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FINN3071: Computational Quantitative Finance

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Type	Tied
Level	3
Credits	20
Availability	Available in 2023/24
Module Cap	None.
Location	Durham
Department	Finance

Prerequisites

Corporate Finance (FINN2041) AND Financial Econometrics 1 (FINN2031)

Corequisites

Security Investment Analysis (FINN3021)

Excluded Combinations of Modules

None

Aims

To provide students with knowledge and understanding of questions in empirical finance linked to appropriate methodologies for their analysis.
To enable students to rigorously explore issues in finance by using computer programming and professional data sources.
To become familiar with the techniques for analysis employed in the quantitative finance industry and to evaluate the trading strategies used by market professionals.
To provide students with the opportunity to develop the ability to critically evaluate academic literature relating to empirical and computational finance.
To offer the opportunity to develop key skills.

Content

Introduction to empirical and computational finance
Using 'R' (and/or similar, e.g. Matlab) with applications in finance: Syntax, data types and writing simple programs; Importing data and working with financial time series (including securities, foreign exchange and commodities); Plotting and visualisation of data; Loops and functions.
Computing asset returns.
Mean-variance portfolio theory, constructing portfolios and statistical analysis of portfolios.
The nature of quantitative trading strategies.
Implementing quantitative trading strategies, including: reversal/continuation; fundamental analysis methods; technical analysis methods.
Evaluating and analysing performance of trading strategies.
Algorithmic trading
High frequency trading.
The focus of the module is on undertaking empirical investigation of issues in finance rather than being centred on econometrics. However, econometrics will be used in an applied sense to provide rigorous support in evaluating results.

Learning Outcomes

Subject-specific Knowledge:

On completion of this module students should have:
developed an advanced knowledge of the central issues in empirical finance;
understanding of a programming language and its use to rigorously explore empirical issues in finance;
understanding of how programs may be employed.

Subject-specific Skills:

On completion of this module students should have:
academic skill to analyse the role of computational analysis in financial markets;
the ability to evaluate the usage of computational and empirical techniques by market professionals;
developed skills to write and develop programs to evaluate empirical finance issues present in relevant academic literature;
written programs to critically analyse trading strategies employed by market participants, such as technical analysis, considering factors such as risk and slippage;
become skilled at advanced usage of professional finance data sources such as Bloomberg and Datastream and how to link these to appropriate software for analysis.

Key Skills:

In addition, students will have had the opportunity to further develop the following key skills:
written communication - through the formative and summative assignments;
planning and organising - observing the strict assignment deadlines;
problem solving - by applying appropriate analytical and quantitative skills to evaluate theoretical concepts using real data;
initiative - by searching relevant literature, identifying recent developments in software packages and information in preparation of the summative assignment;
numeracy - by analysing financial data;
computer literacy - by writing complex programs to analyse financial data and evaluate trading systems. In addition, word-processing the assignments; downloading assignment information and notes from Learn Ultra.

Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module

Teaching is by lectures and workshops.
Learning takes place through attendance at lectures and participation in workshops, including independent study in solving assigned problems.
Formative assessment is by means of an assignment. Feedback will be provided to each individual student which can then be built upon and developed for the summative coursework assignment. Written feeback and general suggestions will be communicated directly to students.
Summative assessment is by means of two assignments designed around the development of programs and methodologies to analyse empirical problems and trading strategy issues, using data unique to each student.

Teaching Methods and Learning Hours

Activity	Number	Frequency	Duration	Total	Monitored
Lectures	8	Fortnightly in terms 1 and 2	2 hrs	16
Workshops	8	4 in term 1, 4 in term 2	2 hrs	16	Yes
Preparation and Reading				168
Total				200

Summative Assessment

Component: Assignment		Component Weighting: 100%
Element	Length / Duration	Element Weighting	Resit Opportunity
Written coursework assignment 1	1500 words	40	same
Written coursework assignment 2	3000 words	60	same

Formative Assessment

One 1500 word assignment. This will be centred around problem solving using appropriate software.

More information

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