Equity Valuation and Portfolio Management

WORKING WITH DATA

In this section, we discuss how to work with data and data quality issues, including some well-probed techniques used to improve the quality of the data. Though the role of getting and analyzing data can be mundane and tedious, we need not forget that high-quality data are critical to the success of a trading strategy. It is important to realize model output is only as good as the data used to calibrate it. As the saying goes: “Garbage in, garbage out.”

Understanding the structure of financial data is important. We distinguish three different categories of financial data: time series, cross-sectional, and panel data. Time series data consist of information and variables collected over multiple time periods. Cross-sectional data consist of data collected at one point in time for many different companies (the cross-section of companies of interest). A panel data set consists of cross-sectional data collected at different points in time. We note that a panel data set may not be homogeneous. For instance, the cross-section of companies may change from one point in time to another.

Data Integrity

Quality data maintain several attributes such as providing a consistent view of history, maintaining good data availability, containing no survivorship, and avoiding look-ahead bias. As all data sets have their limitations, it is important for the quantitative researcher to be able to recognize the limitations and adjust the data accordingly.¹⁴

Data used in research should ...