Computational Finance with a Year in Industry MSc

Royal Holloway, University of London

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.

In this module you will develop an understanding of the core concepts in data and information management, looking at the role of databases and database management systems in managing organisational data and information. You will learn how to identify organisational information requirements, model them using conceptual data modelling techniques, convert the conceptual data models into relational data models and verify their structural characteristics using normalisation techniques. You will gain experience in designing and implementing a relational database using an industrial database management system, and examine how to manipulate data using SQL.

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.

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.

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.

In this module you will learn how to use MATLAB (Matrix Laboratory) and WEKA (Waikato Environment for Knowledge Analysis) as tools for machine learning and data mining. For MATLAB, you will develop an understanding of how to input and output data using vectors, arrays and matrics; learn techniques in data visualization, including plots in 2 and 3 dimensions, scatter plots, barplots, and histograms; and learn how to implement concepts from linear algebra and statistics, including probability and matrix decompositions. For WEKA, you will develop an understanding of how to use the software as a tool for training and testing, predicting generalisation performance, and cross-validation; and learn how to implement decision trees, na? Bayes classifiers, and clustering methods.