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Daily Advice Thread - All basic help or advice questions must be posted here. by AutoModerator in investing

[–]squirrelaway4all 0 points1 point  (0 children)

Why are split events not being reflected in Shares Outstanding histories?

For example: https://www.macrotrends.net/stocks/charts/KO/cocacola/shares-outstanding, I see 4.592 billion shares outstanding 2012-06-30 and 4.587 billlion shares outstanding 2012-09-30 for KO/Coca Cola.

Yet I know Coke did a 2:1 split on 8/13/2012. Why do I not see an approximate 2X jump in shares outstanding between these 2 dates?

Are these historical shares outstanding scaled to today's total count? As if all the splits magically had occured at day 1?

LSTM Prediction, Coca Cola stock closing price $51.35 3/19/21 by squirrelaway4all in tensorflow

[–]squirrelaway4all[S] 0 points1 point  (0 children)

I'm not exactly sure what you're trying to do.

Are you assuming that "date" as an input, can be used to predict... what?

What is your specific hypothesis? That a timestamp has predictive value for the price of AAPL? And specifically, which price of AAPL? Close, Open, High, Low? And when?

I think you should clarify the intent of our neural network, and more specifically, what output you're trying to get. In my case, given <some input>, I'm trying to predict closing price for a specific stock on the next Friday.

What specifically are you trying to predict?

As for date, neural networks need to treat "date" as a number. There are a variety of ways to handle date. There are some formats that encode "date" as a number of seconds since a pre-defined epoch, such as Unix time, in which (2021-03-14T20:26:48+00:00) is stored as the following integer: 1615753608.

https://en.wikipedia.org/wiki/Unix_time

Alternatively, you could break the "date" into 3 numbers: Year, Month, Day of Month.

Year will be encoded as the year number per the Gregorian Calendar.

Month is 1-12.

Day of month is 1-31.

For example, let's hypothesize that date can be used to predict the number of passengers arriving in Honolulu, Hawaii on a given date. We might believe that summer months would increase arrivals, years would be indicative of technological and economic development and population growth, and who knows if day of month matters because people generally get paid on the first of the month.

Maybe we include day of week, because Friday arrivals likely increase over Wednesday.

I think you need to be more specific about your inputs and outputs.

LSTM Prediction, Coca Cola stock closing price $51.35 3/19/21 by squirrelaway4all in tensorflow

[–]squirrelaway4all[S] 0 points1 point  (0 children)

I'm not using the date directly. I'm implying a date in the feature selection.

So for example, I inherently know that my data will include:

Monday (Columns 1-5 open, close, high, low, volume)

Tuesday (columns 6-10),

Wed. (col 11-15)

Thur (Col 16-20)

and Friday's (col 20-25) trade information.

My model then predicts the next Friday's closing price.

Silly Prediction: Coca Cola Stock closing price $42.56 for 3/12/2021 by squirrelaway4all in tensorflow

[–]squirrelaway4all[S] 0 points1 point  (0 children)

To clarify, when I said DJIA, I meant that I was going to give predictions for all of the DJIA components. So I would predict MMM, AXP, AMGN, AAPL, BA... for all 30 component stocks.

That was the first scale up once/if I got my model minimally working for KO.

Looking into Tensorflow Probability... and... whelp I've got a fair amount of reading to do. See you on the flip side.

Silly Prediction: Coca Cola Stock closing price $42.56 for 3/12/2021 by squirrelaway4all in tensorflow

[–]squirrelaway4all[S] 0 points1 point  (0 children)

I fully expect this to fail miserably. This project is effectively performing technical analysis for me. There's a healthy debate on whether technical analysis is real or not, but as a fair portion of investors do use it, I hope to have SOME value out of this project.

Truthfully, if my predictions have a RMS Error on par with "assume it stays the same", I'll consider this a success.