Computational Finance with a Year in Industry

Core Modules

Year 1

• Data Analysis

  This module covers algorithm-independent machine learning; unsupervised learning and clustering; exploratory data analysis; Bayesian methods; Bayes networks and causality; and applications, such as information retrieval and natural language processing. You will develop skills in data analysis, including data mining and statistics.
• Foundations of Finance

  In this module, you will develop an understanding of the technical, analytical and quantitative methods used for analysing financial and equity markets. You will look at the theory of choice under uncertainty, and the modern theories of asset pricing and asset valuation, with consideration for the concepts of arbitrage pricing and the notion of market completeness.

• Investment and Portfolio Management

  In this module you will be introduced to the underlying theory and empirical evidence in portfolio management and its practice in the financial sector. Portfolio theory is blended with practical issues encountered in the investment process, and you will cover topics which include identifying investor objectives and constraints, recognizing risk and return characteristics of investment vehicles, developing strategic asset allocations among equity, managing portfolio risk, increasing portfolio return, and evaluating portfolio and manager performance relative to investment objectives and other appropriate benchmarks. You will develop an understanding of how funds are allocated in portfolio construction, and look at security analysis, optimal portfolio selection and delegated portfolio management.
• Ethics in Advanced Computing and Artificial Intelligence

  This course is designed to enhance your awareness of the many ethical implications of working with advanced technology. The course recognises that the ethical issues in computing and AI come to the forefront through developments in technology, bringing new responsibility for novel ethical, social, and legal implications of technology almost on a daily basis.

• Individual Project

  The individual project provides you will the opportunity to demonstrate independence and originality, to plan and organise a large project over a long period, and to put into practice some of the techniques you have been taught throughout the programme.
• Advanced Topics Seminar
• Academic Integrity

  This module will describe the key principles of academic integrity, focusing on university assignments. Plagiarism, collusion and commissioning will be described as activities that undermine academic integrity, and the possible consequences of engaging in such activities will be described. Activities, with feedback, will provide you with opportunities to reflect and develop your understanding of academic integrity principles.

   

Year 2

• Year in Industry

  You will spend this year on a work placement. You will be supported by the Department of Computer Science and the Royal Holloway Careers and Employability Service to find a suitable placement. This year forms an integral part of the degree programme and you will be asked to complete assessed work. The mark for this work will count towards your final degree classification.
• Individual Project

  You will carry out an extended piece of individual work under the supervision of an academic member of staff, including the preparation of a dissertation and any programs you may have written. Your project may stress theoretical, methodological, or implementation aspects of a problem or case study, and you may wish to build on the experience that you will have gained during your placement.

Optional Modules

Below is a taster of some of the exciting optional modules that students on the course could choose from during this academic year. Please be aware these do change over time, and optional modules may be withdrawn or new ones added.

Year 1

• Machine Learning

  In this module you will develop an understanding of modern machine learning techniques and gain practical experience in developing machine learning systems. You will look at the main advantages and limitations of the various approaches to machine learning and examine the features of specific machine-learning algorithms. You will also consider how the ideas and algorithms of machine learning can be applied in other fields, including medicine and industry.
• Principles of Computation and Programming

  In this module you will develop an understanding of the basics of algorithmic thinking and problem solving using programming. You will become familiar with using the Java programming language, examining particular features and constructs as well as basics of object-oriented programming. You will use these to solve specific algorithmic tasks and evaluate programming solutions.
• Methods of Computational Finance

  In this module you will develop an understanding of the mathematical and computational models underlying derivative securities. You will learn how to apply techniques for pricing derivatives and dynamic hedging, and look at the market efficiency hypothesis and its applications in examining financial techniques. You will also consider models of risk exposure and the techniques used for calculating value at risk.
• Intelligent Agents and Multi-Agent Systems

  In this module you will develop an understanding of the notion of an agent, and how agents are distinct from other software paradigms. You will analyse the characteristics of applications that lend themselves to an agent-oriented solution and consider the key issues associated with constructing agents capable of intelligent autonomous action. You will look at the key issues in designing societies of agents that can effectively cooperate in order to solve problems and evaluate the key types of multi-agent interactions possible in such systems. You will also examine the main application areas of agent-based solutions, developing a meaningful agent-based system using a contemporary agent development platform.
• Online Machine Learning

  In this module you will develop an understanding of the on-line framework of machine learning for issuing predictions or decisions in real-time. You will learn about protocols, methods and applications of on-line learning, covering probabilistic models based on Markov chains and their applications, such as PageRank and Markov Chain Monte-Carlo. You will examine the time series models, exploring their connections with Kalman filters, and learning models based on the prequential paradigm, including prediction with expert advice, aggregating algorithm, sleeping and switching experts. You will also consider universal algorithms, their application to portfolio theory, and how prediction within a confidence framework is achieved.
• Large-scale Data Storage and Processing

  In this module you will develop an understanding of the underlying principles of large-scale data storage and processing frameworks. You will look at the opportunities and challenges of building massive scale analytics solutions, gaining hands-on experience in using large and unstructured data sets for analysis and prediction. You will examine the techniques and paradigms for querying and processing massive data sets, such as MapReduce, Hadoop, data warehousing, SQL for data analytics, and stream processing. You will consider the fundamentals of scalable data storage, including NoSQL databases, and will design, develop, and evaluate an end-to -nd analytics solution combining large-scale data storage and processing frameworks.
• Business Intelligence Systems, Infrastructures and Technologies

  In this module you will develop an understanding of the role of business intelligence systems in the IT environment of modern organisations. You will look at the concepts, terminology and architectures of data warehouses and business intelligence solutions, considering data modelling concepts and design solutions using dimensional modelling. You will examine the key elements of business intelligence applications such as data analysis, data mining and dashboards, and evaluate aspects of visualisation and the relationship between business intelligence solutions and CRM and ERP systems. You will also gain hands-on experience using industrial business intelligence tools.
• Visualisation and Exploratory Analysis

  In this module you will develop an understanding of the principles of statistical visualisation and open-ended exploratory analysis of data. You will look at the construction of linear projections of multivariate data and non-linear dimensions reduction methods. You will gain practical experience in using standard graph visualisation methods and evaluating results, and consider how to avoid data snooping. You will also critically evaluate choices in representational mode, glyph design, and colour design for presentation graphics
• Fixed Income Securities and Derivatives

  This module provides an overview of a very significant area of the contemporary financial world. The first part gives a coverage of the important elements of the default-free fixed income securities market, and the second part covers the derivatives market. The module focuses on the analytical tools used in portfolio management and risk management. For bond portfolios, these tools include yield curve construction, duration, convexity and formal term structure models. For derivatives, the emphasis will be on valuation, trading mechanisms and management of credit risk.

• Private Equity and Corporate Governance

  The aim of this module is to give a more in-depth look at corporate finance issues related to company evaluation and with the main user of those evaluations e.g. private equity and Venture Capitalist (VC). It also aims at giving a practical approach to key aspects of corporate valuation, for example, leverage. Finally, this module will detail the dynamic and the valuation specificity for the different stages of the company from the start up, to the IPO to the mature company doing merger and acquisition.

• Databases

  The aim of this module is to teach students a number of database concepts and techniques. This ranges from the specification and modelling stages to the implementation of relational databases. The course also introduces students to the usage of databases from software applications. The content of the course includes:

  • Data modelling: views, subschema, data dictionary, data independence, entity relationship model.
  • The relational model: relations, attributes, domains, relational algebra.
  • Database design: normalisation, normal forms, entities and attributes
  • SQL: basic SQL, correspondence between the relational model and SQL commands, simple queries, combination and sub-queries
  • Administration and implementation: integrity, recovery from failure, concurrency, deletion and updating, forms, report writing.
• Advanced Distributed Systems and Communication Networks

  The module covers the fundamental principles of building modern distributed systems, for example in the context of the Internet of Things (IoT), focussing, in particular, on two central components of the IoT reference architecture-cloud infrastructure and wireless networking. The module will discuss major challenges found in these environments (such as massive scales, wide distribution, decentralisation, unreliable communication links, component failures and network partitions) and general approaches for dealing with these. Topics covered will include abstract models (such as the synchronous and asynchronous distributed computing models, models for wireless networks); algorithmic techniques (such as distributed coordination, the fault-tolerant design of distributed algorithms, synchronization techniques); and practical case studies. You will also have an opportunity to implement various components of a realistic distributed system through a series of formative coursework assignments, lab practicals, and a final project.
• Artificial Intelligence Principles and Techniques

  This module focuses on acquiring a deep understanding of foundational AI principles and techniques to model complex real-world problems as well as writing algorithms and problems to solve them. The module will start with an introduction to AI that will define core AI concepts, provide the philosophical foundations of AI and discuss ethical issues in this field. The module will continue by covering intelligent agents and classical search to then move to local search and optimisation algorithms. Finally, adversarial search and constraint satisfaction problems will also be taught. All these topics will be covered both from a theoretical point of view, during the lectures, and from a practical point of view during the labs.

• Foundations of Corporate Finance

  This postgraduate module introduces the key principles of corporate finance and financial decision-making. Students will learn how organisations evaluate investment opportunities, raise finance and create shareholder value through effective financial management. The module provides a foundation for advanced study in finance and related disciplines.

• Deep Learning

  The aim of the course is to give students an introduction to deep learning that covers neural network optimisation by gradient descent from first principles, and which also gives a broader introduction to a range of advanced architectures, with hands-on implementation. The course starts by considering models of artificial neural networks for supervised learning, and introduces notions of activation function, loss function, and computation of loss-gradients using back-propagation with the chain rule. Neural network learning with back-propagation and different gradient descent algorithms will be covered in detail, and visualised in lab-sessions. Next, the 'disappearing gradient' problem in deep architectures will be raised, and methods for resolving this problem will be discussed. A range of deep architectures will be described for discriminative learning, generative learning and learning of representations, and for reinforcement learning. Students will implement a deep architecture in a project assignment at the end of the course.

• Autonomous Intelligent Systems

  This specialist module focuses on acquiring a deep understanding of the principles and techniques that are needed to design and build autonomous intelligent systems (AISs). The module will start with an introduction to AISs and real-world examples of them. It will then cover knowledge representation and engineering techniques based on formal logic. The module will then tackle autonomous decision making techniques, from AI planning to probabilistic reasoning and Markov Decision Processes. Reinforcement learning and techniques for cooperation and coordination between artificial agents will also be taught. All these topics will be discussed both from a theoretical point of view, during the lectures, and from a practical point of view, during the labs.

   

• Natural Language Processing

  The aim of this module is to teach the necessary background knowledge and practical techniques - especially deep learning - needed to apply natural language processing to large, real-life text-based projects. A brief survey of computational linguistic theory will include notions of syntax, semantics, and pragmatics. Practical techniques for preparing and pre-processing text will be taught in lab sessions. Typical commercial applications of NLP will be surveyed, with practical examples. Standard NLP techniques covered will include: topic modelling and LDA, and construction of word-embeddings.

   

• Reinforcement Learning

  This module considers the philosophical problem of defining 'intelligence', drawing on perspectives from AI, psychology, and law, to motivate Reinforcement Learning (RL) as the optimisation of a policy within a Markov Decision Process (MDP). It introduces classic dynamic programming approaches to computing an optimal policy, before covering algorithms for learning optimal policies, including Q-learning, Sarsa, and actor-critic methods, as well as policy gradient methods and bandit algorithms. Deep RL is explored in depth, including deep Q-networks and the AlphaZero family of algorithms. The module closes by critically examining whether RL offers an adequate model of human and animal decision-making, and of general intelligence itself.