, ensuring consistency with other units and focusing on both improvement and future success. This process includes both individual feedback and cohort-wide summaries, aligning with the university's standardized approach to learning and assessment.

Main Texts:
1. Collard, Jean-Francois. (2022) Hands-On Data Analysis in R for Finance. Chapman and Hall/CRC, 1st edition.
2. Bennett, Mark J. & Hugen, Dirk L. (2016). Financial Analytics with R: Building a laptop laboratory for data science. Cambridge, United Kingdom Cambridge University Press.
3. Tsay, Ruey S. (2012). An Introduction to Analysis of Financial Data with R. 1st Edition. Wiley.

Module Study Guide and Handbook
BUV Learning Resources Centre and website
BUV Canvas; Learning Management System
SU VLE
SU Library