In other words, categorical data is essentially a way of assigning numbers to qualitative data (e.g. 1 for male, 2 for female, and so on). However, a company with an excessively high TIE ratio could indicate a lack of productive investment by the company’s management. An excessively high TIE suggests that the company may be keeping all of its earnings without re-investing in business development through research and development or through pursuing positive NPV projects. This may cause the company to face a lack of profitability and challenges related to sustained growth in the long term. When e.g. a certain number of pieces is produced in a certain time span, here can be calculated, how long it takes for a different number or how many pieces are produced in a different time span.
Every sector is financed differently and has varying capital requirements. Therefore, while a company may have a seemingly high calculation, the company may actually have the lowest calculation compared to similar companies in the same industry. As a general rule of thumb, the higher the times interest earned ratio (TIE), the better off the company is from a credit risk standpoint. To improve its times interest earned ratio, a company can increase earnings, reduce expenses, pay off debt, and refinance current debt at lower rates.
As a rule, companies that generate consistent annual earnings are likely to carry more debt as a percentage of total capitalization. If a lender sees a history of generating consistent earnings, the firm will be considered a better credit risk. With ratio data, not only can you meaningfully measure distances between data points (i.e. add and subtract) – you can also meaningfully multiply and divide. For example, 20 minutes is indeed twice as much time as 10 minutes. You couldn’t do that with credit scores (i.e. interval data), as there’s no such thing as a zero credit score.
Numerical data, on the other hand, reflects data that are inherently numbers-based and quantitative in nature. In other words, these are things that are naturally measured as numbers (i.e. they’re quantitative), as opposed to categorical data (which involves assigning numbers to qualitative characteristics or groups). Categorical data is data that reflect characteristics or categories (no big surprise there!). For example, categorical data could include variables such as gender, hair colour, ethnicity, coffee preference, etc.
- We’ll now move on to a modeling exercise, which you can access by filling out the form below.
- The ratio does not seek to determine how profitable a company is but rather its capability to pay off its debt and remain financially solvent.
- As a rule, companies that generate consistent annual earnings are likely to carry more debt as a percentage of total capitalization.
- Some techniques work with categorical data (i.e. nominal or ordinal data), while others work with numerical data (i.e. interval or ratio data) – and some work with a mix.
- In this scenario, the duration of cooking time would be considered a ratio variable because there is a true zero value – zero minutes.
Nominal, Ordinal, Interval & Ratio Data
This is why ratio data is king in the land of measurement levels. Like interval data, it is ordered/ranked and the numerical distance between points is consistent (and can be measured). But what are current liabilities what makes it the king of measurement is that the zero point reflects an absolute zero (unlike interval data’s arbitrary zero point).
In all of these examples, the data options are categorical, and there’s no ranking or natural order. In other words, they all have the same value – one is not ranked above another. So, you can view nominal data as the most basic level of measurement, reflecting categories with no rank or order involved. When you’re collecting survey data (or, really any kind of quantitative data) for your research project, you’re going to land up with two types of data – categorical and/or numerical.
Get instant access to video lessons taught by experienced investment bankers. Learn financial statement modeling, DCF, M&A, LBO, Comps and Excel shortcuts. Here, we can see that Harrys’ TIE ratio increased five-fold from 2015 to 2018. This indicates that Harry’s is managing its creditworthiness well, as it is continually able to increase its profitability without taking on additional debt.
What Does a Times Interest Earned Ratio of 0.90 to 1 Mean?
A higher ratio suggests that the company is more likely to be able to meet its interest obligations, reducing the risk of default. Here, Company A is depicting an upside scenario where the operating profit is increasing while interest expense remains constant (i.e. straight-lined) throughout the projection period. While there aren’t necessarily strict parameters that apply to all companies, a TIE ratio above 2.0x is considered to be the minimum acceptable range, with 3.0x+ being preferred. Statology makes learning statistics easy by explaining topics in simple and straightforward ways. Our team of writers have over 40 years of experience in the fields of Machine Learning, AI and Statistics.
What is the Times Interest Earned Ratio?
The times interest earned ratio is a measurement of a company’s solvency. While a higher calculation is often better, high ratios may also be an indicator that a company is not being efficient or not prioritizing business growth. Times interest earned ratio is a solvency metric that evaluates whether a company is earning enough money to pay its debt. It specifically compares the income a company makes prior to interest and taxes to what interest expense it must pay on its debt obligations. The times interest earned ratio is highly dependent on industry metrics.
The times interest earned (TIE) ratio is a solvency ratio that determines how general rules for debits and credits well a company can pay the interest on its business debts. It is a measure of a company’s ability to meet its debt obligations based on its current income. The formula for a company’s TIE number is earnings before interest and taxes (EBIT) divided by the total interest payable on bonds and other debt. The result is a number that shows how many times a company could cover its interest charges with its pretax earnings.
The reason it’s important to understand the levels of measurement in your data – nominal, ordinal, interval and ratio – is because they directly impact which statistical techniques you can use in your analysis. Some techniques work with categorical data (i.e. nominal or ordinal data), while others work with numerical data (i.e. interval or ratio data) – and some work with a mix. While statistical software like SPSS or R might “let” you run the test with the wrong type of data, your results will be flawed at best, and meaningless at worst.
The higher the ratio, the better, as it indicates how many times a company could pay off its debt with its earnings. This ratio is crucial for investors, creditors, and analysts as it provides insight into the company’s financial health and stability. A higher TIE ratio suggests that the company is generating sufficient earnings to comfortably cover its interest payments, indicating lower financial risk. Conversely, a lower TIE ratio may signal financial distress, where the company struggles to manage its interest payments, posing a higher risk to creditors and investors.
In this case, one company’s ratio is more favorable even though the composition of both companies is the same. The times interest earned ratio looks at how well a company can furnish its debt with its earnings. It is one of many ratios that help investors and analysts evaluate the financial health of a company.
