This week, we focused on using the SAS programming techniques we have learned so far to conduct a real-world analysis of financial portfolio performances. This project included merging several groups with large data quantities, filtering and organizing them by date, standardizing the data to be on the same scale, and using portfolio weights to accurately depict the daily returns for each portfolio against a benchmark. To summarize our findings, we created charts and graphical visualizations to compare the actual returns versus the expected returns against the benchmark. This project served as a prime example of a real-life situation where SAS programming skills and the ability to effectively execute data analysis would be highly valued. While working through this scenario, I found myself wanting to tackle several steps in the programming language at once, which would often result in an error that I would have to dig through my work to decipher. For example, when executing the merge by date step, it required inputting each asset and the associated date function one by one with proper syntax to avoid any simple mistakes. This was a primary example of why it is often worthwhile to do each programming step one at a time to avoid simple mistakes and ensure the language is properly formatted.
The experience we gained through this case scenario will be useful in future endeavors where comparing large groups of data, which may or may not be standardized in its original form, and using the information extracted from the analysis to make informed and validated business decisions. Looking back on my own career endeavors, I could see this program being very helpful in previous medical case studies I worked on that involved filtering through thousands of patient records with infinite data tags for a more precise and specific patient cohort. Prior to working with SAS, I primarily relied on Excel to execute data filtering, which does not have nearly the same data capacity or analytical capability as SAS does.
