Durham University
Programme and Module Handbook

Undergraduate Programme and Module Handbook 2017-2018 (archived)

Module ECOS3341: Computational Quantitative Finance

Department: Business School (Economics and Finance) [Queen's Campus, Stockton]

ECOS3341: Computational Quantitative Finance

Type Tied Level 3 Credits 20 Availability Not available in 2017/18 Module Cap Location Queen's Campus Stockton
Tied to N305
Tied to N306
Tied to N307
Tied to NN43
Tied to NN34
Tied to N302
Tied to N304
Tied to N402
Tied to N403
Tied to N405


  • Corporate Finance (ECOS 2011) AND Introduction to Financial Econometics


  • Security Investment Analysis (ECOS 3261)

Excluded Combination of Modules

  • None


  • 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.


  • 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 main 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, taking into account 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; revising relevant material in preparation for examinations;
  • problem solving - e.g. by applying appropriate analytical and quantitative skills to evaluate theoretical concepts using real data;
  • initiative - e.g. by searching relevant literature, identifying recent developments in software packages and information in preparation of the summative assignment;
  • numeracy - e.g. by analysing financial data;
  • computer literacy - e.g. by writing complex programs to analyse financial data and evaluate trading systems. In addition, word-processing the assignments; downloading assignment information and notes from DUO.

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 beforehand.
  • 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. Overall feedback and general suggestions for improvements will be posted on DUO.
  • 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/Hours
Lectures 8 Fortnightly in terms 1 and 2 2 hour 16
Workshops 8 4 in each of terms 1 and 2 2 hour 16
Preparation and Reading 168
Total 200

Summative Assessment

Component: Assignment Component Weighting: 100%
Element Length / duration Element Weighting Resit Opportunity
Written coursework assignment I 1500 words 40% Same
Written coursework assignment II 3000 words 60% Same

Formative Assessment:

One 1,500 word assignment. This will be centred around problem solving using appropriate software.

Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University