I have a few questions on how to clean/preprocess financial time series data. Are there any sources that can help with this?
The questions are:
- How should I adjust the data to deal with price changes related to dividends, stock splits, buybacks, etc?
- How should I deal with outliers? Should I remove them or keep them since they produce important information?
- How should I detect and deal with missing or otherwise corrupted/incorrect data?
- How does my cleaning methodology change based on my needs, i.e. should I clean differently for risk management vs. volatility forecasting?
- How should my procedure change based on the frequency of my data?
- How can I denoise/improve the signal-to-noise of my time series for better parameter estimation?
- When should I avoid different data augmentations/processing? What are the drawbacks of each augmentation?
- What other considerations am I missing?
Basically, is there a comprehensive (or as comprehensive as possible) guide to cleaning financial time series data that includes these topics? I'm mostly interested in data dealing with equities, indices, and derivatives on these.
I also read somewhere about using AI for this, are there any good resources on that?