[Lemaxoxo]

Do you have some data/resources on this? I'm a total snowflake at this, but I'm willing to learn.

[airstrike]

In hindsight this is harder than it seems if you don't already have access to data, so I'll try to be as informative as possible in my response

It's hard to find this information out there, so here's ~all you need to know.

Data is usually behind paywalls, unfortunately. Industry standards are Bloomberg terminal (ridiculously expensive, 5 digits $), FactSet (very expensive, 4 digits), Capital IQ (expensive, not sure)... but there are a number of up-and-coming startups trying to disrupt the space so you may be able to grab data from them. I think https://atom.finance has a 7-day free trial you could use to play around with.

P/E simply means the company's _P_rice per share divided by _E_arnings per share. Cancel out the "per share" terms and you get total market capitalization (which is the value of the total equity) divided by net income (since "earnings per share" really means "net income per share")

So the "P" is easy to get. It's your Adj Close.

The "E" is trickier as it can mean a lot of things. Diluted EPS from financial statements? Last year's EPS? Management's guidance for EPS? None of those are actually correct even if they are all "EPS"

Importantly--and contrary to 99% of the info you will find online--the most relevant EPS number are forward estimates of EPS, usually for the next twelve months ("NTM"). That is based on an average or median of analyst estimates which is called "consensus". These are analysts from financial institutions who build their own little models based on their own views of where the business is going to go, informed by recent earnings, management's color in earnings calls and filings, etc.

Believe it or not, as hairy as that sounds, EPS is fairly easy to get as it's a metric that has less room for interpretation than, say, EBITDA.

So you're not going to go out there, read all these (paid) analyst reports, find their EPS, calculate the median, etc. Bloomberg, Capital IQ, FactSet do this for you and it's easily observable for the end user (that's their business).

The thing is, as you may have guessed, "next twelve months" are a moving target across time. Analysts usually provide estimates for the current fiscal year (i.e. FY 2023, ending 12/31/2023 for most companies) and the following year, ending 12/31/2024. Let's call these FY0_EPS and FY1_EPS, for simplicity

You might be tempted to just take a moving average of these two estimates, so that on 1/1/2023 it is 100% of FY0_EPS + 0% of FY1_EPS, on 1/2/2023 it is 99.9% + 0.1% and gradually "move forward in time" as the days pass. That sort of works (and definitely checks the box for a proof-of-concept like in your post) but for the sake of completeness, I'll just say that the right-er approach is to only "move forward in time" when new earnings are released. So it doesn't matter if we're in 1/1/2023 or 2/1/2023--what matter is what is the latest reported quarter. Take Coca-Cola for instance (https://www.bamsec.com/companies/21344/coca-cola-co). Let's roll the tape backward one year. They reported FY 2021 earnings on 2/22/2022, at which point analysts published new estimates in revised models, so on from that day forward until the next quarterly earnings we take 100% FY0_EPS + 0% FY1_EPS, in which these correspond to estimates for FY 2022 and FY 2023, respectively.

On 4/1/2022, Coca-Cola reported Q1 2022 results, analysts published new estimates, and we now take 75% FY0_EPS + 25% FY1_EPS. On 7/1/2022, we move forward another quarter so 50% + 50%, then 25% + 75% starting on 10/26 and then back to square one with 100% + 0% except FY0_EPS now means FY 2023 vs FY 2022 previously, and FY1_EPS means FY 2024

So your table is something like (I'm making up numbers)

    +------------+--------+-----------+---------+---------+-------------+-------------+-------------+
    | Date       | Ticker | Adj_Close | FY0_EPS | FY1_EPS | Period      | NTM0_Weight | NTM1_Weight |
    | 01/01/2022 |     KO |     90.10 |   20.00 |   24.00 | Q1 2022     |        1.00 |        0.00 |  
    | 01/02/2022 |     KO |     91.14 |   20.00 |   24.00 | Q1 2022     |        1.00 |        0.00 |  
    | 01/03/2022 |     KO |     89.30 |   20.00 |   24.00 | Q1 2022     |        1.00 |        0.00 |  
    | 01/04/2022 |     KO |     91.09 |   20.00 |   24.00 | Q1 2022     |        1.00 |        0.00 |  
    | 01/05/2022 |     KO |     92.01 |   20.00 |   24.00 | Q1 2022     |        1.00 |        0.00 |  
    | 01/06/2022 |     KO |     89.05 |   20.00 |   24.00 | Q1 2022     |        1.00 |        0.00 |  
    ...
    | 07/02/2022 |     KO |     89.05 |   19.50 |   23.20 | Q2 2022     |        0.75 |        0.25 |

With that you can take NTM0_Weight and NTM1_Weight to calculate NTM_EPS by multiplying those weights by FY0_EPS and FY1_EPS. And then can take AdjClose / NTM_EPS to calculate P/E

Why is this useful? Because in theory you can take the average P/E of companies X, Y and Z in one industry and compare it to a fourth company W. Is W's P/E multiple above or below the industry average? You now know if they are over or undervalued, respectively, which means you know if you should buy or sell that stock (if you believe you picked the right "comparable" companies in that industry)

This is just one example... there are all sorts of similar analyses done daily in the financial services industry. I'm not saying it's easy to extract alpha from trading on these, but that's the framework

[Lemaxoxo]

Thank you so much for this! It's very generous of you to have taken the time.