Quantitative Analysis Meaning
“Quantitative analysis (QA) is a technique that uses mathematical and statistical modeling, measurement, and research to understand behavior. Quantitative analysts represent a given reality in terms of a numerical value.” Quantitative analysis is used to monitor, assess productivity, value financial instruments, and forecast actual occurrences like shifts in a nation’s gross domestic product (GDP). The primary responsibility of a quantitative analyst is to express a particular hypothetical problem in terms of data variables.
Techniques of Quantitative Analysis
- Regression Analysis: Regression analysis is a widespread approach used by statisticians, economists, entrepreneurs, and other professionals. It includes making predictions or estimating the effects of one parameter on another using a statistical equation. Regression analysis may be used by entrepreneurs to assess how advertising costs affect their bottom line. A company owner can determine if two factors have a positive or negative association using this method.
- Linear Programming: Most businesses periodically lack resources, including commercial space, equipment for manufacturing, and manpower. Directors of businesses must devise strategies to deploy resources wisely in such circumstances. A quantitative approach that identifies how to arrive at such an ideal solution is linear programming. It is also used to assess, under a set of limitations, such as labour, how a business might maximize earnings and lower operating expenses.
- Data mining: It is the application of statistical techniques and computer programming knowledge to mine data. Data mining is becoming more and more popular as the variety and number of datasets expand. Extremely big data sets are analyzed using data mining techniques to look for hidden patterns or relationships.
Uses of Quantitative Analysis
Numerous areas of investment and commerce employ quantitative analysis. It frequently operates by contrasting data sets. Data mining, optimization, and financial modeling are more examples of quantitative analysis. While an investment corporation may utilize vast quantities of data to make purchase and sell decisions, “quant” investments for an individual may just require one or two indicators.
Everything that can have data gathered and monitored is susceptible to some sort of quantitative analysis. Today, enormous volumes of data are stored and processed daily. Many quantitative analysts employ robust computer methods and algorithms to uncover the most relevant data and find patterns or connections.