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These day, there is relatively new phenomena of combining quantitative data and fundamental data called 'Quantamentals'.

In this regards, I was wondering how to combine Four Essential Types of Financial Data

1) Fundamental Data(eg. Asset, Liability,sales)
2) Market Data(Volume, Price/Yield,Volatility)
3) Analytics Data(Analyst Recommendations, Credit Rating) and

4) Alternative Date(Satellite, Twitter)

into one data frame for analyses, given difference in frequency.

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There are at least two ways of doing it:

1) Resampling them to their median frequency.
2) Build one ML model for each data type, then combine the 4 different forecasts into a single meta-ML model.

(Courtesy: MARCOS LO´PEZ DE PRADO)

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Consider investigating the MIDAS approach of incorporating signals with different frequencies.

The classical apporach is to create a signal based on each source and tune it to your trading/rebalancing frequency. Convert this signal to an alpha based on Grinold and Khan, then add the alphas together.

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  • $\begingroup$ Are there any machine learning models for MIDAS or kalman-filters $\endgroup$ – Azam Yahya Apr 11 '18 at 9:34
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Keep in mind that if we turn to Arbitrage Pricing Theory then we can model the returns of the asset by some linear combination of the features.

Take the Carhart four-factor model as an example it incorporates financial statement features with momentum which is a feature derived from closing prices.

There are two books that provide a lot of context: Quantitative Equity Portfolio Management & the bible of factor investing Active Portfolio Management.

Another common technique is to make use of a dimensionality reduction algorithm such as PCA to decompose all your different features into a few features that are information rich.

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