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
• Total contact hours: 35
• Private study hours: 115
• Total study hours: 150
Main assessment methods:
Individual Report – 2000 words (30%)
Individual Research Project – 3000-3500 words (70%)
Reassessment methods:
100% coursework
• 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
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
University of Kent makes every effort to ensure that module information is accurate for the relevant academic session and to provide educational services as described. However, courses, services and other matters may be subject to change. Please read our full disclaimer.
