Finance, Banking and Investment
When finance changes, so do we
Finance undergraduate degrees at Bayes
Our flexible cluster of Finance degrees provide students with the knowledge and skills required for a successful career in the increasingly competitive world of global finance and banking.
Students can switch between the three Finance degrees after the first 18 months of the course and the average salary of our Finance graduates, 15 months after graduating, is £39,318 (Graduate Outcomes 2017/18).
BSc in Banking and International Finance
This degree provides a broad overview of the modern banking industry, with an emphasis on central banking, managerial finance, commercial and retail banking.
BSc in Finance
This degree provides a broad overview of the world of finance, with an emphasis on corporate finance and mergers and acquisitions issues.
BSc in Investment and Financial Risk Management
This degree introduces you to different aspects of the financial world, with an emphasis on risk, portfolio and asset management.
Sign up for an online chat to find out more about Finance at Bayes
Finance is not a static subject. Similar to other disciplines, theories evolve and new shocks to the global economic order bring new findings and moments of reflection.
The 1973 OPEC oil crisis and the 2008 global financial crisis are both pivotal moments for all finance academics and professionals, each event requiring a deep level of scholarship to fully understand today’s global economy.
New developments in areas such as artificial intelligence, digital currencies and the Covid-19 pandemic have reshaped the future of finance. When finance changes, so do we.
The Bayes Finance Faculty is one of the largest and most widely recognised in the world, ranked 10th in Europe for Finance*, and our academics continue to stay ahead of curve, researching the topics vital to the success and security of banks, financial institutions and policy makers across the world.
These are some of the areas in which the Faculty of Finance is leading the global conversation on the most topical areas of today:
Artificial Intelligence and Trading
For decades, financial traders have tried to outperform the market through varying strategies, however machine learning (a subfield of artificial intelligence) is revolutionising the nature of trading.
Recent years have witnessed an unprecedented availability of information on financial, economic, and social-related phenomena.
Researchers, practitioners, and policymakers nowadays have access to huge datasets (the so-called “Big Data”) on people, companies and institutions, web and mobile devices, satellites, etc., at increasing speed and detail.
Machine learning is a relatively new approach to data analytics, which places itself in the intersection between statistics, computer science, and artificial intelligence. Its primary objective is that of turning information into knowledge and value by “letting the data speak”.
To this purpose, machine learning limits prior assumptions on data structure, and relies on a model-free philosophy supporting algorithm development, computational procedures, and graphical inspection more than tight assumptions, algebraic development, and analytical solutions.
Computationally unfeasible a few years ago, machine learning is a product of the computer’s era, of today machines’ computing power and ability to learn, of hardware development, and continuous software upgrading.
This approach is in line with Bayesian statistics in that it allows for a constant update of the models when new information is received from the latest available data.
Researchers at Bayes have been tracking the evolution of algorithmic trading (i.e. using computer codes and software to trade, rather than manually scanning the markets) and have provided vital consultancy to investment firms, helping them better understand how to optimize their portfolio strategies and improve their market forecasts with powerful machine learning tools.
Brexit and Financial Passporting Rights
The UK withdrawal from the European Union in 2020, coupled with more stringent regulation at the EU level, constituted substantial challenges for the operation of multinational banking groups in the Europe, Middle East and Africa (EMEA) region.
While the UK was in the EU, most large international financial intermediaries (i.e. banks, investment funds, insurers, stock exchanges) had set up headquarters and operations in London, both to service UK customers and access the UK market infrastructure, but also to obtain access to the EU markets and customers.
However, following the UK’s decision to leave the EU in 2016, new regulatory provisions at the EU level required them to establish a legal entity within the EU.
These regulations required that non-EU globally systemically important banks (G-SIBs), which have two or more subsidiaries in the EU, must establish an Intermediate Parent Undertaking in the EU (EU IPU) – a most complex undertaking, particularly given the uncertainties of what the future relationship between the UK and the EU were to look like at the time.
Researchers at Bayes Business School have been crucial in assisting non-EU G-SIBs in how to establish IPUs.
Following extensive surveying of finance industry and academic experts, the Centre for Banking Research has provided indispensable consultancy to multinational banking businesses, helping them understand the impact of political risk and map out their potential strategic responses to Brexit and other future political uncertainties.
Covid-19 and the Shadow Economy
The COVID-19 pandemic has reshaped the demand for goods and services worldwide. The combination of a public health emergency, economic distress, and misinformation-driven panic have pushed customers and vendors towards the ‘shadow economy’.
The shadow economy encompasses not only illegal activities, e.g. sales of illicit drugs or stolen goods, but also unreported income from the production of legal goods and services, i.e. tax evasion.
A major element of the global shadow economy are dark web marketplaces (DWMs) -commercial websites accessible via free software, which have for years been used to facilitate illegal transactions.
During the Covid-19 pandemic, DWMs have gained significant popularity, driven by huge restrictions to physical access to illegal goods and services, thus much of this illicit trading being facilitated online.
Researchers at Bayes have investigated the spread of illicit trading via DWMs after receiving funding from one of the UK’s largest funders.
The research revealed the proliferation of Personal Protective Equipment (PPE), medicines (e.g. hydroxychloroquine) and stolen medical records that were traded illegally during the pandemic – 2020 has been a record year for DWMs revenue.
Their results have helped regulators and policy makers monitor the increase in scams and frauds for potentially life-threatening Covid-related products and better protect the public.
Cryptocurrencies and Transparency
Cryptocurrencies are digital assets designed to facilitate exchanges of good and services, which due to being encrypted into code through cryptography (the process of storing and transmitting data in an unintelligible form so unintended recipients cannot understand it) is highly secure and virtually impossible to counterfeit – a crucial feature lacking in traditional currencies (US dollar, Euro, etc.).
A blockchain is a digital ledger consisting of blocks which store encrypted details of transactions and are then chained together to form an uneditable record which can be duplicated and distributed across global computer networks.
This underlying blockchain technology allows cryptocurrency transactions to be processed much faster than traditional currency exchanges, with lower or zero transaction fees.
The permanent, unalterable record of transactions increases trust in digital currencies and due to its share ability to users across the world, is highly transparent.
Every cryptocurrency is entirely defined and governed by its code.
As codes are created by a human developer who conducts the cryptography process, it is vital that the coding process is transparent otherwise this could lead to a loss of trust and damage the stakeholders of a particular code – rendering the entire cryptocurrency obsolete.
Therefore, by having “open code” where codes are accessible to the public, this protects the cryptocurrency and stakeholders from manipulations. However, this assumes that cryptocurrencies are isolated entities with zero connections.
Researchers at Bayes have discovered that 4% of developers are acting in a non-transparent manner and have contributed to the code of more than one cryptocurrency and that their market value is impacted by this cross-asset dependency (i.e. one developer creates codes for both cryptocurrency X and cryptocurrency Y, therefore the market value of cryptocurrency Y is directly linked to that of cryptocurrency X).
Their work is allowing researchers, investors and regulators to better understand this previously overlooked dimension of code-based ecosystems by demonstrating a clear link between the collaborative development of cryptocurrencies and their market behaviour.
Artificial Neural Networks and Financial Forecasting
Forecasting financial market returns is one of the most effective tools for risk management and portfolio diversification. Given the significance of financial markets, forecasting financial returns occupies a paramount position in investment decision making.
Financial markets are characterized by high volatility, dynamism, and complexity. Their movements are influenced by several factors, such as macroeconomics, international events, and human behaviour. Hence, forecasting financial markets trends is an important and challenging task.
The profitability of investments highly depends on the predictability of the market movements. If a forecasting model or technique can precisely predict the direction of the market, investment risk and uncertainty can be minimized.
It would enhance investment flows into the markets and also be useful for policymakers and regulators in making appropriate decisions and taking corrective measures.
There are several forecasting techniques used in Risk Management to obtain accurate forecasts for investment decision making, ranging from time series econometric models such as generalized autoregressive conditional heteroskedasticity (GARCH) to technical analysis and machine learning methods.
Recently, a class of artificial intelligence (AI) models—such as feedforward, backpropagation, and recurrent neural network models—have been introduced for forecasting purposes. The distinguishing features of artificial neural networks (ANN) are that they are data-driven, nonlinear, self-adaptive and have very few a priori assumptions.
Frequency-domain models, such as spectral analysis, wavelets, and Fourier transformations, have also been shown to improve the forecasting accuracy of financial markets.
Researchers at Bayes have extensive experience of forecasting consultancy in Risk Management and work in close collaboration with the International Institute of Forecasters (IIF).
Bayes research has established that the best forecasting accuracy is obtained by combining AI and traditional GARCH econometric models to a different degree, according to the liquidity of the markets.
*ShanghaiRanking Global Ranking of Academic Subjects 2021