Trient replaced the spreadsheets with a financial analytics platform developed using MATLAB.
After architecting and setting up a SQL Server® database for financial data, Fischer and the team began developing the data access layer of the analytics platform by writing MATLAB functions to access the financial data using Database Toolbox™.
They added functionality to retrieve market data from several market data providers using Datafeed Toolbox™. Working in MATLAB with Financial Toolbox™, they wrote data cleaning routines that identify outliers, perform linear interpolation for missing values, and normalize dates.
Fischer and the team completed the data access layer with functions that enhance the raw data series (for example, via filtering or calculating window averages), and then store these derived data series in the database.
Using the object-oriented programming capabilities of the MATLAB language, Fischer developed components with defined interfaces to the analytics layer, facilitating consistent use of the components across Trient.
To build the analytics layer, Fischer developed a screening framework with MATLAB and Statistics and Machine Learning Toolbox™. This layer uses fundamental market data and examines various factors to identify cross-region variations, for example as a function of industry and market capitalization.
For example, the framework for equities examines various fundamental data types and looks for differences between the fundamental value and the market value. For this layer, Fischer and the team used Financial Instruments Toolbox™ and Optimization Toolbox™.
Fischer used Econometrics Toolbox™ to run Monte Carlo simulations and generate forecast distributions of time-series models.
The data cleaning and screening calculation was accelerated by using Parallel Computing Toolbox™ and a multicore processor.
Trient analysts use the platform daily to generate reports and charts, perform studies, and run analyses that support investment decisions.