Time-series analysis is a type of statistical analysis used to investigate the changing behavior of a variable over a period of time. It is used by financial managers to forecast and analyze trends, set financial objectives, predict investment outcomes, and assess performance against industry standards.
Overview and Key Characteristics
Time-series analysis is the study of data points over a period of time. It aims to identify trends and patterns in data and extrapolate them into future forecasts. These patterns may include linear, non-linear, polynomial, exponential, seasonal, or other complex variations.
Time-series analysis is used to identify cyclical patterns in data, as well as sudden changes or disruptions. It is often used to predict future behavior of business and market trends, and to set financial objectives.
Time-series analysis is also used to analyze a company’s performance against industry standards. This allows financial managers to evaluate the effectiveness of their investments against comparable benchmarks, and improve processes and strategies where needed.
Key Features
Time-series analysis is a powerful method for uncovering patterns, trends, and disruptions in data. Some key features of this method include:
• Assessing data over time to identify trends and cyclical behavior
• Forecasting future results based on past performance
• Performing linear, non-linear, polynomial, seasonal, and other complex analyses
• Offering insight into key financial objectives and industry standards
• Analyzing the performance of investments against comparable benchmarks
Real-World Example
A financial manager at a software company may conduct time-series analysis to forecast the total number of licenses sold in the upcoming year. The manager starts by collecting all of the monthly license sales data from the last 5 years. Then, they run a regression analysis to identify any trends or patterns in the data. They may discover that license sales increase exponentially during the second quarter of the year, due to seasonal spending patterns.
Based on this information, the financial manager can then extrapolate this pattern into the future and accurately forecast the number of licenses sold in the upcoming year. They can also set realistic financial objectives and budget allocations for the upcoming year.
Conclusion
Time-series analysis is a useful tool for forecasting, analysis, and decision-making. It enables financial managers to uncover trends in data, predict investment outcomes, and assess performance benchmarks. This method offers a powerful insight into the behavior of company data points over time, allowing for more accurate predictions and better decision-making.