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My data is stock data with features such as stocks' closing prices.I am curious to know if I can put the economy feature such as 'national interest rate' or 'unemployment rate' besides each stocks' features.

Data:

Date Ticker Open High Low Close Interest Unemp. 1/1 AMZN 75 78 73 76 0.015 0.03 1/2 AMZN 76 77 72 72 0.016 0.03 1/3 AMZN 72 78 76 77 0.013 0.03 ... ... ... ... ... ... ... ... 1/1 AAPL 104 105 102 102 0.015 0.03 1/2 AAPL 102 107 104 105 0.016 0.03 1/3 AAPL 105 115 110 111 0.013 0.03 ... ... ... ... ... ... ... ...

As you can from the table above, daily prices of AMZN and AAPL are different but the Interest and Unemployment rates are the same. Can I feed the data to my neural network like the table above?

In other words, can I put the individual stocks' information besides the environment feature such as interest rates?

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  • $\begingroup$ developing AI solutions is 99% experimenting with data so feel free to experiment! $\endgroup$ – quester Oct 4 at 13:36
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I am curious to know if I can put the economy feature such as 'national interest rate' or 'unemployment rate' besides each stocks' features.

The variables are macro-econometric and they, in general, seem to have some influence on stocks' prices. This inclusion might as well increase your model's prediction accuracy. You can definitely use them as predictors. As mentioned in comments - Experimentation is the way to go.

Can I feed the data to my neural network like the table above?

In general, you can have any kind of numeric variables as input to a neural network. Things will work out fine. The important thing is the selection of relevant predictor variables that, potentially, have some relationship with the response variable.

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