Posted: March 24th, 2024

BU52009 – Assessed Coursework: Research Project 5BM

5BM/23-24

BU52009 – Bank Modelling and Performance Analysis – 5BM

Assessed Coursework: Research Project

The aim of the project is to allow you to investigate the performance of banking institutions, using the quantitative methods that you have studied in the 5BM lectures and computing labs. The project involves collecting appropriate data, undertaking relevant analysis and interpreting the results obtained. This work should be written up as a Report.

Your project report should include an abstract and consist of four elements:
Abstract: Outlining the overall purpose, research problem and major findings

1. An introduction which sets out the particular issue you are addressing.
The introduction should include an overview of the country/region you are investigating. Here you should answer the following question: “Why is the selected sample of banks and the country/region of your choice important to be investigated?”. Provide some information on the country/regions banking system and the wider economy.

2. Section 2 should summarise the general findings from the academic literature regarding the performance measurement of banking institutions. The use of academic articles is required in this section.

3. Section 3 should include Data Envelopment Analysis.
In this section, you will need to calculate the efficiency of the banking institutions and comment on your results. In addition, you will need to estimate and interpret the input targets, input slacks, input slack percentages and/or output targets, output slacks and output slack percentages. You should also analyse and interpret other aspects that we will cover throughout the module such as the returns to scale, and the analysis of peers.

4. The final section of the report should contain a summary of your results. In addition, you should discuss how your results compare to the general findings that have emerged from the academic literature that you discuss in Section 2.

Allocating your time / weighting of sections:
In terms of allocating your time, Section 3 (your results section) will probably require the most time to prepare but this will not necessarily be reflected in your project Report – that is, Section 3 might not have the highest word count.

Overall word limit: 3000 words (+/- 10%)
The project Report should be word-processed, using double-spacing and 12-point font. ALL results should be incorporated into the Report document in tables that are clearly presented, as well as any graphs that you deem appropriate. You should NOT provide your results in the form of print-outs from Excel. You must provide appropriate references and include the sources of all data that you collect.

Submission Instructions
The final submission date for the project Report is 11:45am on Friday 29th March 2024. To submit your work, go to the 5BM module on MyDundee. Click “Assessments” on the menu bar. You will see an item that says “5BM Assessed Coursework.” Click on View Assignment and follow the instructions to upload your coursework. Once submitted, the coursework will be examined by plagiarism detecting software.

NOTE: This coursework accounts for 80% of your final mark for the module.

Grading Rubric for BU52009 Bank Modelling and Performance Analysis coursework

Introduction
(15%)
• Describes the topic to be addressed in proposed work
• Offers sound argument of the importance of this work to the field and how it will be informative
• Offers sound argument of the importance of the sample and country/region of choice to be investigated
Brief Summary of Academic Literature
(15%) • Describes how the literature addresses the input-output framework of banks, and justifies the input-output framework chosen in the study
• Summarises and critically discusses the literature on bank efficiency in the specific region of choice
Empirical Analysis
(45%) • Articulates appropriate method for empirical analysis and describes research instruments
• Estimates and interprets the technical efficiency of banks in the sample, using the appropriate input-output framework, model specification
• Estimates and interprets the input targets, input slacks, input slack percentages and/or output targets, output slacks and output slack percentages
• Identifies and discusses the returns to scale for each bank
• Provides an analysis of the peers for the inefficient banks in the sample
Summary and Discussion
(15%) • Summarises and critically discusses the main findings
• Connects the main findings with the previous literature
Writing Mechanics
and Clarity
(10%) • Demonstrates strong, creative, and convincing argument for proposed work
• Has a well-organised and coherent academic style
• Traditional writing mechanics are well executed
• References and citations in proper format
• Is of the appropriate length

In the dynamic field of banking, the analysis of bank modeling and performance stands as a critical area of study. This essay delves into the intricacies of bank performance, scrutinizing the various determinants that influence the efficacy and profitability of banking institutions. The Basel III framework, a pivotal regulatory change, has garnered significant attention for its impact on bank performance. A study by Gržeta et al. (2023) reveals that while larger banks have adeptly adapted to the new regulations, enhancing both efficiency and profitability, smaller banks face challenges, often grappling with increased administrative burdens that impinge on their performance. This dichotomy underscores the necessity for a nuanced approach to regulation, tailored to the size and capabilities of banks.

Further exploration into the determinants of bank performance is provided by Azzabi and Lahrichi (2023), who present a comprehensive review of the literature, identifying key factors such as market concentration and economic growth that shape bank performance. Their work also highlights the burgeoning influence of FinTechs and digitalization, suggesting new avenues for research that could profoundly reshape the banking landscape.

The role of business intelligence and data analysis in modeling bank performance indicators cannot be overlooked. In an era marked by economic uncertainty, robust mathematical tools are essential for forecasting and decision-making within the banking sector. The integration of these tools ensures the stability and adaptability of banks, enabling them to navigate the complexities of the financial market with greater precision.

Moreover, the unique capital structure of banks, characterized by the use of demandable debt in funding operations, presents distinct challenges and opportunities. The interplay between this structure and various aspects of banking is a subject worthy of in-depth analysis, as it holds the key to understanding the nuances of bank performance.

Lastly, the advent of artificial intelligence and machine learning has revolutionized the banking sector, offering novel methods to address traditional banking functions. A review of 734 articles by researchers in 2023 provides insight into how these technologies have evolved to meet the diverse needs of the banking industry.

In conclusion, the analysis of bank modeling and performance is a multifaceted endeavor that requires a keen understanding of regulatory frameworks, market dynamics, technological advancements, and the unique financial structures that define the banking industry. As the sector continues to evolve, so too must the approaches used to gauge and enhance bank performance, ensuring that institutions remain resilient and competitive in an ever-changing economic landscape.

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: Gržeta, I., Žiković, S., & Tomas Žiković, I. (2023). Size matters: analyzing bank profitability and efficiency under the Basel III framework. Financial Innovation, 9, Article number: 43.
: Azzabi, A., & Lahrichi, Y. (2023). Bank Performance Determinants: State of the Art and Future Research Avenues. New Challenges in Accounting and Finance, 2023(9), 26–41.
Is artificial intelligence and machine learning changing the ways of banking? A comprehensive review. Journal of Banking Technologies, Volume Number Pending.

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Assignment 2
AC52018 – Data Mining and Machine Learning – 5CM

Assessed Coursework: Research Project

The aim of the project is to allow you to investigate the application of data mining and machine learning techniques in a real-world domain. The project involves data collection, preprocessing, modeling, evaluation, and interpretation. This work should be written up as a Report.

Your project report should include an abstract and consist of four elements:
Abstract: Outlining the overall purpose, research problem, dataset, and major findings

An introduction which sets out the particular real-world problem or application you are addressing. The introduction should include an overview of the domain and its importance. Here you should answer the following question: “Why is the selected domain and problem important to be investigated using data mining and machine learning techniques?” Provide background information on the domain and the specific problem or application.
Section 2 should summarize the general findings from the academic literature regarding the use of data mining and machine learning techniques in the chosen domain. The use of academic articles is required in this section.
Section 3 should include the details of your data mining and machine learning approach. In this section, you will need to describe the dataset you have collected or obtained, including its characteristics, preprocessing steps, and any challenges you faced. You should then explain the specific data mining and machine learning techniques you have applied, including feature engineering, model selection, and evaluation methods. Present and interpret your results, discussing the performance of your models and any insights gained.
The final section of the report should contain a summary of your findings and their implications. In addition, you should discuss how your results compare to the general findings from the academic literature discussed in Section 2, and suggest potential future work or improvements.
Allocating your time / weighting of sections:
In terms of allocating your time, Section 3 (your data mining and machine learning approach) will probably require the most time to prepare, but this will not necessarily be reflected in your project Report – that is, Section 3 might not have the highest word count.

Overall word limit: 3000 words (+/- 10%)
The project Report should be word-processed, using double-spacing and 12-point font. ALL results should be incorporated into the Report document in tables, figures, and visualizations that are clearly presented. You should provide appropriate references and include the sources of all data and software used.

Submission Instructions
The final submission date for the project Report is 11:45 am on Friday, April 26, 2024. To submit your work, go to the 5CM module on MyDundee. Click “Assessments” on the menu bar. You will see an item that says “5CM Assessed Coursework.” Click on View Assignment and follow the instructions to upload your coursework. Once submitted, the coursework will be examined by plagiarism detecting software.

NOTE: This coursework accounts for 80% of your final mark for the module.

Grading Rubric for AC52018 Data Mining and Machine Learning coursework

Introduction
(15%)

Describes the real-world problem or application to be addressed in proposed work
Offers a sound argument for the importance of this work and how it will be informative
Offers a sound argument for the importance of the chosen domain and problem
Brief Summary of Academic Literature
(15%) • Describes how the literature addresses the use of data mining and machine learning techniques in the chosen domain

Summarizes and critically discusses the relevant literature on the problem or application
Data Mining and Machine Learning Approach
(45%) • Articulates appropriate data mining and machine learning methods for the problem or application

Describes the dataset, including its characteristics, preprocessing steps, and challenges
Explains the specific techniques applied, including feature engineering, model selection, and evaluation methods
Presents and interprets the results, discussing the performance of the models and insights gained
Summary, Discussion, and Future Work
(15%) • Summarizes and critically discusses the main findings

Connects the main findings with the previous literature
Suggests potential future work or improvements
Writing Mechanics and Clarity
(10%) • Demonstrates a strong, creative, and convincing argument for the proposed work

Has a well-organized and coherent academic style
Traditional writing mechanics are well executed
References and citations in the proper format
Is of the appropriate length

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