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Quant Signals: PCA model and Backtesting Features

Using a Principal Component Analysis (PCA) model to analyze assets across financial markets provides a powerful framework for investment decisions. By mapping out the macro anatomy of a given asset, PCA identifies key trends and underlying patterns that influence price fluctuations and market dynamics.
2024-05-24
<a href="https://stenoresearch.com/author/cvh-2514/" target="_self">Claus Hornsleth</a>

Claus Hornsleth

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Steno Research PCA model 

Using a Principal Component Analysis (PCA) model to analyze assets across financial markets provides a powerful framework for investment decisions. By mapping out the macro anatomy of a given asset, PCA identifies key trends and underlying patterns that influence price fluctuations and market dynamics. In this process, we have defined what we see as the most important global macro factors, ensuring that our analysis is comprehensive and targeted. This allows investors to anticipate market movements and make informed investment decisions. We recently decided to go short XLU US Equity. Lets use that as usercase.

Using a Principal Component Analysis (PCA) model to analyze assets across financial markets provides a powerful framework for investment decisions. By mapping out the macro anatomy of a given asset, PCA identifies key trends and underlying patterns that influence price fluctuations and market dynamics.

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