In a business trading comparable model, the information tends to be quite a lot more detailed than we've necessarily put in from the analysis from scratch. But let's take a look at some of the additional information and I want to explain some of the elements here. So we've got the ticker of the company often you may want to link it to the share price. So you need a formula that references the ticker. Then we've got a country, the analysis date, this is as of March 7th, so this is a little older than the ones we did from scratch, but I'm gonna leave it as it is now because otherwise I'd have to change many more numbers. And then we've got a calendarized two date. And the reason that that number or that date is in there is that companies have different year ends. And that doesn't necessarily even mean that the year end is at the quarter end. Sometimes it is. So for example, Apple's year end is the 30th of September. So this means the 10 K filing is as that date. And that means that if you're comparing Apple to another company like Microsoft, which has a 10 K end date as of December 31st, they're not directly comparable. And that's true of not just the historical earnings, but their forecast earnings because the analysts will be forecasting on a fiscal year basis rather than say a calendar year basis. But if we're comparing companies, we need to make sure that the earnings period are consistent. So we need to choose a date where we get all their historical numbers and forecast numbers adjusted or can rise to the year end date. And we've decided here that we want December 31st as our year end date. We've got a currency and that's just in case you want to convert things like market capitalization for the multiples, it doesn't matter because multiples are just a ratio of value to earnings and they both will be in the same currency. So it doesn't matter if they're both in a different currency because the multiples will still be good to compare even if they're being calculated with different base currencies because they're just a ratio got a marginal tax rate if you need to make any adjustments. Historical year end. And then we've just got some issues to do with the date format home currency.

And then we've got the foreign exchange rate and then we've got the latest closing price. And that will typically be the last traded day prior to the valuation date. And the reason we use the last traded day is that this gives us an audit trail. It's easy to find the closing price of the prior traded day. It's not easy to find just a random share price during the day. We've also got the 52 week high and low because that allows you just to see if this company's trading at the high or low end of its range because you would normally expect the peer group to all trade within a similar place within the 52 week range. So the whole sector, if it's been revalued, will trade at the top end of its range. If it's been valued downwards, it will trade at the bottom end of that range. If one of the companies is out of sync, it could mean that it's in play for being an acquisition target. And so it's quite useful to know that we've got a credit rating for any credit comps a beta for our WACC calculation. And then we've got some growth estimates of what the forward growth will be. And then we've got some margins and a return on investor capital, which are calculated here. Then down below we've got the enterprise value to equity value bridge laid out, making all the adjustments for dilution, for debt and for financial assets. And then we've got the earnings breakdown again, we've got the historicals, then we've got the LTM calculation and further along we've got the consensus estimates. And remember, consensus estimates will be based on a fiscal year and not necessarily a calendar year end or pretty much always a fixed fiscal year end, not calendar year. So we've got the consensus estimates and then if you come further down, we've also got a lease adjustment because if you are comparing US GAAP companies with IFRS companies and you want them on the level playing field, you need to convert operating leases to debt equivalents for US companies, which means treating the operating lease liability is debt and adding back the operating lease expensed EBITDA to get EBITDA plus rents. So EBITDA with an R on the end.

And then down at the bottom here you can see we've got some categorization workings. So if you had to adjust the company's numbers to make their year end, the calendar year end, this is a calculation. So it would just calculate whether you need to take a percentage of last year or a percentage of the next year to get a kind of weighted average number. So for example, Apple reports in September 30th as their year end. And if you're comparing them to Microsoft, which reports to December 31st for apple's numbers, you'd need to remove 25% of their year number and then add 25% of the next forecast year to get an equivalent year that would be comparable to Microsoft. And this is done automatically in this model. And if we go up to the top, you can see we've got a little calculation for strength on the right, the calendarized key numbers here for the forecast years, we'll do that weighted average to make sure that the year end is as at December 31st. Now for this company, we don't need to do that because their year end is already 31st of December, but if it wasn't, then the calendarization below would adjust the forecast numbers. So they would be equivalent to a year ending December 31st. You'd never categorized LTM numbers, you just leave them alone because that's always the latest 12 months. And the reason we leave them alone is for valuation we don't really use LTM multiples unless you're doing say an LBO analysis. Generally the LTM numbers are just used in your credit stats for things like debt to LTM EBITDA. And the reason the credit markets use LTM numbers is that they're much more conservative. They don't want to base their lending on forward earnings. They want to base their lending on historically delivered earnings because that's more conservative. So that gives you an overview of this sheet, and it's a good example of the kind of thing that you probably may find in a treading comps model on Wall Street.