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Stock price values can be predicted from past price data? 

Yes, Principal component analysis (PCA) identifies a small number of principle components that explain most of the variation in stock dataset. This method is often used for dimensionality reduction and analysis of the data.

PCA does reduce from 7 dimensional space of the SP 500 stocks ( 'Date', 'Adj Close', 'Close', 'High', 'Low', 'Open', 'Volume' ), into 2 dimensional space ( 'principal component 1', 'principal component 2' ).

Stock price values can be predicted from past price data? 

Yes, Principal component analysis (PCA) identifies a small number of principle components that explain most of the variation in stock dataset. This method is often used for dimensionality reduction and analysis of the data.

PCA does reduce from 7 dimensional space of the SP 500 stocks ( 'Date', 'Adj Close', 'Close', 'High', 'Low', 'Open', 'Volume' ), into 2 dimensional space ( 'principal component 1', 'principal component 2' ).

This clustering and visualizing of S&P 500 stocks use several unsupervised learning techniques, (Affinity Propagation clustering, Manifold learning techniques ..), to extract the stocks of S&P 500 from variations of close and low prices in historical quotes.

The quantity that we use is the daily variation, 1314 trading days in 5 years, in quote price: quotes that are linked tend to fluctuate in relation to each other during a day.

Stock price values can be predicted from past price data? 

Yes, Principal component analysis (PCA) identifies a small number of principle components that explain most of the variation in stock dataset. This method is often used for dimensionality reduction and analysis of the data.

PCA does reduce from 7 dimensional space of the SP 500 stocks ( 'Date', 'Adj Close', 'Close', 'High', 'Low', 'Open', 'Volume' ), into 2 dimensional space ( 'principal component 1', 'principal component 2' ).

Stock price values can be predicted from past price data? 

Yes, Principal component analysis (PCA) identifies a small number of principle components that explain most of the variation in stock dataset. This method is often used for dimensionality reduction and analysis of the data.

PCA does reduce from 7 dimensional space of the SP 500 stocks ( 'Date', 'Adj Close', 'Close', 'High', 'Low', 'Open', 'Volume' ), into 2 dimensional space ( 'principal component 1', 'principal component 2' ).

This clustering and visualizing of S&P 500 stocks use several unsupervised learning techniques, (Affinity Propagation clustering, Manifold learning techniques ..), to extract the stocks of S&P 500 from variations of close and open prices in historical quotes.

The quantity that we use is the daily variation, 1313 trading days in 5 years, in quote price: quotes that are linked tend to fluctuate in relation to each other during a day.

Stock price values can be predicted from past price data? 

Yes, Principal component analysis (PCA) identifies a small number of principle components that explain most of the variation in stock dataset. This method is often used for dimensionality reduction and analysis of the data.

PCA does reduce from 7 dimensional space of the SP 500 stocks ( 'Date', 'Adj Close', 'Close', 'High', 'Low', 'Open', 'Volume' ), into 2 dimensional space ( 'principal component 1', 'principal component 2' ).

Stock price values can be predicted from past price data? 

Yes, Principal component analysis (PCA) identifies a small number of principle components that explain most of the variation in stock dataset. This method is often used for dimensionality reduction and analysis of the data.

PCA does reduce from 7 dimensional space of the SP 500 stocks ( 'Date', 'Adj Close', 'Close', 'High', 'Low', 'Open', 'Volume' ), into 2 dimensional space ( 'principal component 1', 'principal component 2' ).

This clustering and visualizing of S&P 500 stocks use several unsupervised learning techniques, (Affinity Propagation clustering, Manifold learning techniques ..), to extract the stocks of S&P 500 from variations of high and low prices in historical quotes.

The quantity that we use is the daily variation, 1312 trading days in 5 years, in quote price: quotes that are linked tend to fluctuate in relation to each other during a day.

Stock price values can be predicted from past price data? 

Yes, Principal component analysis (PCA) identifies a small number of principle components that explain most of the variation in stock dataset. This method is often used for dimensionality reduction and analysis of the data.

PCA does reduce from 7 dimensional space of the SP 500 stocks ( 'Date', 'Adj Close', 'Close', 'High', 'Low', 'Open', 'Volume' ), into 2 dimensional space ( 'principal component 1', 'principal component 2' ).