computational finance thesis topics

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Supervised deep learning in computational finance

• Numerical Analysis

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T1 - Supervised deep learning in computational finance

AU - Liu, S.

N2 - Mathematical modeling and numerical methods play a key role in the field of quantitative finance, for example, for financial derivative pricing and for risk management purposes. Asset models of increasing complexity, like stochastic volatility models (local stochastic volatility, rough volatility based on fractional Brownian motion) require advanced, efficient numerical techniques to bring them successfully into practice. When computations take too long, an involved asset model is not a feasible option as practical considerations demand a balance between the model’s accuracy and the time it takes to compute prices and risk management measures. In the big data era, typical basic computational tasks in the financial industry are often involved and computationally intensive due to the large volumes of financial data that are generated nowadays. Besides the traditional numerical methods in financial derivatives pricing in quantitative finance (like partial differential equation (PDE) discretization and solution methods, Fourier methods, Monte Carlo simulation), recently deep machine learning techniques have emerged as powerful numerical approximation techniques within scientific computing. Following the so-called Universal Approximation Theory, we will employ deep neural networks for financial computations, either to speed up the solution processes or to solve highly complicated, highdimensional, problems in finance. Particularly, we will employ supervised machine learning techniques, based on intensive learning of so called labeled information (input-output relations, where sets of parameters form the input to a neural network, and the output to be learned is a solution to a financial problem).

AB - Mathematical modeling and numerical methods play a key role in the field of quantitative finance, for example, for financial derivative pricing and for risk management purposes. Asset models of increasing complexity, like stochastic volatility models (local stochastic volatility, rough volatility based on fractional Brownian motion) require advanced, efficient numerical techniques to bring them successfully into practice. When computations take too long, an involved asset model is not a feasible option as practical considerations demand a balance between the model’s accuracy and the time it takes to compute prices and risk management measures. In the big data era, typical basic computational tasks in the financial industry are often involved and computationally intensive due to the large volumes of financial data that are generated nowadays. Besides the traditional numerical methods in financial derivatives pricing in quantitative finance (like partial differential equation (PDE) discretization and solution methods, Fourier methods, Monte Carlo simulation), recently deep machine learning techniques have emerged as powerful numerical approximation techniques within scientific computing. Following the so-called Universal Approximation Theory, we will employ deep neural networks for financial computations, either to speed up the solution processes or to solve highly complicated, highdimensional, problems in finance. Particularly, we will employ supervised machine learning techniques, based on intensive learning of so called labeled information (input-output relations, where sets of parameters form the input to a neural network, and the output to be learned is a solution to a financial problem).

U2 - 10.4233/uuid:5966c116-1108-4ecf-8f86-3d8348a3504a

DO - 10.4233/uuid:5966c116-1108-4ecf-8f86-3d8348a3504a

M3 - Dissertation (TU Delft)

SN - 978-94-6384-191-7

Computational Finance: An Introduction

• First Online: 01 January 2011

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• Jin-Chuan Duan 4 ,
• James E. Gentle 5 &
• Wolfgang Karl Härdle 6 , 7  

Part of the book series: Springer Handbooks of Computational Statistics ((SHCS))

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This book is the fourth volume of the Handbook of Computational Statistics . As with the other handbooks in the series, it is a collection of articles on specific aspects of the broad field, written by experts in those areas. The purpose is to provide a survey and summary on each topic, ranging from basic background material through the current frontiers of research. The development of the field of computational statistics has been rather fragmented. We hope that the articles in this handbook series can provide a more unified framework for the field.

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Department of Mathematics, National University of Singapore, Singapore, 119077, Singapore

Jin-Chuan Duan

Department of Computer Science, George Mason University, Fairfax, VA, USA

James E. Gentle

Ladislaus von Bortkiewicz Chair of Statistics and CASE - Center for Applied Statistics and Economics, Humboldt-Universität zu Berlin, Spandauer Straße 1, 10178, Berlin, Germany

Wolfgang Karl Härdle

Graduate Institute of Statistics, CDA - Centre for Complex Data Analysis, National Central University, No. 300, Jhongda Rd., Jhongli City, Taoyuan County, 32001, Taiwan, (R.O.C.)

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, Risk Management Institute, National University of Singapore, 21 Heng Mui Keng Terrace, Level 4, Singapore, 119613, Singapore

L.v.Bortkiewicz Chair of Statistics, C.A.S.E. Centre f. Appl. Stat. & Econ., Humboldt-Universität zu Berlin, Unter den Linden 6, Berlin, 10099, Germany

, Department of Computational and Data Sci, George Mason University, University Drive 4400, Fairfax, 22030, Virginia, USA

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Duan, JC., Gentle, J.E., Härdle, W.K. (2012). Computational Finance: An Introduction. In: Duan, JC., Härdle, W., Gentle, J. (eds) Handbook of Computational Finance. Springer Handbooks of Computational Statistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17254-0_1

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MSc Theses on Machine Learning and Computational Finance 2020

MSc Thesis “The Performance of Artificial Neural Networks on Rough Heston Model”

By Chun Kiat Ong, University of Birmingham U.K. 2020

Supervisor: Dr. Daniel J. Duffy, Datasim Education BV Amsterdam

September 24 2020

Chun Kiat Ong The Performance of Artificial Neural Networks on Rough Heston Model

This is one of the first MSc theses to address the full software lifecycle of the analysis (maths), design (Structured Analysis/top-down decomposition) and implementation (C++, Python, ANN, Keras, TensorFlow) to computing option prices and implied volatility under rough Heston model.  This new model resolves a number of issues surrounding the original Heston model.

We compare the solutions based on ANNs with more traditional computational solutions; based on our level playing field analysis (that is, we compare “apples with apples”), for this problem the performance of the ANN solution is 7 times slower for option pricing and 17 times slower for implied volatility modelling than traditional methods. Of course, this is only one example but it is hard evidence nonetheless.

There are few articles that discuss the application of ANNs to computational finance and the ones that have been published claim outlandish performance improvements (10,000 times faster) or claim that they can solve 100-factor partial differential equations (PDEs) with Deep Learning techniques.

The popularity of Machine and Statistical Learning techniques (ML for short) of recent years can be found in pattern and image recognition, classification, social media services, online fraud detection, to name a few. More recently, there has been a flurry of activity and interest in applying ML to financial applications, in particular, option pricing, calibration and volatility modelling. The relatively few published articles devoted to these topics are testament to the fact that much needs to be done in order to advance the current mathematical and software knowledge from ad-hoc solutions, trial-and-error experimentation and folklore to defined processes and standardised design patterns.

The main goal of this thesis is to discuss the applicability of ML (in particular, Artificial Neural Networks (ANN)) to option pricing and implied volatility using the rough Heston model. This is a generalisation and improvement of the popular Heston model to address the latter’s difficulty in matching observed vanilla option prices. We shall deem ML to be successful (or not) by comparing it with more traditional methods such as analytical solutions and numerical methods. The main requirements and metrics are that the new methods be accurate (in some sense) and have good run-time performance. In any case, we wish to unambiguously quantify these metrics.

The approach taken in this thesis is state-of-art and original in a number of ways:

• Rough Heston and the related (numerical) mathematics (fractional Riccati equation).
• The optimal combination (speed, usability) of C++ and Python.
• The software design is based on Duffy’s Domain Architectures to partition a software system into loosely-coupled and autonomous subsystems. We have a defined process to effect this decomposition, thus allowing the student to “hit the ground running” and increase productivity.
• Taking the mystique out of ML applications by viewing them as standard software systems with “embedded AI components”, which are basically algorithms that have been written in C++ and Fortran and wrapped into libraries that can be called from Python.
• Few MSc theses reach this level. There are a number of reasons for this achieved level of expertise.

Prediction is difficult, especially predicting the Future

The area of Machine Learning and its realisation in software reminds me of the heyday of the Object-Oriented (OO) Paradigm, characterised by ad hoc solutions and by solving problems in  any way that developers could manage. After some time developers were able to distinguish in these ad hoc solutions things that usually work and things that do not usually work (a classic example is that class inheritance is a mixed blessing). The ones that work entered the folklore and people tell each other about them informally. We codify the folklore as written heuristics and rules of procedures as it becomes more and more systematic. Eventually this codification becomes crisp enough to support models and theories, together with the associated mathematics (Duffy 2004, Shaw and Garlan 1996).

In the case of Machine Learning we see some opportunities for improvement by trying to replace ad hoc solutions by more robust ones:

• Broadening the mathematical scope of current practice … much of the theory seems to be based on linear algebra, discrete mathematics and finite dimensional problems. These methods will face a brick wall at some stage. It is worth mentioning that many of the ML algorithms can be replaced by other algorithms based on advanced mathematics such as Functional Analysis and Hilbert Space (for example, RKHS) methods.
• A more disciplined approach to software design and avoiding “balls of mud” code. Avoiding the scenario of next-generation armies of Python maintenance programmers. In particular, data scientists and non-programmers need to develop their software skills.
• The optimal combination of C++ and Python  for ML applications.

If you have any queries please do not hesitate to contact me [email protected]

Duffy, Daniel J. Duffy, Domain Architectures Wiley 2004.

Duffy, Daniel J. Duffy, Financial Instrument Pricing using C++, second edition , Wiley 2018.

Mandara, Dalvir , Artificial Neural Networks for Black-Scholes Option Pricing and Prediction of Implied Volatility for the SABR Stochastic Volatility Model, MSc Mathematical Finance - 2018/19 University of Birmingham.

https://www.datasim.nl/application/files/8115/7045/4929/1423101.pdf

Shaw, M. and Garlan, D. Software Architecture, Prentice-Hall 1996.

Research Topics & Ideas: Finance

120+ Finance Research Topic Ideas To Fast-Track Your Project

If you’re just starting out exploring potential research topics for your finance-related dissertation, thesis or research project, you’ve come to the right place. In this post, we’ll help kickstart your research topic ideation process by providing a hearty list of finance-centric research topics and ideas.

PS – This is just the start…

We know it’s exciting to run through a list of research topics, but please keep in mind that this list is just a starting point . To develop a suitable education-related research topic, you’ll need to identify a clear and convincing research gap , and a viable plan of action to fill that gap.

If this sounds foreign to you, check out our free research topic webinar that explores how to find and refine a high-quality research topic, from scratch. Alternatively, if you’d like hands-on help, consider our 1-on-1 coaching service .

Overview: Finance Research Topics

• Corporate finance topics
• Investment banking topics
• Private equity & VC
• Asset management
• Hedge funds
• Financial planning & advisory
• Quantitative finance
• Treasury management
• Financial technology (FinTech)
• Commercial banking
• International finance

Corporate Finance

These research topic ideas explore a breadth of issues ranging from the examination of capital structure to the exploration of financial strategies in mergers and acquisitions.

• Evaluating the impact of capital structure on firm performance across different industries
• Assessing the effectiveness of financial management practices in emerging markets
• A comparative analysis of the cost of capital and financial structure in multinational corporations across different regulatory environments
• Examining how integrating sustainability and CSR initiatives affect a corporation’s financial performance and brand reputation
• Analysing how rigorous financial analysis informs strategic decisions and contributes to corporate growth
• Examining the relationship between corporate governance structures and financial performance
• A comparative analysis of financing strategies among mergers and acquisitions
• Evaluating the importance of financial transparency and its impact on investor relations and trust
• Investigating the role of financial flexibility in strategic investment decisions during economic downturns
• Investigating how different dividend policies affect shareholder value and the firm’s financial performance

Investment Banking

The list below presents a series of research topics exploring the multifaceted dimensions of investment banking, with a particular focus on its evolution following the 2008 financial crisis.

• Analysing the evolution and impact of regulatory frameworks in investment banking post-2008 financial crisis
• Investigating the challenges and opportunities associated with cross-border M&As facilitated by investment banks.
• Evaluating the role of investment banks in facilitating mergers and acquisitions in emerging markets
• Analysing the transformation brought about by digital technologies in the delivery of investment banking services and its effects on efficiency and client satisfaction.
• Evaluating the role of investment banks in promoting sustainable finance and the integration of Environmental, Social, and Governance (ESG) criteria in investment decisions.
• Assessing the impact of technology on the efficiency and effectiveness of investment banking services
• Examining the effectiveness of investment banks in pricing and marketing IPOs, and the subsequent performance of these IPOs in the stock market.
• A comparative analysis of different risk management strategies employed by investment banks
• Examining the relationship between investment banking fees and corporate performance
• A comparative analysis of competitive strategies employed by leading investment banks and their impact on market share and profitability

Private Equity & Venture Capital (VC)

These research topic ideas are centred on venture capital and private equity investments, with a focus on their impact on technological startups, emerging technologies, and broader economic ecosystems.

• Investigating the determinants of successful venture capital investments in tech startups
• Analysing the trends and outcomes of venture capital funding in emerging technologies such as artificial intelligence, blockchain, or clean energy
• Assessing the performance and return on investment of different exit strategies employed by venture capital firms
• Assessing the impact of private equity investments on the financial performance of SMEs
• Analysing the role of venture capital in fostering innovation and entrepreneurship
• Evaluating the exit strategies of private equity firms: A comparative analysis
• Exploring the ethical considerations in private equity and venture capital financing
• Investigating how private equity ownership influences operational efficiency and overall business performance
• Evaluating the effectiveness of corporate governance structures in companies backed by private equity investments
• Examining how the regulatory environment in different regions affects the operations, investments and performance of private equity and venture capital firms

Asset Management

This list includes a range of research topic ideas focused on asset management, probing into the effectiveness of various strategies, the integration of technology, and the alignment with ethical principles among other key dimensions.

• Analysing the effectiveness of different asset allocation strategies in diverse economic environments
• Analysing the methodologies and effectiveness of performance attribution in asset management firms
• Assessing the impact of environmental, social, and governance (ESG) criteria on fund performance
• Examining the role of robo-advisors in modern asset management
• Evaluating how advancements in technology are reshaping portfolio management strategies within asset management firms
• Evaluating the performance persistence of mutual funds and hedge funds
• Investigating the long-term performance of portfolios managed with ethical or socially responsible investing principles
• Investigating the behavioural biases in individual and institutional investment decisions
• Examining the asset allocation strategies employed by pension funds and their impact on long-term fund performance
• Assessing the operational efficiency of asset management firms and its correlation with fund performance

Hedge Funds

Here we explore research topics related to hedge fund operations and strategies, including their implications on corporate governance, financial market stability, and regulatory compliance among other critical facets.

• Assessing the impact of hedge fund activism on corporate governance and financial performance
• Analysing the effectiveness and implications of market-neutral strategies employed by hedge funds
• Investigating how different fee structures impact the performance and investor attraction to hedge funds
• Evaluating the contribution of hedge funds to financial market liquidity and the implications for market stability
• Analysing the risk-return profile of hedge fund strategies during financial crises
• Evaluating the influence of regulatory changes on hedge fund operations and performance
• Examining the level of transparency and disclosure practices in the hedge fund industry and its impact on investor trust and regulatory compliance
• Assessing the contribution of hedge funds to systemic risk in financial markets, and the effectiveness of regulatory measures in mitigating such risks
• Examining the role of hedge funds in financial market stability
• Investigating the determinants of hedge fund success: A comparative analysis

Financial Planning and Advisory

This list explores various research topic ideas related to financial planning, focusing on the effects of financial literacy, the adoption of digital tools, taxation policies, and the role of financial advisors.

• Evaluating the impact of financial literacy on individual financial planning effectiveness
• Analysing how different taxation policies influence financial planning strategies among individuals and businesses
• Evaluating the effectiveness and user adoption of digital tools in modern financial planning practices
• Investigating the adequacy of long-term financial planning strategies in ensuring retirement security
• Assessing the role of financial education in shaping financial planning behaviour among different demographic groups
• Examining the impact of psychological biases on financial planning and decision-making, and strategies to mitigate these biases
• Assessing the behavioural factors influencing financial planning decisions
• Examining the role of financial advisors in managing retirement savings
• A comparative analysis of traditional versus robo-advisory in financial planning
• Investigating the ethics of financial advisory practices

The following list delves into research topics within the insurance sector, touching on the technological transformations, regulatory shifts, and evolving consumer behaviours among other pivotal aspects.

• Analysing the impact of technology adoption on insurance pricing and risk management
• Analysing the influence of Insurtech innovations on the competitive dynamics and consumer choices in insurance markets
• Investigating the factors affecting consumer behaviour in insurance product selection and the role of digital channels in influencing decisions
• Assessing the effect of regulatory changes on insurance product offerings
• Examining the determinants of insurance penetration in emerging markets
• Evaluating the operational efficiency of claims management processes in insurance companies and its impact on customer satisfaction
• Examining the evolution and effectiveness of risk assessment models used in insurance underwriting and their impact on pricing and coverage
• Evaluating the role of insurance in financial stability and economic development
• Investigating the impact of climate change on insurance models and products
• Exploring the challenges and opportunities in underwriting cyber insurance in the face of evolving cyber threats and regulations

Quantitative Finance

These topic ideas span the development of asset pricing models, evaluation of machine learning algorithms, and the exploration of ethical implications among other pivotal areas.

• Developing and testing new quantitative models for asset pricing
• Analysing the effectiveness and limitations of machine learning algorithms in predicting financial market movements
• Assessing the effectiveness of various risk management techniques in quantitative finance
• Evaluating the advancements in portfolio optimisation techniques and their impact on risk-adjusted returns
• Evaluating the impact of high-frequency trading on market efficiency and stability
• Investigating the influence of algorithmic trading strategies on market efficiency and liquidity
• Examining the risk parity approach in asset allocation and its effectiveness in different market conditions
• Examining the application of machine learning and artificial intelligence in quantitative financial analysis
• Investigating the ethical implications of quantitative financial innovations
• Assessing the profitability and market impact of statistical arbitrage strategies considering different market microstructures

Treasury Management

The following topic ideas explore treasury management, focusing on modernisation through technological advancements, the impact on firm liquidity, and the intertwined relationship with corporate governance among other crucial areas.

• Analysing the impact of treasury management practices on firm liquidity and profitability
• Analysing the role of automation in enhancing operational efficiency and strategic decision-making in treasury management
• Evaluating the effectiveness of various cash management strategies in multinational corporations
• Investigating the potential of blockchain technology in streamlining treasury operations and enhancing transparency
• Examining the role of treasury management in mitigating financial risks
• Evaluating the accuracy and effectiveness of various cash flow forecasting techniques employed in treasury management
• Assessing the impact of technological advancements on treasury management operations
• Examining the effectiveness of different foreign exchange risk management strategies employed by treasury managers in multinational corporations
• Assessing the impact of regulatory compliance requirements on the operational and strategic aspects of treasury management
• Investigating the relationship between treasury management and corporate governance

Financial Technology (FinTech)

The following research topic ideas explore the transformative potential of blockchain, the rise of open banking, and the burgeoning landscape of peer-to-peer lending among other focal areas.

• Evaluating the impact of blockchain technology on financial services
• Investigating the implications of open banking on consumer data privacy and financial services competition
• Assessing the role of FinTech in financial inclusion in emerging markets
• Analysing the role of peer-to-peer lending platforms in promoting financial inclusion and their impact on traditional banking systems
• Examining the cybersecurity challenges faced by FinTech firms and the regulatory measures to ensure data protection and financial stability
• Examining the regulatory challenges and opportunities in the FinTech ecosystem
• Assessing the impact of artificial intelligence on the delivery of financial services, customer experience, and operational efficiency within FinTech firms
• Analysing the adoption and impact of cryptocurrencies on traditional financial systems
• Investigating the determinants of success for FinTech startups

Commercial Banking

These topic ideas span commercial banking, encompassing digital transformation, support for small and medium-sized enterprises (SMEs), and the evolving regulatory and competitive landscape among other key themes.

• Assessing the impact of digital transformation on commercial banking services and competitiveness
• Analysing the impact of digital transformation on customer experience and operational efficiency in commercial banking
• Evaluating the role of commercial banks in supporting small and medium-sized enterprises (SMEs)
• Investigating the effectiveness of credit risk management practices and their impact on bank profitability and financial stability
• Examining the relationship between commercial banking practices and financial stability
• Evaluating the implications of open banking frameworks on the competitive landscape and service innovation in commercial banking
• Assessing how regulatory changes affect lending practices and risk appetite of commercial banks
• Examining how commercial banks are adapting their strategies in response to competition from FinTech firms and changing consumer preferences
• Analysing the impact of regulatory compliance on commercial banking operations
• Investigating the determinants of customer satisfaction and loyalty in commercial banking

International Finance

The folowing research topic ideas are centred around international finance and global economic dynamics, delving into aspects like exchange rate fluctuations, international financial regulations, and the role of international financial institutions among other pivotal areas.

• Analysing the determinants of exchange rate fluctuations and their impact on international trade
• Analysing the influence of global trade agreements on international financial flows and foreign direct investments
• Evaluating the effectiveness of international portfolio diversification strategies in mitigating risks and enhancing returns
• Evaluating the role of international financial institutions in global financial stability
• Investigating the role and implications of offshore financial centres on international financial stability and regulatory harmonisation
• Examining the impact of global financial crises on emerging market economies
• Examining the challenges and regulatory frameworks associated with cross-border banking operations
• Assessing the effectiveness of international financial regulations
• Investigating the challenges and opportunities of cross-border mergers and acquisitions

Choosing A Research Topic

These finance-related research topic ideas are starting points to guide your thinking. They are intentionally very broad and open-ended. By engaging with the currently literature in your field of interest, you’ll be able to narrow down your focus to a specific research gap .

When choosing a topic , you’ll need to take into account its originality, relevance, feasibility, and the resources you have at your disposal. Make sure to align your interest and expertise in the subject with your university program’s specific requirements. Always consult your academic advisor to ensure that your chosen topic not only meets the academic criteria but also provides a valuable contribution to the field. 

If you need a helping hand, feel free to check out our private coaching service here.

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Computational Finance MSc

London, Bloomsbury

The rising sophistication of the financial sector is bringing a great demand for experts with skills across mathematics, finance, statistics and computer science. The Computational Finance MSc produces talented quantitative analysts or ‘quants’ in these areas, in just one year. This fast-paced and innovative programme is taught at UCL Computer Science, a renowned centre of academic excellence, with world-class credentials in computational statistics and machine learning.

UK tuition fees (2024/25)

Overseas tuition fees (2024/25), programme starts, applications accepted.

Applications closed

Applications open

• Entry requirements

A minimum of an upper second-class UK Bachelor's degree (or an international qualification of an equivalent standard) in a relevant discipline with a strong quantitative component evidenced by good performance in mathematics and statistics examinations. Good performance is defined as scores in these subjects not falling below a UK upper second-class or international equivalent level. There is not an exhaustive list of relevant disciplines, but individuals with a background in mathematics, statistics, physics, computer science, engineering, economics, or finance are encouraged to apply.

The English language level for this programme is: Level 2

UCL Pre-Master's and Pre-sessional English courses are for international students who are aiming to study for a postgraduate degree at UCL. The courses will develop your academic English and academic skills required to succeed at postgraduate level.

Further information can be found on our English language requirements page.

This programme is suitable for international students on a Student visa – study must be full-time, face-to-face, starting September.

Equivalent qualifications

Country-specific information, including details of when UCL representatives are visiting your part of the world, can be obtained from the International Students website .

International applicants can find out the equivalent qualification for their country by selecting from the list below. Please note that the equivalency will correspond to the broad UK degree classification stated on this page (e.g. upper second-class). Where a specific overall percentage is required in the UK qualification, the international equivalency will be higher than that stated below. Please contact Graduate Admissions should you require further advice.

About this degree

As a student on the Computational Finance MSc, you will learn advanced quantitative, modelling and programming skills, which are essential for quant roles in trading, research, regulation, and risk. This applied taught postgraduate programme is distinctive in that it provides a combination of finance, mathematics, statistics, and computer science.

Computational finance is at the intersection of mathematics, statistics, computer science, finance and economics. Elevating your skills in these disciplines is essential if you want to make your mark as a quant in a large bank, hedge fund or financial regulator, or in the world of fintech and innovative start-ups.

You will learn from our world-leading experts in this field. The syllabus is constantly reviewed to stay ahead of the trends; core modules span financial engineering and numerical methods for finance, to data science and machine learning for finance.

Optional modules allow you to dive deeper into specific areas of interest, such as algorithmic trading, market microstructure, blockchain technologies, the management of markets, systemic and operational risk, numerical optimisation, and financial market modelling and analysis. You will conclude your experience with a project that brings an opportunity to work with an industry partner on a real-world problem, or to delve into a research project supervised by one of our leading academics.

This programme gives you key skills that will enable you to pursue a career as a ‘quant’, while you immerse yourself in London life and the benefits of living in a global financial centre.

Who this course is for

What this course will give you.

UCL is ranked 9th globally in the latest QS World University Rankings (2024) , giving you an exciting opportunity to study at one of the world’s best universities.

UCL Computer Science is recognised as a world leader in teaching and research. The department was ranked 1st in England and 2nd in the UK for research power in Computer Science and Informatics in the UK's most recent Research Excellence Framework (REF) You will learn from leading experts at the forefront of computer science innovation.

Additionally, the UCL Computational Finance MSc has been ranked 2nd in the UK, 6th in Europe and 24th in the world in the 2023 Risk.net Quant Finance Master’s Guide , which assesses the quality of quantitative finance programmes around the world.

The location in the global financial centre of London coupled with the Department’s extensive links with industry in the City give you ample opportunities for contact with potential future employers, as well as opportunities for practical, hands-on experience with these companies through the UCL Industry Exchange Network (IXN) .

The foundation of your career

Graduates of this programme go on to work as quantitative analysts, quantitative traders or quantitative developers, plus a range of other jobs in asset allocation, risk management, fintech, regulation, or research for a large spectrum of employers.

They work for large banks such as Credit Suisse and JP Morgan, regulators such as the Bank of England and the Financial Conduct Authority, hedge funds and fintech companies, or they stay in academia to pursue a PhD. Altogether, the options for graduates of the Computational Finance MSc are exciting and varied.

Employability

In a programme that embraces mathematics, statistics, finance and computer science in equal measure, you can expect to acquire a combination of highly sought-after skills currently needed in the financial industry. You will graduate with advanced quantitative, modelling and programming skills, which are essential for ‘quant’ roles in trading, asset allocation, risk management, fintech, regulation and research.

UCL is proud to support innovation and link our students and research directly to real-world business applications. From internships to solving complex problems with commercial partners, UCL Engineering has a collaborative, innovative spirit at its core.

As a student and later as a graduate, you will have access to a UCL Engineering careers events programme, connecting you with employers and alumni. This programme provides invaluable insight into the reality of different roles, sectors, and current application processes.

Entrepreneurial minds thrive at UCL. For example, UCL’s IDEALondon was the first innovation centre led by a university in London, and incubates companies post-seed to reach technical and business milestones. Our academic and industrial networks provide a safe and supportive environment to grow a company.

Teaching and learning

The programme’s core curriculum is typically delivered through a combination of lectures, tutorials, and lab classes, as well as directed and self-directed learning supported by teaching materials and resources, published through each module’s online virtual learning environment. Each module employs a teaching strategy that aligns with and supports its intended learning outcomes.

You will be assessed through a range of methods across the programme, which will vary depending on any optional or elective module choices. The programme’s core curriculum is typically assessed by methods including coursework, programming tests, individual and group projects, class tests, written exams, oral assessments, and a final research project/dissertation.

Contact time takes various forms, including lectures, seminars, tutorials, project supervisions, demonstrations, practical classes and workshops, visits, placements, office hours (where staff are available for consultation), email, videoconferencing, or other media, and situations where feedback on assessed work is given (one-to-one or in a group).

Each module has a credit value that indicates the total notional learning hours a learner will spend to achieve its learning outcomes. One credit is considered equal to 10 hours of notional learning, which includes all contact time, self-directed study, and assessment.

The contact time for each of your 15 credit taught modules will typically include 22-30 hours of teaching activity over the term of its delivery, with the balance then comprised of self-directed learning and working on your assessments. You will have ongoing contact with teaching staff via each module’s online discussion forum, which is typically used for discussing and clarifying concepts or assessment matters and will have the opportunity to access additional support via regular office hours with module leaders and programme directors.

Your research project/dissertation module is 60 credits and will include regular contact with your project supervisor(s), who will guide and support you throughout your project. You will dedicate most of your time on this module to carrying out research in connection with your project and writing up your final report.

The Computational Finance MSc is a one-year programme.

In term 1, you will be introduced to financial engineering and to numerical methods in finance. You will choose from optional topics which include probability theory and stochastic processes, market risk and portfolio theory, market microstructure, operational risk management for financial institutions, financial institutions and markets, and blockchain technologies.

In term 2, you will study data science and machine learning with applications in finance. You will choose from optional topics that include algorithmic trading, networks and systemic risk, applied computational finance, financial market modelling and analysis, numerical optimisation, and advanced machine learning in finance. You will also begin preparation for your final research project/dissertation.

In term 3, you will focus on any examinations that take place in the main examination period and undertake your final research project/dissertation; the project is usually done within a placement organised by UCL at a bank, hedge fund, fintech, or other financial services firm located in London, or sometimes at a regulatory authority.

Compulsory modules

Optional modules.

Please note that the list of modules given here is indicative. This information is published a long time in advance of enrolment and module content and availability are subject to change. Modules that are in use for the current academic year are linked for further information. Where no link is present, further information is not yet available.

Students undertake modules to the value of 180 credits. Upon successful completion of 180 credits, you will be awarded an MSc in Computational Finance.

Accessibility

Details of the accessibility of UCL buildings can be obtained from AccessAble accessable.co.uk . Further information can also be obtained from the UCL Student Support and Wellbeing team .

Online - Open day

Graduate Open Events: Department of Computer Science

Join us for a live online information session to hear from Computer Science staff. We will cover areas such as the general admission process, careers support, and industry links/placements. There will also be an opportunity for you to ask staff and current students any questions you may have. Two sessions will run for this event. These sessions are the same and are repeated to cater to people in different time zones.

Fees and funding

Fees for this course.

The tuition fees shown are for the year indicated above. Fees for subsequent years may increase or otherwise vary. Where the programme is offered on a flexible/modular basis, fees are charged pro-rata to the appropriate full-time Master's fee taken in an academic session. Further information on fee status, fee increases and the fee schedule can be viewed on the UCL Students website: ucl.ac.uk/students/fees .

Additional costs

All full time students are required to pay a fee deposit of £2,000 for this programme. All part-time students are required to pay a fee deposit of £1,000.

Students will require a modern computer (PC or Mac) with minimum specifications 8GB RAM and 500GB SSD storage. A computer with the stated specifications is estimated to cost £500 or greater.

For more information on additional costs for prospective students please go to our estimated cost of essential expenditure at Accommodation and living costs .

Funding your studies

For a comprehensive list of the funding opportunities available at UCL, including funding relevant to your nationality, please visit the Scholarships and Funding website .

UCL East London Scholarship

Deadline: 20 June 2024 Value: Tuition fees plus £15,700 stipend () Criteria Based on financial need Eligibility: UK

UCL Friends & Alumni Association scholarship for Machine Learning

Deadline: 3 June 2024 Value: $20,000 (1 year) Criteria Based on both academic merit and financial need Eligibility: EU, Overseas

Students are advised to apply as early as possible due to competition for places. Those applying for scholarship funding (particularly overseas applicants) should take note of application deadlines.

There is an application processing fee for this programme of £90 for online applications and £115 for paper applications. Further information can be found at Application fees .

When we assess your application we would like to learn:

• why you want to study Computational Finance at graduate level
• why you want to study Computational Finance at UCL
• what particularly attracts you to the chosen programme
• how do your academic and professional background and skills meet the demands of this challenging programme
• where would you like to go professionally with your degree

Together with essential academic requirements, the personal statement is your opportunity to illustrate whether your reasons for applying to this programme match what the programme will deliver.

Due to competition for places on this programme, no late applications will be considered. Students with visa requirements or applying for scholarships are advised to apply early.

Please note that you may submit applications for a maximum of two graduate programmes (or one application for the Law LLM) in any application cycle.

Choose your programme

Please read the Application Guidance before proceeding with your application.

Year of entry: 2024-2025

Got questions get in touch.

Computer Science

[email protected]

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Postgraduate taught

MSc Statistics and Computational Finance

Explore contemporary statistical methodology and related data analysis

Year of entry: 2024 (September)

1 year full-time

Department of Mathematics

September 2024 ( semester dates )

Apply for this course

Join us online or in person to find out more about postgraduate study at York.

Train to work as a professional statistician and gain skills and experience working at the interface between statistics and finance. 

Our course emphasises data analysis and will provide you with contemporary statistical ideas and methodologies that are attractive to prospective employers. The skills you gain are useful for a wide range of financial data analysis and in a range of other sectors where data analysis is required, for example sociology, health science, medical science or biology. This experience is also an ideal foundation for further academic study; many of our students choose to progress to PhD.

Our Statistics and Probability Group has a thriving research culture. Our group works with mainstream statistics to develop new methodology and apply it to real-world problems. The team produces world-class research, publishing in top journals. Our graduate students have full access to this expertise, as well as being exposed to forefront research carried out across the globe through our regular seminars and working groups.

Learn more about the study of finance at York.

Guest stars

Hear from leading mathematicians from around the world at our regular seminars and events

Research excellence

A UK top 20 research department according to the Times Higher Education’s ranking of the latest REF results (2021).

Course content

This course will equip you with the necessary skills to:

• translate problems from the workplace into contemporary statistical ideas and methodologies
• solve problems using your advanced knowledge in statistical modelling and computational finance
• interpret and communicate your results.

The core modules we offer will give you a strong grounding in statistical data analysis and modelling techniques, as well as much-needed computational skills required by finance-based employers, such as Python and R languages. Additionally, you'll choose an option module and dissertation topic that are aligned with your own interests, resulting in a flexible programme tailored to meet your needs.

Dissertation

Core modules.

• Generalised Linear Models
• Statistical Pattern Recognition
• Statistics for Finance and Insurance
• Multivariate Data Analysis
• Computational Finance with Python

Option modules

You will also study one option module:

• Decision Theory and Bayesian Statistics
• Mathematical Methods of Finance
• Mathematical Finance in Discrete Time

Our modules may change to reflect the latest academic thinking and expertise of our staff.

• Statistics and Computational Finance Dissertation

Your Masters will culminate in a dissertation  on a selected topic in Statistics or Computational Finance.

It will be a piece of independent work that you complete over the summer. You will have guidance from a project supervisor with weekly supervision, scheduled at a time to suit you.

Our recent students' dissertations have investigated topics including:

• Feature Selection in Trading Behaviour in Financial Markets - A Big Data Analysis
• Modelling the UK and USA GDP Data: Estimation and Prediction
• The day of the week effect in different countries' stock markets

The York approach

Every course at York is built on a distinctive set of learning outcomes. These will give you a clear understanding of what you will be able to accomplish at the end of the course and help you explain what you can offer employers. Our academics identify the knowledge, skills, and experiences you'll need upon graduation and then design the course to get you there.

Students who complete this course will be able to:

• Use, with a high level of confidence and sophistication, the appropriate modern statistical (incl. probabilistic) methodology and associated tools that underpin a wide range of applied problems, particularly in finance, big data analysis but also more generally in science and industry.
• Recognise and critically evaluate different statistical (incl. probabilistic) methods in order to find a suitable strategy for solving an unfamiliar problem open to investigation.
• Use logical reasoning as a basis for the critical analysis of ideas or statements which have a statistical and financial context, and develop independently their own ideas using well-founded reasoning.
• Independently conduct a piece of applied research in a relevant specialised area, for example take into account recent statistical methodology, apply it and interpret conclusions on real data sources.
• Communicate advanced statistical and mathematical analyses and associated conclusions clearly, in writing or in a presentation, at a level appropriate for the intended audience.
• Create mathematical documents, presentations and computer programmes by accurately and efficiently using a range of digital technologies and programming tools.

Fees and funding

Annual tuition fees for 2024/25.

Students on a Student Visa are not currently permitted to study part-time at York.

Fees information

UK (home) or international fees?  The level of fee that you will be asked to pay depends on whether you're classed as a UK (home) or international student.  Check your fee status .

Find out more information about tuition fees and how to pay them.

• Postgraduate taught fees and expenses

Funding information

Discover your funding options to help with tuition fees and living costs.

We'll confirm more funding opportunities for students joining us in 2024/25 throughout the year.

If you've successfully completed an undergraduate degree at York you could be eligible for a  10% Masters fee discount .

Funding opportunities

• UK government Masters loans
• Funding for UK students
• Funding for international students

Living costs

You can use our  living costs guide  to help plan your budget. It covers additional costs that are not included in your tuition fee such as expenses for accommodation and study materials.

Teaching and assessment

You’ll work with world‐leading academics who’ll challenge you to think independently and excel in all that you do. Our approach to teaching will provide you with the knowledge, opportunities, and support you need to grow and succeed in a global workplace.

Teaching format

Our teaching is informed by the latest research, meaning you can focus on the latest ideas and models.

We use a wide range of teaching methods to suit different learning styles including:

• Problem classes

For some modules you may also attend practical classes, computer laboratories or workshops.

Lectures are used to describe new concepts you will have to learn and problems classes put them into practice. Seminars are small, interactive sessions which allow us to focus on your individual needs. You'll be able to use our Virtual Learning Environment to supplement lectures and seminars. This includes access to our short videos designed to reinforce your knowledge on certain topics, and are accompanied by a set of dedicated study notes.

While you're working on your project and your dissertation you'll have regular meetings with your academic supervisor who will offer advice and support. We will give you a supervisor with specialist knowledge of the area you're investigating.

Teaching location

You will be based in the  Department of Mathematics  in James College on Campus West. Most of your small group teaching will take place in the Department's dedicated MSc seminar room (the Dusa McDuff room), with larger classes taking place close by in James College, Derwent College and elsewhere on Campus West.

About our campus

Our beautiful green campus offers a student-friendly setting in which to live and study, within easy reach of the action in the city centre. It's  easy to get around campus  - everything is within walking or pedalling distance, or you can always use the fast and frequent bus service.

Assessment and feedback

All taught modules are assessed by a combination of closed book written exams, coursework, projects and presentations.

The closed book written exam assesses your subject-specific knowledge through both theoretical and practical questions and open-ended problems.

The coursework and projects often require the use of software, giving you an opportunity to develop your technical skills. They will test your subject knowledge and analytical, theoretical skills as well as the practical aspects of application, implementation and interpretation.

Developing and delivering digital presentations will enhance your communication skills for a range of audiences, from the general public to subject experts.

The independent study module relies on your own research, so you'll continue to develop your critical reasoning and digital literacy skills, including programming. As this module is assessed with a dissertation, your training is rounded off by consistently working on your written communication skills.

Careers and skills

The big data analysis skills you develop on this course provide attractive employment opportunities in a growing number of industries where such skills are in high demand. The course is also a good foundation for continuing your studies at PhD level.

Career opportunities

• Quantitative analyst
• Account manager for a bank
• Trainee chartered accountant
• Management associate
• Software developer

Transferable skills

• Confidence with high-level financial statistical analysis
• Logical thinking
• Analysis of problems
• Problem-solving
• Flexible thinking, the ability to learn and apply complex ideas quickly and precisely
• Digital literacy
• Time management
• Communication skills
• Research skills

Our computational skills are transferable and employers recognise the value of the programming knowledge you will have developed to a high level during this course.

Entry requirements

English language.

If English isn't your first language you may need to provide evidence of your English language ability. We accept the following qualifications:

For more information see our postgraduate English language requirements .

If you haven't met our English language requirements

You may be eligible for one of our pre-sessional English language courses . These courses will provide you with the level of English needed to meet the conditions of your offer.

The length of course you need to take depends on your current English language test scores and how much you need to improve to reach our English language requirements.

After you've accepted your offer to study at York, we'll confirm which pre-sessional course you should apply to via You@York .

You can apply and send all your documentation online. You don’t need to complete your application all at once: you can start it, save it and finish it later.

• How to apply

Applications open 25 September 2023.

Get in touch if you have any questions

• Dr Yue Zhao
• +44 (0)1904 32 3669

• Dr Degui Li
• [email protected]
• +44 (0)1904 32 4160

Related courses

• MSc Mathematical Finance
• MSc Mathematical Finance by online distance learning
• MSc Financial Engineering
• MSc Mathematical Sciences

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Find out more about York. Chat to staff and students and take the tour, on campus or online.

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This collection of MIT Theses in DSpace contains selected theses and dissertations from all MIT departments. Please note that this is NOT a complete collection of MIT theses. To search all MIT theses, use MIT Libraries' catalog .

MIT's DSpace contains more than 58,000 theses completed at MIT dating as far back as the mid 1800's. Theses in this collection have been scanned by the MIT Libraries or submitted in electronic format by thesis authors. Since 2004 all new Masters and Ph.D. theses are scanned and added to this collection after degrees are awarded.

MIT Theses are openly available to all readers. Please share how this access affects or benefits you. Your story matters.

If you have questions about MIT theses in DSpace, [email protected] . See also Access & Availability Questions or About MIT Theses in DSpace .

If you are a recent MIT graduate, your thesis will be added to DSpace within 3-6 months after your graduation date. Please email [email protected] with any questions.

Permissions

MIT Theses may be protected by copyright. Please refer to the MIT Libraries Permissions Policy for permission information. Note that the copyright holder for most MIT theses is identified on the title page of the thesis.

Theses by Department

• Comparative Media Studies
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Collections in this community

Doctoral theses, graduate theses, undergraduate theses, recent submissions.

Designing Macromolecules using Machine Learning and Simulations 

Methods to program and to probe RNA tertiary structure with nucleic acid origami 

Effects of Crystalline Anisotropy on Solid-state Dewetting 

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MSc Theses on Machine Learning and Computational Finance

• Thread starter Daniel Duffy
• Start date 10/7/19

Daniel Duffy

C++ author, trainer.

• The Heston model (analytical solution and Yanenko Splitting and Alternating Explicit (ADE) methods).
• Using (Gaussian) Radial Basis Functions instead of traditional Backpropagation to compute neural network weights.
• A mathematical, numerical and computational analysis of the Continuous Sensitivity Equation (CSE) method.
• Parallel software design for ML/PDE applications.

Blogs :: Datasim

Those are really well-done theses, especially considering they are just three-month master theses. I saw lots of carefully designed analysis. Those students would benefit a lot no matter they decide to work in industry or continue to pursue Ph.D. I wish I could have done such a master thesis when I started. It's so interesting to see a comparison btw neural networks and classic numerical approach for option pricing. My personal take-on ML is they will only produce acceptable results when the data is super large and approximation has almost no effect on the results. So IMHO the back and bone for applied math is still classic numerical methods and statistics.  

This is a really good start to investigate AI in finance. Look forward to seeing more results from you and your team. The thesis was written in a very clear and coherent way, I believe both professionals and students can benefit from reading it. btw: Can you indicate what the discussion on the "UAT" is? what is "UAT" btw: I'm also very uncomfortable about the "flaky" math behind AI. I'm reading Strang's book "learning from data" to see how he addresses it.  

UAT is the magic wand of AI. https://pdfs.semanticscholar.org/05ce/b32839c26c8d2cb38d5529cf7720a68c3fab.pdf It is based around measure theory which is not precise enough, mathematically (in the function approximation sense). In practice it seems to work until you use gradient descent.  

Graduation day! Congratulations, Dalvir Singh Mandara https://www.linkedin.com/feed/update/urn:li:activity:6610872313523113984/  

Blogs - MSc Theses on Machine Learning and Computational Finance 2020 :: Datasim

Hilbert Space Kernel Methods for Machine Learning: Background and Foundations - Splash

ODE Gradient Systems for Optimisation in Finance and Machine Learning, new Perspectives​

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• Mathematical and Computational Finance @ Oxford
• Study with us

DPhil (PhD) studies in Mathematical Finance @ Oxford

The Mathematical and Computational Finance Group (MCFG) at Oxford is one of the largest and most dynamic research environments in mathematical finance in the world.

We combine core mathematical expertise with interdisciplinary approach. We foster lively interactions between researchers coming from different backgrounds and a truly impressive seminar programme, all this within one of the world's top universities, singular through its tradition and unique environment.

If you are passionate about mathematics and research and want to pursue a DPhil in Financial Mathematics, Oxford simply offers one of the best and most exciting places to do it!

 Research Topic and Supervisor Allocation

We welcome students with their own particular ideas of research topic as well as students with a broad interest in the field of Mathematical Finance. You have an opportunity to tell us about your research passions, and indicate potential supervisors, in your application form. This will be followed up during the interview.

In light of this, if you are offered a place, an appropriate supervisor will be proposed prior to your arrival in Oxford. However, there can be some flexibility over this once you arrive.  Keeping with the Oxford tradition, we offer our students independence and respect as early researchers, and always aim to match students with the most appropriate supervisors.

Outstanding students with a strong background in analysis, probability and data science are welcome to apply for our DPhil program. Each year we receive a large number of excellent applications. The selection process is extremely competitive and we can only admit a handful of candidates each year.

In order to apply for DPhil studies in Mathematical & Computational Finance, please indicate your interest in Mathematical and Computational Finance on your application form. Selected applicants will be invited for an interview -- either in person or by video call.

For general information on DPhil please consult our  Doctor of Philosophy (DPhil) admissions pages .

For the CDT Mathematics of Random Systems please consult our  the CDT website .

Or please contact  @email .

Funding for DPhil students is available from a variety of sources. Please note that some funding opportunities have deadlines: it is advised to apply before the deadline in order to maximise your chances of receiving funding.

Funding is also available through the  Centre for Doctoral Training in Mathematics of Random Systems . To apply for this program please How to Apply .

Email:  @email Phone:  +44 (0)1865 615234

Join us on  LinkedIn  or  sign up to our newsletter

DPhil Graduates

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Dissertations / Theses on the topic 'Computational economics'

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Pugh, David. "Essays in computational economics." Thesis, University of Edinburgh, 2014. http://hdl.handle.net/1842/9882.

Jelonek, Piotr Zbigniew. "Essays on computational economics." Thesis, University of Leicester, 2014. http://hdl.handle.net/2381/28644.

Grinis, Inna. "Essays in applied computational economics." Thesis, London School of Economics and Political Science (University of London), 2017. http://etheses.lse.ac.uk/3580/.

Balikcioglu, Metin. "Essays on Environmental and Computational Economics." NCSU, 2008. http://www.lib.ncsu.edu/theses/available/etd-12032008-210449/.

Schuster, Stephan. "Applications in agent-based computational economics." Thesis, University of Surrey, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.556466.

Chong, Shi Kai. "A computational approach to urban economics." Thesis, Massachusetts Institute of Technology, 2018. https://hdl.handle.net/1721.1/122318.

Hull, Isaiah. "Essays in Computational Macroeconomics and Finance." Thesis, Boston College, 2013. http://hdl.handle.net/2345/bc-ir:104376.

Wong, Yiu Kwong. "Application of computational models and qualitative reasoning to economics." Thesis, Heriot-Watt University, 1996. http://hdl.handle.net/10399/688.

Lupi, Paolo. "The evolution of collusion : three essays in computational economics." Thesis, University of York, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.341598.

Gao, Lili. "Applications of MachLearning and Computational Linguistics in Financial Economics." Research Showcase @ CMU, 2016. http://repository.cmu.edu/dissertations/815.

Lee, Myong-hwal. "Computational analysis of optimal macroeconomic policy design /." Digital version accessible at:, 1998. http://wwwlib.umi.com/cr/utexas/main.

Ellison, Sara Fisher. "A nonparametric residual-based specification test : asymptotic, finite-sample, and computational properties." Thesis, Massachusetts Institute of Technology, 1993. http://hdl.handle.net/1721.1/12697.

Lodhi, Aemen Hassaan. "The economics of internet peering interconnections." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/53092.

Henkel, Marco. "Darstellung der Agent-based Computational Economics und Anwendung auf ausgewählte Märkte." Münster Verl.-Haus Monsenstein und Vannerdat, 2009. http://d-nb.info/1001177894/04.

Wu, Di. "Three Essays on the Credit Card Debt Puzzle, Income Falsification, and Numerical Approximation." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1563316071624495.

Okasha, Ahmed E. "Agent-based computational economics : studying the effect of different levels of rationality on macro-activities for economic systems." Thesis, University of Kent, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.529398.

Latsch, Wolfram Wilhelm. "Beyond complexity and evolution : on the limits of computability in economics." Thesis, University of Oxford, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.325103.

Schaff, Frederik [Verfasser]. "Pure agent-based computational economics of time, knowledge and procedural rationality with an application to environmental economics / Frederik Schaff." Hagen : Fernuniversität Hagen, 2016. http://d-nb.info/1114292087/34.

Krause, Thilo. "Evaluating congestion management schemes in liberalized electricity markets applying agent-based computational economics /." Zürich : ETH, 2007. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=16928&part=abstracts.

Teglio, Andrea. "From agent-based models to artificial economies." Doctoral thesis, Universitat Jaume I, 2011. http://hdl.handle.net/10803/83303.

Dennig, Francis. "On the welfare economics of climate change." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:aefca5e4-147e-428b-b7a1-176b7daa0f85.

Cui, Zhuoya. "Understanding social function in psychiatric illnesses through computational modeling and multiplayer games." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/103528.

Yan, Chang. "A computational game-theoretic study of reputation." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:e6acb250-efb8-410b-86dd-9e3e85b427b6.

Jessup, Ryan K. "Neural correlates of the behavioral differences between descriptive and experiential choice an examination combining computational modeling with fMRI /." [Bloomington, Ind.] : Indiana University, 2008. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3337258.

Boto, Joaquim Paulo da Silva. "Desenvolvimento de Modelos Baseados em Agentes: Plataforma Aplicacional." Master's thesis, Instituto Superior de Economia e Gestão, 2010. http://hdl.handle.net/10400.5/2962.

Strid, Ingvar. "Computational methods for Bayesian inference in macroeconomic models." Doctoral thesis, Handelshögskolan i Stockholm, Ekonomisk Statistik (ES), 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:hhs:diva-1118.

Kiose, Daniil. "The ACEWEM computational laboratory : an integrated agent-based and statistical modelling framework for experimental designs of repeated power auctions." Thesis, London Metropolitan University, 2015. http://repository.londonmet.ac.uk/1257/.

Joseph, Joshua Allen Jr. "Computational Tools for Improved Analysis and Assessment of Groundwater Remediation Sites." Diss., Virginia Tech, 2008. http://hdl.handle.net/10919/28458.

Faleiro, Jorge. "Supporting large scale collaboration and crowd-based investigation in economics : a computational representation for description and simulation of financial models." Thesis, University of Essex, 2018. http://repository.essex.ac.uk/21782/.

Kraft, Dennis [Verfasser], Susanne [Akademischer Betreuer] Albers, Martin [Gutachter] Bichler, and Susanne [Gutachter] Albers. "Incentive Design for Present-Biased Agents : A Computational Problem in Behavioral Economics / Dennis Kraft ; Gutachter: Martin Bichler, Susanne Albers ; Betreuer: Susanne Albers." München : Universitätsbibliothek der TU München, 2018. http://d-nb.info/1173898999/34.

Kraft, Dennis Verfasser], Susanne [Akademischer Betreuer] [Albers, Martin [Gutachter] Bichler, and Susanne [Gutachter] Albers. "Incentive Design for Present-Biased Agents : A Computational Problem in Behavioral Economics / Dennis Kraft ; Gutachter: Martin Bichler, Susanne Albers ; Betreuer: Susanne Albers." München : Universitätsbibliothek der TU München, 2018. http://nbn-resolving.de/urn:nbn:de:bvb:91-diss-20181130-1445718-1-8.

MANCA, MAURIZIO. "Consumption and saving specification: a new perspective." Doctoral thesis, Università Politecnica delle Marche, 2014. http://hdl.handle.net/11566/242765.

Bertolai, Jefferson Donizeti Pereira. "Dinâmica monetária eficiente sob encontros aleatórios: uma classe de métodos numéricos que exploram concavidade." reponame:Repositório Institucional do FGV, 2009. http://hdl.handle.net/10438/4277.

Gräbner, Claudius [Verfasser], Wolfram [Akademischer Betreuer] [Gutachter] Elsner, and Christian [Gutachter] Cordes. "A systemic framework for the computational analysis of complex economies: An evolutionary-institutional perspective on the ontology, epistemology, and methodology of complexity economics / Claudius Gräbner. Betreuer: Wolfram Elsner. Gutachter: Wolfram Elsner ; Christian Cordes." Bremen : Staats- und Universitätsbibliothek Bremen, 2016. http://d-nb.info/1102308889/34.

Gräbner-Radkowitsch, Claudius [Verfasser], Wolfram [Akademischer Betreuer] [Gutachter] Elsner, and Christian [Gutachter] Cordes. "A systemic framework for the computational analysis of complex economies: An evolutionary-institutional perspective on the ontology, epistemology, and methodology of complexity economics / Claudius Gräbner. Betreuer: Wolfram Elsner. Gutachter: Wolfram Elsner ; Christian Cordes." Bremen : Staats- und Universitätsbibliothek Bremen, 2016. http://nbn-resolving.de/urn:nbn:de:gbv:46-00105216-14.

Thompson, David R. M. "The positronic economist : a computational system for analyzing economic mechanisms." Thesis, University of British Columbia, 2015. http://hdl.handle.net/2429/52868.

Teixeira, Henrique Oliveira. "Bank networks and firm credit: an agent based model approach." reponame:Repositório Institucional do FGV, 2016. http://hdl.handle.net/10438/15973.

Sartzetaki, Maria. "Computational modeling for evaluating the economic impact of airports on regional economies." Thesis, Cranfield University, 2011. http://dspace.lib.cranfield.ac.uk/handle/1826/7219.

Pham, Tien Duc, and n/a. "A new approach to regional modelling: an Integrated Regional Equation System (IRES)." Griffith University. School of International Business and Asian Studies, 2004. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20041022.083520.

Pham, Tien Duc. "A new approach to regional modelling: an Integrated Regional Equation System (IRES)." Thesis, Griffith University, 2004. http://hdl.handle.net/10072/366367.

Angus, Simon Douglas Economics Australian School of Business UNSW. "Economic networks: communication, cooperation & complexity." Awarded by:University of New South Wales. Economics, 2007. http://handle.unsw.edu.au/1959.4/27005.

Björnfot, Fredrik. "GDP Growth Rate Nowcasting and Forecasting." Thesis, Umeå universitet, Institutionen för fysik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-132951.

Metzig, Cornelia. "A Model for a complex economic system." Thesis, Grenoble, 2013. http://www.theses.fr/2013GRENS038/document.

Davy, Simon Mark. "Decentralised economic resource allocation for computational grids." Thesis, University of Leeds, 2008. http://etheses.whiterose.ac.uk/1369/.

Bogan, Nathaniel Rockwood. "Economic allocation of computation time with computation markets." Thesis, Massachusetts Institute of Technology, 1994. http://hdl.handle.net/1721.1/32603.

Merz, Laura. "AUTOMATION-INDUCED RESHORING: An Agent-based Model of the German Manufacturing Industry." Thesis, Uppsala universitet, Institutionen för geovetenskaper, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-394212.

Mignot, Sylvain. "Négocier ou enchérir, l’influence des mécanismes de vente : le cas du marché aux poissons de Boulogne-sur-Mer." Thesis, Paris 2, 2012. http://www.theses.fr/2012PA020101/document.

Ngaruye, Innocent. "Contributions to Small Area Estimation : Using Random Effects Growth Curve Model." Doctoral thesis, Linköpings universitet, Matematisk statistik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-137206.

Schriner, Andrew W. "#Crowdwork4dev:Engineering Increases in Crowd Labor Demand to Increase the Effectiveness of Crowd Work as a Poverty-Reduction Tool." University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1445341861.

Tran, Hieu. "Fragilité financière par l'analyse des réseaux et l'approche comportementale." Thesis, Bordeaux, 2018. http://www.theses.fr/2018BORD0445/document.

Stockholm Business School

Doctoral programme in finance.

Are you curious about how financial markets operate, how corporations make financial decisions, or how to manage risks in a portfolio? Are you interested in how financial markets can be made sustainable, or how societies can promote financial stability? Then you should consider the PhD in Finance.

What we offer:

• a world-class course program;
• a fully funded doctoral studentship with a competitive salary;
• a brand new campus close to the city centre as well as nature and recreation.

As a doctoral student you will undergo rigorous training in theory, method and analysis – through individual supervision, coursework, seminars and workshops. No doubt, you will also benefit from being part of an academic community where scholarly dialogue continues outside of the seminar room. Doctoral students are expected to complete their PhD in four years, with possible extension up to five years.

Research environment and research topic

SBS Finance is a highly international group of about twenty faculty members, post-docs, PhD students, and teaching assistants. The group hosts a weekly seminar series, where external as well as internal speakers present their academic research. There are also ad hoc academic events in the form of workshops and conferences.

Research topic

Financial economists study how scarce resources are allocated over time. Financial decisions differ from other allocation decisions in that their costs and revenues are spread over time and not known in advance. To implement their decisions, individuals, companies or other agents use the financial system. The system includes markets for stocks, bonds and other financial instruments, as well as financial intermediaries (such as banks and insurance companies), financial service companies (such as financial advisers) and the bodies that oversee and regulate the financial markets and institutions. The programme is open for thesis proposals on any topic related to Finance.

Current research that our faculty members work on are presented under Research projects in Finance.

Students interested in monetary policy and financial stability can choose to do a specialization in "Money and Finance". The specialization is provided by the Centre for Monetary Policy and Financial Stability (CeMoF), which is a joint venture between the Department of Economics, the Institute for International Economic Studies, and Stockholm Business School.

Read more about Centre for Monetary Policy and Financial Stability (CeMoF)

Programme structure, coursework and thesis

The doctoral programme in Finance comprises 240 higher education credits, or four years of full-time study. This consists of courses worth 105 credits and a dissertation worth 135 credits. The two parts can be followed simultaneously, but most of the course work is typically done during the first half of the study period. Many doctoral students also choose to teach during their studies, thereby extending their funding to up to five years.

The coursework includes mandatory courses in quantitative methods (15 credits) and finance (30 credits), as well as elective courses in fields closely related to finance (60 credits).

As a doctoral student in finance you can follow the course program offered by Stockholm Doctoral Program in Economics, Econometrics, and Finance (SDPE), which is a collaboration between Stockholm university and Stockholm School of Economics. Prior to the first semester, you are encouraged to participate in a Computational Bootcamp.

Read more about the course program offered by Stockholm Doctoral Program in Economics, Econometrics, and Finance (SDPE)

Read more about the Computational Bootcamp

For the electives, doctoral students in finance often take courses offered at the Swedish House of Finance and at partner universities in the Nordic Finance Network. Students choosing the "Money and Finance" specialization are required to take a course in Monetary Economics at Stockholm university.

Read more about courses offered at the Swedish House of Finance

Read more about courses at partner universities in the Nordic Finance Network

Read more about the course in Monetary Economics

The thesis is developed under the guidance of the supervisors. As a doctoral student at SBS you are assigned one or more supervisors from day one. As you progress with the thesis work you also get feedback at three “milestone seminars”: the thesis proposal seminar, the midway seminar, and the final script seminar. In addition, you are encouraged to present your work at conferences in Sweden and abroad. You are allocated a research budget to cover conference expenses.

At the end of the program, the thesis is assessed at the public dissertation defence. Doctoral theses in finance typically consist of three or more academic articles, of which at least one is solo-authored.

Employment / funding

When you are admitted to the PhD program, you typically also become employed by Stockholm university. The position takes the form of a temporary employment for a maximum of four years of full-time study, conditional on that the studies proceed according to plan. There is no tuition fee and you are paid a monthly salary . The entry level salary currently amounts to SEK 29 700 per month before taxes, increasing up to SEK 34 000 during the course of the program. The program requires you to be based in Sweden, with the main workplace being Stockholm Business School.

Each doctoral student is offered a research budget to cover expenses for courses, conferences, fieldwork, databases, and software. All employees at Stockholm university are also entitled to an annual sum of SEK 3 000 to cover expenses related to health and fitness activities.

Department duty

Many students also choose to teach during their PhD studies, but this is optional. You may take on departmental duty of up to 20% of full time each year, thereby extending your funding for the doctoral programme up to five years. In addition to teaching, the departmental duty may include research assistance and administrative tasks, such as assisting in the organization of academic events.

Application and admission

Eligibility.

Candidates for the doctoral programme in Finance at SBS must fulfil at least one of the following criteria:

• Completed an advanced level degree (master’s degree).
• Completed courses equivalent to at least 240 credits, of which at least 60 credits must be at master’s level.
• Acquired equivalent knowledge in another way, in Sweden or elsewhere.

In addition, the applicant must have successfully completed at least 90 credits in Finance, or equivalent, including a thesis comprising at least 15 credits. Admission to PhD positions is limited and competition for positions is usually tough.

Application

The general period of opening for PhD positions is in the month of January.

Apply for PhD position here, closing date Feb 1st 2024.

Selection criteria

Selection of applicants is made with respect to their ability to benefit from studies at doctoral level. This is assessed on basis of:

• performance in previous higher education studies
• performance in independent written reports and theses
• performance in standardized general knowledge examinations
• letters of recommendation

Short-listed candidates are called to an interview.

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Last updated: February 2, 2024

Source: SBS

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Last updated: May 3, 2024

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Topics for master thesis

The Faculty at the Department of Finance can supervise in all the fields of Finance. Here is a list of potential topics and supervisors. Please contact us if you would like to discuss topics for your master thesis.

FINANCIAL MARKETS

Corporate finance, specific topics.

Master topics - Finans|Bergen

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March 19, 2024

Congratulations Dr. Warner!

On March 12, 2024, Elisa Warner defended her dissertation titled "Advancing Clinical Outcome Prediction through Innovative Multimodal and Domain-Generalized AI that Accommodates Limited Data." Her mentor was Arvind Rao, PhD, an associate professor in DCMB and in Radiation Oncology.

Warner's main research topic is about multimodal and multidomain AI for clinical decision support under the constraints of limited data. Clinical decision support systems are computer-based systems developed with the goal of assisting health care providers in arduous clinical tasks or improving decision-making. In routine clinical care, medical practices tend to be dynamic and must account for diversity of data. In this dissertation, Warner focused on developing innovative multimodal and multidomain AI models for clinical decision support, with an emphasis on applications with limited data availability. Her research addressed questions regarding constructing machine-learning-based models that mimic real-world mental models and bridge domain gaps in cases of limited data. She presented three case studies as examples of informed models that accommodate diverse data types, as real-world clinical practice is intrinsically multimodal and multidomain. The intent is that these models provide inspiration for additional models outside of the provided use cases and assert that methodologies can be combined and adapted as needed.

Brief Q&A with Warner:

What does particularly interest you about your research? One thing that really interests me about this work is that in real-world clinical decisions, we understand that health care professionals take in a diverse range of data. However, many models don't account for this diversity and instead rely on single modalities or domains of data such as an image from a single source to arrive at a classification. We also understand that there are limitations to obtaining data, where for example only certain patients can qualify for specialized imaging or lab tests. This means that models developed for real-world clinical settings need to take these limitations into account.

How did you come up with this topic? I joined DCMB with a real interest in translational medicine and clinical decision support models because I believe in the potential of what automated decision support systems can do to improve patient health when done right. Given the projects and data presented to me, I then further tweaked the thesis question to involve workarounds for limited data.

What drew you to U-M computational medicine and bioinformatics in the first place? Of all the bioinformatics programs that I explored during the application process, I found U-M DCMB to be the most developed and diverse. Unlike many bioinformatics programs, DCMB had a solid roster of professors working on clinical decision support and digital medicine, which was a huge factor for my decision. I also find the city of Ann Arbor to be a perfect place for college students to thrive.

What was your most exciting moment during your PhD training? I absolutely loved my conference travels and I was so lucky that Dr. Rao and Dr. Cevidanes were willing to fund that. Visiting Singapore was like meeting a long lost love -- I just fell in love with the city. I was so fortunate to have visited Scotland and Australia on conference travel as well.

What are your career plans and how did your training prepare you for these? I am moving to Austin to pursue a career as a Staff ML Scientist at Visa Inc. Although I'm working on fraud prevention, the challenges with the data are the same kind of challenges I faced here and the targets are similar to the targets I had in my dissertation. The PhD program helped me understand all the diverse problems I can meet working with imperfect data and how to troubleshoot them.

What advice do you have for upcoming students? I believe that every student who has been accepted to the program has what it takes to succeed. I always tell younger students that the only difference between a person who graduates and a person who quits the program is that the person who graduated didn't quit. Everybody here is smart enough.

What do you like to do outside the lab? The things I like the most are making my mom happy because I love her more than anything and spending time with other family and friends. True to my Filipino roots, I also love singing and I use my karaoke machine every day (to my neighbors' dismay). I love going outside and traveling to learn about different kinds of people and cultures. I like going to museums and classical music concerts or musicals. Lastly, I've most recently gotten into crochet and knitting and I'm looking forward to finishing my Gryffindor lion Wooble.

Associate Professor

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