There is a wide choice of dissertation topics provided by all 4 departments.
This module will initially introduce students to core concepts in finance like time value of money, net present value analysis and alternative investment rules to assess investment decisions taken by firms and then moves on to the introduction of basic concepts related to financial markets, including definitions of key assets and market types as well as an understanding of the economics of financial markets with a focus on their functions, participants and organisational forms.
For the final semester, following your summer internship, you will take a mixture of required and elective courses.
The aim of the module is to provide students with the hands-on time-series skills to competently estimate, test and interpret market-risk forecasting and control models & techniques which are required in the current regulatory environment: Value-at-Risk, Expected Shortfall, backtesting, extreme-value distributions, and copula models. Programs “owned” by business schools can be strong on financial markets but pay less attention to the mathematical modeling. This course will cover how such data can be modelled, how inferences about the models can be made, and how statistical models can be used for predicting future outcomes and behaviour. Sophisticated methods of data visualization, mining, and modeling can extract useful information from the flood of complex, noisy, big data that arises from financial markets. Analysis of appropriate stochastic models has become extremely important in recent years, such as for accurately pricing options. Graduates of this course leave with a complete toolkit of skills, from big data mining techniques to Python programming.
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You will learn about the inner workings of the financial markets through your required summer internship, returning to the program in the fall with a much better sense of the full-time position you wish to pursue. It focuses on the implications of investor behaviour and capital market imperfections (such as limits to arbitrage) for investment management. This module provides extensive coverage of methods used for valuing derivative securities in the investment banking industry, and includes an introduction to stochastic calculus.
Due to their inherent randomness, it is natural to model financial and economic systems using probability models and stochastic processes. Then, the extrapolative forecasting methods of exponential smoothing and ARIMA models are considered. Neither of these kinds of programs has the laser focus of MSCF, applying machine learning and other data science tools to quantitative finance. Taken as a whole, students receive broad training and experience in both the theory and the practical implementation of data science methods. The module will also include a series of workshops in which you will learn how to use the SAS Enterprise Miner software for data mining (a software skill much sought after in the job market) and how to use it on real datasets in a real world scenario. There is substantial amounts of data collected which relates to business, financial or economic applications.
63% of MSCF Students in 2019 received return full-time offers following their Summer Internship. Likelihood inference: definition; calculating mles; asymptotic results; calculating confidence intervals; likelihood ratio statistic. All MSCF courses are developed expressly for the intended career paths our students, with introductory courses in the early fall leading to core courses throughout the first year followed by additional core courses and various electives in the final semester, allowing you to focus on the area of quantitative finance of most interest to you. Quantitative Methods for Finance and Investment is an option module within the Foundation Degree programme and is designed for students whose work will involve an understanding of quantitative methods. This module covers ideas from Extreme Value Theory which give a sound mathematical basis to such extrapolation, and shows practically how it can be used to give accurate assessments of financial risk in a range of scenarios.
Stochastic Calculus is a theory that enables the calculation of integrals with respect to stochastic processes. Gain experience of statistical packages to prepare you for your future career. In your first term from October to December, you will take five core modules as below.
The Careers Team at LUMS helps you shape your career plans and supports your job-hunting process in a variety of ways, including personalised one-to-one support and interactive workshops on areas such as career strategies, writing CVs and applications, interview skills, psychometric testing, what to expect at assessment centres, and online networking strategies. Introduction to probability building from the axioms: Univariate random variables: standard distributions and their justifications, interrelations, and properties; Multivariate random variables: marginals and copulas, their decomposition as a series of conditionals, dependence measures, and standard distributions; Simulation of random variables and approximation of their properties by Monte Carlo; Poisson and Gaussian processes covering stationarity, conditional independence, and standard examples; Simulation of a range of stochastic processes to approximate properties. A detailed treatment of causal modelling follows, with a full evaluation of the estimated models.
The module will cover a wide range of data mining methods, including simple algorithms such as decision trees all the way to state of the art algorithms of artificial neural networks, support vector regression, k-nearest neighbour methods etc. It will also critically analyse various portfolio management approaches used by professional investors in order to understand the strengths and weaknesses of these approaches.
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MSCF’s highly-integrated, interdisciplinary curriculum is well-balanced between theory and practice. CFRM 420: Introduction to Computational Finance and Financial Econometrics (3) This course is an introduction to computational finance and financial econometrics. Also, it is suitable for newbies, intermediates, as well as experts. This module focuses on how financial theories are applied to investment management decisions. This module gives a thorough (but not too rigorous) introduction to stochastic processes in general and their use in modeling in business, finance and economic applications. Conversely, programs “owned” by math departments are often highly theoretical and less focused on “real world” applicability.
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