Managing financial data in the 21st century means working with big data and financial technologies. (It’s a far cry from the 20th century’s statistical analysis and spreadsheets!) From banking and investing applications, which collect data on financial markets and technology users, to the creation of cryptocurrencies and online investing, financial data has taken on new importance in our society. Financial data is ubiquitous and is no longer confined to banking institutions and investment firms. Financial analysts must draw on more advanced data analytics methods to sort through this unremitting collection and circulation of information.
While the growing FinTech industry sees increasing opportunities for data scientists, there are also multiple opportunities for financial analysis within the data science industry. Financial analysts using Python can begin building a more data-focused career in banking, investing, and software development. Python is the go-to programming language for analyzing financial statements and influencing business decisions. You can use Python’s financial libraries to make predictions, create risk assessments, and manage stock portfolios. Any financial professional or business analyst would greatly benefit from learning more about using Python to manage financial data!
What is Financial Data Management?
Financial data is any data collected from financial institutions or individuals representing monetary transactions or indicating those entities' economic health or capabilities. This data includes banking transactions, credit scores, stock prices, and taxes. Since most businesses and institutions work with a wide range of financial data, it is important to incorporate the practice of financial data management. Financial data management uses software and policies to regulate the collection and analysis of financial data in compliance with state and federal laws.
By incorporating Python into financial data management, analysts can easily manage larger stores of data and make more informed and data-driven decisions with that financial data. Proper financial data management also means that analysts are aware of the procedures and governance around protecting personal data and upholding the laws around investing, accounting, and handling the accounts of businesses and individuals. Financial data management is one of the main reasons that financial analysts use data science tools and programming languages, such as Python, in their work.
How Financial Analysts Use Python to Manage Data
Financial analysts use Python to analyze financial statements and holdings, manage stock portfolios and risk assessments, create predictions for financial investments, and build financial technology and software.
Analyzing Financial Statements
Today, financial analysis is heavily influenced by the unrelenting collection of data from numerous sources. So, the spreadsheet software and analytics tools of the 20th century are not always equipped to manage the size and volume of data that larger institutions and corporations have collected over time. This is why more financial analysts are turning to Python for analyzing financial statements. Python’s financial libraries enable analysts to access packages of pre-written codes and functions, making it easier to manage the various types of statistical analysis required to understand financial statements.
Most of these financial libraries focus on numerical data, so it’s common to see overlap with Python’s data science libraries. For example, many financial analysts use libraries such as Pandas and NumPy to analyze statements and keep track of account holdings. In addition, you can use libraries like Quandl with both Python and Excel to collect and analyze data from multiple financial institutions, such as banks and government offices. This library is also helpful for tracking economic trends and making investment decisions.
Stock Portfolio and Risk Management
Financial analysts also use Python to manage stock portfolios and to create risk assessments and models. Risk assessments are commonly used to weigh the consequences of a financial decision through a simulation or predictive analytics. These analyses can then determine the potential outcome of making one decision over another. Creating financial models that assess risk is most useful when making investments and managing a stock portfolio. Financial analysts can use past stock market data to predict what might happen in the market in the future.
Financial analysts using Python can employ platforms and mobile applications for algorithmic trading to manage stock portfolios and mitigate risk. There are numerous Python libraries with packages and reusable code explicitly created for developing trading strategies, whether traditional or cryptocurrency. One example is PyAlgoTrade, a free and open-source algorithmic trading library that evaluates historical financial data to build an automated trading strategy.
Financial Technology and Software Development
While most financial analysts are interested in managing financial data for banking and stocks, many recognize that the future of financial data is in software and technologies that allow users to manage their personal and business finances. Financial analysts can also use Python to learn more about software engineering and development, specifically, the development of mobile applications for other forms of financial technology, such as cryptocurrency.
For example, several cryptocurrencies are written using Python programming languages, and Python Developers can also use currencies like Ethereum to trade assets within a virtual environment. Developing products and services that innovate more traditional and hierarchical banking and investing models enables analysts and developers to create a financial technology future in which users have all the tools they need to make stock and investment decisions.
Is Financial Analysis in Your Future?
Financial data analysis methods and technologies continue to evolve and grow, meaning financial and business analysts will require more advanced data science and programming training. Noble Desktop’s data science classes are instructive for analysts who want to develop skills that will be useful in the industry's future. The FinTech Bootcamp introduces the many uses of financial data in the 21st century, from creating models for risk assessment to managing stock portfolios and investments. The Python for Finance Bootcamp offers training in Python’s financial libraries and statistical analysis for more advanced financial professionals. Additionally, Noble Desktop offers Python classes that include training in this programming language and specific business and finance applications for Python.