What Is Cross Sectional Analysis and How Does It Work?

What is Cross-Sectional Analysis?

Cross-sectional analysis is a type of analysis where an investor, analyst or portfolio manager compares a particular company to its industry peers. Cross-sectional analysis may focus on a single company for head-to-head analysis with its biggest competitors or it may approach it from an industry-wide lens to identify companies with a particular strength. Cross-sectional analysis is often deployed in an attempt to assess performance and investment opportunities using data points that are beyond the usual balance sheet numbers.

Key Takeaways

  • Cross-sectional analysis focuses on many companies over a focused time period.
  • Cross-sectional analysis usually looks to find metrics outside the typical ratios to produce unique insights for that industry.
  • Although cross-sectional analysis is seen as the opposite of time series analysis, the two are used together in practice.

How Cross-Sectional Analysis Works

When conducting a cross-sectional analysis, the analyst uses comparative metrics to identify the valuation, debt-load, future outlook and/or operational efficiency of a target company. This allows the analyst to evaluate the target company's efficiency in these areas, and to make the best investment choice among a group of competitors within the industry as a whole.

Analysts implement a cross-sectional analysis to identify special characteristics within a group of comparable organizations, rather than to establish relationships. Often cross-sectional analysis will emphasize a particular area, such as a company's war chest, to expose hidden areas of strength and weakness in the sector. This type of analysis is based on information-gathering and seeks to understand the "what" instead of the "why." Cross-sectional analysis allows a researcher to form assumptions, and then test their hypothesis using research methods.

The Difference Between Cross-Sectional Analysis and Time Series Analysis

Cross-sectional analysis is one of the two overarching comparison methods for stock analysis. Cross-sectional analysis looks at data collected at a single point in time, rather than over a period of time. The analysis begins with the establishment of research goals and the definition of the variables that an analyst wants to measure. The next step is to identify the cross-section, such as a group of peers or an industry, and to set the specific point in time being assessed. The final step is to conduct analysis, based on the cross-section and the variables, and come to a conclusion on the performance of a company or organization. Essentially, cross-sectional analysis shows an investor which company is best given the metrics she cares about.

Time series analysis, also known as trend analysis, focuses in on a single company over time. In this case, the company is being judged in the context of its past performance. Time series analysis shows an investor whether the company is doing better or worse than before by the measures she cares about. Often these will be classics like earning per share (EPS), debt-to-equity, free cash flow and so on. In practice, investors will usually use a combination of time series analysis and cross-sectional analysis before making a decision. For example, looking at the EPS overtime and then also checking the industry benchmark EPS.

Examples of Cross-Sectional Analysis

Cross-sectional analysis is not used solely for analyzing a company; it can be used to analyze many different aspects of business. For example, a study released on July 18, 2016, by the Tinbergen Institute Amsterdam (TIA) measured the factor timing ability of hedge fund managers. Factor timing is the ability for hedge fund mangers to time the market correctly when investing, and to take advantage of market movements such as recessions or expansions.

The study used cross-sectional analysis and found that factor timing skills are better among fund managers who use leverage to their advantage, and who manage funds that are newer, smaller and more agile, with higher incentive fees and a smaller restriction period. The analysis can help investors select the best hedge funds and hedge fund managers.

The Fama and French Three Factor Model credited with identifying the value and small cap premiums is the result of cross-sectional analysis. In this case, the financial economists Eugene Fama and Kenneth French conducted a cross-sectional regression analysis of the universe of common stocks in the CRSP database.   

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