Financial analysis is an essential technique employed by companies and economists to predict future trends and risks based on current and past statistical data. Financial analysts correlate data through various models which can then be used to determine facts; form recommendations for future practices; determine the likelihood of bankruptcy; more accurately predict consumers who are high insurance risks; and forecast factors which are necessary for effective financial planning. Two key models used are univariate and multivariate financial analysis which analyse statistics in different ways.
Understanding the differences between the two models of univariate and multivariate financial analysis is not generally expressed in layman’s terms as the issues are complex. Their affect on consumer finance is of relevance when the analysis is completed and used to assist in the underwriting process.
Univariate financial analysis models concentrate on individual variables rather than multiple ratios. It isolates variables such as profitability or debt levels and studies the data in isolation without relying on equating other data into the calculations. As thus univariate financial analysis can have limitations as the results obtained do not incorporate influencing variables.
Multivariate financial analysis involves examining multiple variables simultaneously to measure co-efficient equations: simply defined it assumes the variable to be analysed and then incorporates variable characteristics for comparison to add further dimensions. Whilst univariate analysis specifically predicts one trend or risk, multivariate analysis combines ratios to consider a broader impact from data.
There are various models which can be used in multivariate financial analysis including regressive, residual and multiple discrimination. The results are often expressed in graph form such as the commonly used histograms.
A very simple way of understanding the statistical analysis used and how it affect decisions based on analysed data is to consider how an insurance company would incorporate it when establishing risk factors for claims. An example is the insurer needing to assess claims made by male car drivers. Simply factoring data collected from claim patterns by male drivers in isolation the univariate method would be used.
The results would be limited though so employing the multivariate method the analyst would introduce multiple variables such as the age group, income group, and employment category of male drivers. Whilst univariate analysis would show the risk potential of all male drivers, the multivariate results could fine tune it to predict the risk of 40-60 year old male drivers with an income level of over $80,000 per annum employed in a secure professional sector.
Complex mathematical equations may be used but the above example expresses the difference between univariate and multivariate financial analysis in a manner which is easily comprehended by the lay person. Financial analysis of either kind is an ongoing business as variables can naturally change quickly making the results no longer reasonably predictive.
Sources: Financial Statement Analysis – Gokul Sinha