Financial Engineering and Risk Management

- Financial Engineering (FE)

Financial Engineering (FE) is a highly interdisciplinary field that draws on theoretical developments from financial economics, applied mathematics, operations research, statistics and computer science. FE is the use of mathematical techniques to solve financial problems. 

Financial engineering uses tools and knowledge from the fields of computer science, statistics, economics, and applied mathematics to solve current financial problems and design new and innovative financial products. 

The widespread use of FE has shaped the entire landscape of financial practice, particularly in derivatives pricing, portfolio management, and risk control. These applications in turn stimulate new problems in FE. 

- Financial Engineering and Risk Management

Financial engineering (FE) is a term coined with the help of modern information technology. FE is an interdisciplinary field in which analysts optimize and analyze a variety of financial decisions such as risk management, financial portfolio planning, forecasting, trading, hedging, fraud detection, and other applications. 

Today, the field of financial engineering has successfully integrated a wide range of quantitative analysis disciplines such as mathematics, statistics, time series, stochastic processes, data mining, and artificial intelligence.

- Machine Learning for Financial Engineering

In recent years, with the vigorous development of artificial intelligence, machine learning and data science, the research and application of financial technology (FinTech) have rapidly emerged and become an indispensable and important part of the financial field.

Machine learning grew out of the need to design algorithms that can learn from data to make accurate predictions and decisions. Such questions arise in various “big data” fields such as finance, genomics, information technology, and neuroscience. Financial engineering in machine learning ranges from the design of large-scale machine learning algorithms to mathematical analysis. 

- Bioinspired Computing for Financial Engineering

Financial operations are often associated with large cash flows and are subject to risk and uncertainty, attracting extensive research efforts to develop sophisticated quantitative models to manage these financial risks. 

There is growing evidence that the financial environment is not governed by mathematical distributions or statistical models. In the field of computer science, people try to develop financial engineering models using soft computing methods to build more flexible financial engineering models. 

Bioinspired computing is an approach to developing computer systems by drawing inspiration from the biological world. Many ideas taken from natural processes have been applied to machine learning and deep learning, leading to new developments in artificial intelligence. 

The pursuit of bionic computing technology is a recent trend, including artificial immune systems, particle swarms, ant colonies, bacterial foraging, artificial bees, harmonious search, nanocomputing, multi-objective, dynamic and large-scale optimization and other technologies being applied to daily life . 

Bioinspired computing enables and builds simple systems that can evolve into more complex systems.

- Soft Engineering for Financial Engineering

The most popular computer languages ​​used by financial engineers include: Python, Java, C++, Scala. However, it is important for aspiring financial engineers to be able to go beyond simple coding mastery. 

To complement their programming abilities, financial engineers should also be able to demonstrate higher-level computer science skills, including advanced modeling and mathematics. Additionally, artificial intelligence (AI) has become an increasingly important technology for financial engineers as they seek to understand, model and predict volatile financial markets.

Financial engineers work at the intersection of data science and the financial industry. For this reason, it is important for aspiring financial engineers to gain an in-depth understanding of market trends and current events about the world economy. 

[More to come ...]