Predicting stock market index values using individual stocks
$begingroup$
I'm trying to predict the market trend (i.e. predict the value of a stock market index, e.g. S&P 500) using the stocks in the index.
My data-set is as follows:
Date | Stock | Sector | Sub Industry | Head Quarters | Market Cap | ... | Close
------------------------------------------------------------------------------------------------------------------------------------
01-01-2001 | AAL | Industrials | Airlines | Texas | ... | ... | ...
01-01-2001 | AAPL | Information Technology | Technology Hardware, Storage & Peripherals | California | ... | ... | ...
Each stock has a category (sector, industry etc.) that influences its price. Also stocks having similar properties (e.g. sector, industry) may exhibit correlation. For this reason, I wish to model all the stocks and not the index itself.
Initial Approach
I tried to transform the data-set for performing regression based on auto-correlation.
The transformed data-set looks like this:
Stock Category | Sector Category | Sub Industry Category | Head Quarters Category | Previous 3 Close | Previous 2 Close | Previous 1 Close | Close
---------------------------------------------------------------------------------------------------------------------------------------------------
1 | 3 | 4 | 12 | ... | ... | ... | ...
1 | 3 | 4 | 12 | ... | ... | ... | ...
1 | 3 | 4 | 12 | ... | ... | ... | ...
1 | 3 | 4 | 12 | ... | ... | ... | ...
2 | 4 | 6 | 8 | ... | ... | ... | ...
The stock name, sector, industry etc. are all numerically encoded ( I could have used one-hot encoding, but that resulted in too many columns to handle). For every stock, for every day, I have 3 additional columns - the close prices for the previous 3 days. The close price for the current day is the output.
This model, I think, takes into consideration that stocks in one category have some correlation. Based on the prediction for all the stocks, the predicted value for the index can be computed.
My questions are:
Are there any improvements I can make to the above model?
Is there any other alternative, e.g. traditional time-series, LSTM etc. that can be used (that considers the correlation between stocks)
time-series regression finance
asked 3 mins ago
prajwaldpprajwaldp
1
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$begingroup$
I'm trying to predict the market trend (i.e. predict the value of a stock market index, e.g. S&P 500) using the stocks in the index.
My data-set is as follows:
Date | Stock | Sector | Sub Industry | Head Quarters | Market Cap | ... | Close
------------------------------------------------------------------------------------------------------------------------------------
01-01-2001 | AAL | Industrials | Airlines | Texas | ... | ... | ...
01-01-2001 | AAPL | Information Technology | Technology Hardware, Storage & Peripherals | California | ... | ... | ...
Each stock has a category (sector, industry etc.) that influences its price. Also stocks having similar properties (e.g. sector, industry) may exhibit correlation. For this reason, I wish to model all the stocks and not the index itself.
Initial Approach
I tried to transform the data-set for performing regression based on auto-correlation.
The transformed data-set looks like this:
Stock Category | Sector Category | Sub Industry Category | Head Quarters Category | Previous 3 Close | Previous 2 Close | Previous 1 Close | Close
---------------------------------------------------------------------------------------------------------------------------------------------------
1 | 3 | 4 | 12 | ... | ... | ... | ...
1 | 3 | 4 | 12 | ... | ... | ... | ...
1 | 3 | 4 | 12 | ... | ... | ... | ...
1 | 3 | 4 | 12 | ... | ... | ... | ...
2 | 4 | 6 | 8 | ... | ... | ... | ...
The stock name, sector, industry etc. are all numerically encoded ( I could have used one-hot encoding, but that resulted in too many columns to handle). For every stock, for every day, I have 3 additional columns - the close prices for the previous 3 days. The close price for the current day is the output.
This model, I think, takes into consideration that stocks in one category have some correlation. Based on the prediction for all the stocks, the predicted value for the index can be computed.
My questions are:
Are there any improvements I can make to the above model?
Is there any other alternative, e.g. traditional time-series, LSTM etc. that can be used (that considers the correlation between stocks)
time-series regression finance
asked 3 mins ago
prajwaldpprajwaldp
1
New contributor
prajwaldp is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
I'm trying to predict the market trend (i.e. predict the value of a stock market index, e.g. S&P 500) using the stocks in the index.
My data-set is as follows:
Date | Stock | Sector | Sub Industry | Head Quarters | Market Cap | ... | Close
------------------------------------------------------------------------------------------------------------------------------------
01-01-2001 | AAL | Industrials | Airlines | Texas | ... | ... | ...
01-01-2001 | AAPL | Information Technology | Technology Hardware, Storage & Peripherals | California | ... | ... | ...
Each stock has a category (sector, industry etc.) that influences its price. Also stocks having similar properties (e.g. sector, industry) may exhibit correlation. For this reason, I wish to model all the stocks and not the index itself.
Initial Approach
I tried to transform the data-set for performing regression based on auto-correlation.
The transformed data-set looks like this:
Stock Category | Sector Category | Sub Industry Category | Head Quarters Category | Previous 3 Close | Previous 2 Close | Previous 1 Close | Close
---------------------------------------------------------------------------------------------------------------------------------------------------
1 | 3 | 4 | 12 | ... | ... | ... | ...
1 | 3 | 4 | 12 | ... | ... | ... | ...
1 | 3 | 4 | 12 | ... | ... | ... | ...
1 | 3 | 4 | 12 | ... | ... | ... | ...
2 | 4 | 6 | 8 | ... | ... | ... | ...
The stock name, sector, industry etc. are all numerically encoded ( I could have used one-hot encoding, but that resulted in too many columns to handle). For every stock, for every day, I have 3 additional columns - the close prices for the previous 3 days. The close price for the current day is the output.
This model, I think, takes into consideration that stocks in one category have some correlation. Based on the prediction for all the stocks, the predicted value for the index can be computed.
My questions are:
Are there any improvements I can make to the above model?
Is there any other alternative, e.g. traditional time-series, LSTM etc. that can be used (that considers the correlation between stocks)
time-series regression finance
asked 3 mins ago
prajwaldpprajwaldp
1
New contributor
prajwaldp is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I'm trying to predict the market trend (i.e. predict the value of a stock market index, e.g. S&P 500) using the stocks in the index.
My data-set is as follows:
Date | Stock | Sector | Sub Industry | Head Quarters | Market Cap | ... | Close
------------------------------------------------------------------------------------------------------------------------------------
01-01-2001 | AAL | Industrials | Airlines | Texas | ... | ... | ...
01-01-2001 | AAPL | Information Technology | Technology Hardware, Storage & Peripherals | California | ... | ... | ...
Each stock has a category (sector, industry etc.) that influences its price. Also stocks having similar properties (e.g. sector, industry) may exhibit correlation. For this reason, I wish to model all the stocks and not the index itself.
Initial Approach
I tried to transform the data-set for performing regression based on auto-correlation.
The transformed data-set looks like this:
Stock Category | Sector Category | Sub Industry Category | Head Quarters Category | Previous 3 Close | Previous 2 Close | Previous 1 Close | Close
---------------------------------------------------------------------------------------------------------------------------------------------------
1 | 3 | 4 | 12 | ... | ... | ... | ...
1 | 3 | 4 | 12 | ... | ... | ... | ...
1 | 3 | 4 | 12 | ... | ... | ... | ...
1 | 3 | 4 | 12 | ... | ... | ... | ...
2 | 4 | 6 | 8 | ... | ... | ... | ...
The stock name, sector, industry etc. are all numerically encoded ( I could have used one-hot encoding, but that resulted in too many columns to handle). For every stock, for every day, I have 3 additional columns - the close prices for the previous 3 days. The close price for the current day is the output.
This model, I think, takes into consideration that stocks in one category have some correlation. Based on the prediction for all the stocks, the predicted value for the index can be computed.
My questions are:
Are there any improvements I can make to the above model?
Is there any other alternative, e.g. traditional time-series, LSTM etc. that can be used (that considers the correlation between stocks)
time-series regression finance
time-series regression finance
asked 3 mins ago
prajwaldpprajwaldp
1
New contributor
prajwaldp is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 3 mins ago
prajwaldpprajwaldp
1
New contributor
prajwaldp is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 3 mins ago
prajwaldpprajwaldp
1
New contributor
prajwaldp is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 3 mins ago
prajwaldpprajwaldp
1
asked 3 mins ago
prajwaldpprajwaldp
1
1
New contributor
prajwaldp is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
prajwaldp is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
prajwaldp is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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