Combination of data mining and financial engineering-ariszheng

Combination of data mining and financial engineering -- ariszheng

Recently, a math expert asked me how to combine data mining and financial engineering? Which books are good tutorials? I am also a beginner.

In my philosophy, the combination of data mining and financial engineering is a composite work, and data mining is a method and tool that can be used in financial engineering. Financial product design, financial product pricing, transaction strategy design, and financial risk management in financial engineering involve a large amount of historical market data, financial data, and macro data. The main goal of data mining is to mine hidden potential rules in data. In the financial market, many financial analysts, or financial engineers, use data mining methods to analyze the market, in an attempt to discover the operating rules of the market, and use this rule to obtain low-risk benefits.

The combination of data mining and financial engineering currently has many points of integration:

(1) Relationship between stock market trends and macro data;

(2) The direct relationship between market trends and fund positions;

(3) Relationship between stock price trends and financial data

If you can find potential patterns in the market (assuming they exist), you can use these patterns to make profits. Data Mining is a tool for discovering these patterns.

The combination of data mining and financial engineering requires broad vision and deep knowledge accumulation. As for how to get started, you may find a combination of methods when you have a deep understanding of data mining, financial engineering, and financial markets.

Which books are good tutorials? Classic textbooks related to data mining, financial engineering, and financial markets are good entry points. A combination of A + B, AA + B ,......, AAA + BBB, which is the best, and the efforts made by compound talents are also compound.

Appendix:

 

QuantityData mining ),It is an extraordinary process of getting effective, novel, potentially useful, and ultimately understandable patterns from a large amount of data. Broadly speaking, data mining refers to the process of "mining" interesting knowledge from a large amount of data stored in databases, data warehouses, or other information databases. Data mining is also known as the knowledge discovery in Database (KDD). Some people regard data mining as a basic step in the knowledge discovery process in the database. The knowledge discovery process consists of the following steps: (1) data cleaning, (2) Data Integration, (3) data selection, (4) data transformation, (5) data mining, (6) mode evaluation, (7) Knowledge Representation. Data mining can interact with users or knowledge bases.

Not all information discovery tasks are considered as data mining. For example, a database management system is used to search for individual records or a specific web page through an Internet search engine, which is a task in the information retrieval (Information Retrieval) field. Although these tasks are important and may involve the use of complex algorithms and data structures, they mainly rely on traditional computer science and technology and the obvious characteristics of data to create index structures, this effectively organizes and retrieves information. Even so, data mining technology has been used to enhance the ability of information retrieval systems.

 

There are multiple definitions of financial engineering.The definition proposed by John Finnerty, an American financial scientist, is best: financial engineering includes the design, development, and implementation of innovative financial tools and financial means, and creatively solve financial problems.

Financial engineering has two concepts: narrow and broad. Financial engineering in the narrow sense mainly refers to the combination and decomposition of different forms based on various existing basic financial products using advanced mathematical and communication tools, to design new financial products that meet customer needs and have specific P/L characteristics. Financial engineering in the broad sense refers to all technical development that uses engineering methods to solve financial problems. It not only includes financial product design, it also includes financial product pricing, transaction policy design, and financial risk management.