Programming for Finance in Python - BUSN9196

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Module delivery information

Location Term Level1 Credits (ECTS)2 Current Convenor3 2022 to 2023
Canterbury
Spring Term 7 15 (7.5) Hirbod Assa checkmark-circle

Overview

This module will introduce students to Python, a programming language that has become the industry standard. Students will learn how to use Python in order to conduct financial and econometric analysis. Particular emphasis will be placed on programming for specific financial applications such as portfolio optimization, asset valuation, and derivatives pricing. Indicative topics include
• Data types and structures
• Input/output operations
• Data visualization
• Summary statistics
• Regression
• Optimization
• Valuation and risk
• Derivatives

Details

Contact hours

• Total contact hours: 35
• Private study hours: 115
• Total study hours: 150

Method of assessment

Main assessment methods:
Individual Report – 2000 words (30%)
Individual Research Project – 3000-3500 words (70%)

Reassessment methods:
100% coursework

Indicative reading

• Y. Hilpisch, "Python for Finance", 2nd edition, 2018, O'Reilly, ISBN 9781492024330
• S. Fletcher and C. Gardner, “Financial Modelling in Python”, 2010, Wiley, ISBN 9780470747896
• Y. Hilpisch, “Derivatives Analytics with Python: Data Analysis, Models, Simulation, Calibration and Hedging”, 2015, Wiley, ISBN 9781119037996
• M. Dawson, “Python Programming for the Absolute Beginner”, 3rd edition, 2011, Cengage, ISBN 9781435455009

Learning outcomes

The intended subject specific learning outcomes.
On successfully completing the module students will be able to:
- Demonstrate knowledge and understanding of the advanced concepts and theory within the field of finance and financial technology, and their application to a company's financial decisions
- Apply the research methodologies required to test and evaluate complex finance models
- Demonstrate knowledge and understanding of complex theoretical and practical aspects of key areas of finance and financial technology
- Demonstrate systematic knowledge and understanding of up-to-date empirical literature in the fields of finance and financial technology
- Apply quantitative and statistical methods on financial data

The intended generic learning outcomes.
On successfully completing the module students will be able to:
- Interpret complex financial data and perform quantitative analysis
- Interpret and comprehensively evaluate the results obtained from quantitative analysis
- Demonstrate advanced problem-solving skills
- Analyse important and complex issues relevant to companies' financial decisions
- Conduct in-depth research in the area of finance and financial technology

Notes

  1. Credit level 7. Undergraduate or postgraduate masters level module.
  2. ECTS credits are recognised throughout the EU and allow you to transfer credit easily from one university to another.
  3. The named convenor is the convenor for the current academic session.
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