What Is a Cross-Sectional Analysis? With Uses and Example
Updated December 5, 2022
Whether you work within the finance industry or are looking to make investments, there are many methods you can use to assess a company's performance. One popular method is cross-sectional analysis, which people often use to compare certain metrics from one company to another. Learning more about this method can help you feel more comfortable using it to assess a company or provide financial advice and recommendations.
In this article, we discuss cross-sectional analysis, its most common uses and how it differs from a time series analysis, and we provide an example analysis.
A cross-sectional analysis allows people who work in various fields to compare their company to their main competitors in the industry.
Although it's common in financial companies, education, retail, governments and health care fields also can use these studies to improve their operations.
Using a cross-sectional analysis with a time series analysis can help you evaluate investment opportunities or advise clients about investment options.
What is cross-sectional analysis?
Cross-sectional analysis is a method of analyzing data about a population or predefined subject at a specific time. People in the finance industry often use cross-sectional analysis to compare companies. For example, financial analysts, investors or portfolio managers may use this method to assess investment opportunities or compare company performance.
Some people refer to a cross-sectional analysis as a cross-sectional study, transverse study or prevalence study. When performing this analysis, the subjects involved typically have at least one variable in common. In finance, this variable may be that they're all companies within the same industry or that provide the same product or service.
How the finance industry uses cross-sectional analysis
Finance experts can use cross-sectional analysis to look at an industry and identify and analyze the same metrics from each company in the target group. The data they gather for this analysis often comes from companies' financial statements, such as balance sheets, income statements and cash flow statements, though they can include other areas of the business. Although a cross-sectional analysis doesn't provide insights about the cause of high or low performance, it may signal a need to perform additional analyses or research to learn more.
By assessing the strengths and weaknesses of each organization, people can help themselves or their clients make more informed investment decisions. Business leaders also can compare their organization to peers to understand their competition, identify potential areas for improvement and set organizational goals. There are many metrics people or entities can look at and measure when assessing an organization, dependent on their specific needs or interests. Here are some examples of what they may examine:
Valuation: The valuation of a business represents how much it's worth, and there are several methods and ratios people can use to determine its value. Understanding an organization's valuation can be useful when deciding whether to buy, sell or invest in it.
Debt load: The debt load represents the total amount of debt an organization or entity carries, and you often can find this figure on a public company's balance sheet. Potential investors may analyze a company's debt in combination with other factors, such as cash flow, to assess whether it can pay its liabilities and whether its debt represents a significant risk.
Operational efficiency: Operational efficiency measures how efficiently a company earns a profit based on its operating costs. A higher operational efficiency represents a more profitable company because it's able to generate more income for the same or lower costs.
Return on equity: Return on equity (ROE) measures a company's income compared to shareholders' equity, which represents the owners' claim on assets after paying debts. Investors typically compare an organization's ROE against its peers to evaluate it, with a good ROE being equal to or above the group's average.
Other uses for cross-sectional analysis
Several other industries use cross-sectional analysis as a method of observational research. Observational studies represent a process in which a person or organization studies a subject without manipulating or influencing it or any of the study's factors. This type of cross-sectional analysis requires choosing a subject and comparing it against similar subjects at a certain time. These subjects often have at least one thing in common. Here are some examples of other uses for cross-sectional analysis:
Education: People in education can use cross-sectional analyses to assess their students' performance within a school or district. For example, a district may gather scores of a standardized state test and compare the results from children from different income levels or schools within the district.
Retail: Market research within the retail industry may use cross-sectional analysis to evaluate customer trends. For example, a retail store may ask shoppers to fill out a survey about their preferred shopping experiences or apparel styles.
Government: Officials and members of the public can gather data from various government sources to assess a population. For example, policymakers may look at unemployment data each month to compare the performance in different states against one another.
Health care: Medical or health care organizations may use cross-sectional analysis to determine the prevalence of an illness within a population. For example, an organization may research the prevalence of diabetes in the elderly population, comparing the data from one state to another.
Cross-sectional analysis vs. time series analysis
Cross-sectional analysis and time series analysis represent two common stock analysis methods, which allow people to examine the stock market and evaluate opportunities to buy and sell stocks. Although they serve similar purposes, these methods differ based on their focus and methodologies:
When performing cross-sectional analysis, people look at data from one point in time. They define the metrics they want to analyze from a target company, then determine the other companies to compare the data against, such as its industry peers. For example, an individual may want to compare the performance of three home improvement retailers to determine which represents the best investment option. Once they determine the metrics to measure and the cross-section of companies to compare, they analyze the data from a designated time, such as the balance sheet for a specific month.
Time series analysis
Instead of comparing a target company or group during a point in time, a time series analysis examines one company over a longer period. Some people refer to this analysis as a trend analysis. Using this method can allow someone to assess an organization based on its past and present performance. They may choose one or more metrics to assess, then monitor and analyze them over a period to assess whether they improve. Financial investors and analysts often use a combination of cross-sectional and time series analyses to gain more comprehensive insights about an organization and its performance.
Example of cross-sectional analysis
People in the finance industry often use a cross-sectional analysis to compare the performance of one company against another. Here's an example of how this works:
Deepa is a financial analyst who wants to assess and compare the financial positions of two companies, which are Happy Camp and Sunlight Adventurer, to help a client determine whether to invest in one of them. To perform the cross-sectional analysis, Deepa looks at their end-of-year balance sheets for 2021. Deepa determines she wants to measure and compare the debt ratios, which show what portion of the company's debt its assets fund, of each company to help assess their financial health. To find this, she divides total debt by total assets.
Happy Camp's balance sheet shows $50,000 in debt and $125,000 in assets, and Sunlight Adventurer's balance sheet shows $35,000 in debt and $50,000 in assets. According to these figures, Happy Camp's debt ratio is equal to 0.4, or 40%, and Sunlight Adventurer's debt ratio is 0.7, or 70%. A ratio higher than 100% means a company has more debt than assets, which can create challenges when repaying those debts if interest rates increase. As a result, Deepa may consider recommending an investment in Happy Camp because its debt ratio is lower and its equity funds more of its assets.
This article is for informational purposes only and does not constitute financial advice. Consult with a licensed financial professional for any issues you may be experiencing.
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