Recently, my mentor and I were cooperating on a scientific research project. When we used statistical software to analyse the difference of financial indicators of delisted companies to create a delisting warning model, we found that the model, only based on assets and financial data, ignored other reasons for delisting such as improper guarantees, major shareholders’ encroachment and senior [University Name]agement violations. As companies who [University Name]oeuvered capital operations to prevent their companies from delisting would stay in the stock market with an inefficient use of stock market funds, we wanted to build a model that could measure the utilization rate of a company’s capital, instead of using a single indicator to determine whether a company should be delisted.

Hwang et al. (2014) developed an early warning model based on non-financial information. They think using non-financial information is more meaningful because it can help different stakeholders provide early warning signals, because non-financial information does not need to involve settlement and audit procedures, and is usually disclosed to the public in promptly.

This reminds me of my internship in Huishang Bank, where I witnessed our credit transmission mechanism and observed how information asymmetry made banks biased when issuing loans. Cecchetti and Kharroubi (2015) used to calculate the data of 15 OECD countries and found that the unbalanced flow of financial resources to “multiple collaterals + low productivity” sectors, such as the real estate and construction industries, had caused a surge in leverage and an asset bubble. On the other hand, due to the externalities of finance, talents are gathered in the financial sector, resulting in the loss of talents in the entity sector.

By comparing China and Ger[University Name]y, which has established a more comprehensive cooperative relationship between banks and enterprises, I conclude that, to solve the adverse effects of credit transmission on the real economy, it is necessary to eliminate information asymmetry and strengthen financial supervision as much as possible. Nowadays, a Chinese secure computing platform Ant Morse conducts data sharing under the premise of ensuring corporate data security and individual user privacy. If commercial banks build a similar dynamic security computing platform and formulate a unified security modelling and risk prediction joint control plan in the industry, it will help reduce business risks and improve the risk control capabilities of the entire industry.

I wanted to know more about works, so I participated in an internship at China Galaxy Securities Co. Ltd (CGS), where I mainly assisted colleagues in sorting out operation data and customers’ information. Also, I helped in the visual investment report of productions while investigating enterprises with predecessors to get aware of the business development process of institutions. At the same time, I was dispatched to assist in the project of initial public offering (IPO), responsible for inquiring suppliers and customers to collect business information, conducting data and correction accounting, as well as calculating development expenditure and capitalization of comparable companies in the same industry.

Fascinated in the application of various models and financial technology, I aspire to become a risk analyst who can apply models in the field of data mining for risk assessment in the future. I am very interested in the follow-up risk [University Name]agement and credit risk identification of wealth [University Name]agement products. I aspire to enter the insurance company headquarters for product design and credit risk identification or join the risk control department of a securities company. During the internship at Galaxy Securities, talking with my boss about careers, I am certain that this is what I aspire to do.

Financial Engineering Masters at the [University Name] teaches advanced financial mathematics combined with computational techniques. It focuses on statistical finance and mathematical modelling with a well-structured curriculum integrated with practical applications. The course setting allows me to mix statistics, econometrics and economics courses to fit my needs. the Financial Engineering Projects attracted me. When I was preparing my graduation thesis "Inquiry into the Optimal Portfolio Model under Partial Information", I was required to explore and improve the optimal portfolio model so that investors can apply it without observing all the information in the market. I understand the difficulty and importance of present modelling results to scientists who do not have a mathematical or computational modelling background

I have always wanted to learn a programming language, strengthen my data analysis skills, and broaden my future employment. The Advanced Mathematical Finance in the core course can help me get familiar with C++. Also, I look forward to using MATLAB extensively to construct and solve linear algebraic equations when learning the Numerical Methods and Numerical Linear Algebra course. ne of my teachers is dedicated to the study of game theory and behavioral finance I learned that game theory can not only be applied to optimize conflicting goals and select the optimal investment portfolio, but also play a role in the field of financial innovation, such as building a program of blockchain decentralized payment system. So I will choose Game Theory as one of my elective courses to learn more. All these courses can bring me one step closer to my career goals.

In addition, I aspire to work in the UK for a couple of years upon graduation, and the career support provided to students at the UoB is superb. [University Name] is a vibrant and thriving city with a diverse community is ideally located to reach several famous cities by train so that it is easy to secure internships. I would like to visit the [University Name] Museum& Art Gallery to see the masterpieces of European artists over the centuries wander around the city, looking for traces of the Industrial Revolution. As a lover of old buildings, I wait to study in such a historical school.

I am ready.

Reference

In Tae Hwang, Sun Min Kang & Shun Ji Jin, 2014, A delisting prediction model based on non-financial information, Asia-Pacific Journal of Accounting & Economics, 21:3, 328-347, DOI: 10.1080/16081625.2014.882322

Stephen G Cecchetti & Enisse Kharroubi, 2015. "Why does financial sector growth crowd out real economic growth?" BIS Working Papers 490, Bank for International Settlements.