Financial data analysis is a comprehensive interdisciplinary discipline that deeply integrates the knowledge of statistics, mathematics, economics and other fields. In the analysis of financial data, it is necessary not only to master the basic principles and methods of these disciplines, but also to apply professional knowledge in related fields such as economics, finance, accounting, and management. The basic methods and tools of financial data analysis are essential to effectively sort out and analyze problems in the financial field, which can help us systematically summarize problems and propose effective solutions. Through the application of these methods and tools, we can have a deeper understanding of the operation of the financial market and provide strong support for investment decisions.
In the basic methods of financial data analysis, due to the high complexity of financial data itself, it is often necessary to use mathematical models to describe the characteristics of financial data. Therefore, financial data analysis usually involves multiple mathematical models, which are inseparable from statistical calculations and data visualization to establish and solve.
Data preprocessing consists of two main steps: data cleaning and standardization. The purpose of data cleansing is to remove outliers, eliminate missing values, and remove incomplete data. Missing values are usually filled in by imputation. For incomplete data, it is usually filled in by substitution, including deletion, deletion of duplicate dates, etc. Data normalization refers to the standardization of data to a certain specified range. For the standardized data, the median, mode and other methods are generally used for statistical analysis and evaluation. Common standardized methods include mean method, median method, standard deviation method, root mean square method, etc., and among the basic methods of financial data analysis, the mean method and median method are commonly used.
In the basic methods of financial data analysis, the purpose is to obtain a preliminary understanding of the data, which is convenient for subsequent statistical analysis and modeling work. It mainly includes descriptive statistical charts, descriptive statistical parameter calculation, descriptive statistical distribution, descriptive statistical chi-square test, etc.
Correlation analysis is a very important research method in statistics, which refers to a statistical analysis method that studies the quantitative relationship between two variables. Commonly used methods are correlation coefficients, correlation analysis, and multiple linear regression analysis.
Cluster analysis and factor analysis are commonly used statistical methods in financial data analysis, which can be used to classify and study different variables, such as financial indicator classification, customer classification, commodity classification, etc., and can also be used to study the correlation between variables, such as index correlation, variance correlation, correlation coefficient matrix, etc.
Through the Yueshu graph database, enterprises can easily break the data silos and realize the effective and unified management of data assets. Regardless of the type and standard of data, the database can be easily connected to quickly import large-scale data and perform low-latency real-time computing. Combined with the basic methods and tools of financial data analysis, companies can better understand market trends and make more informed decisions, resulting in better business growth and development.