The basics of quantitative modeling
One exciting goal I have for this blog is to articulate the basic methods of quantitative modeling, followed by, hopefully, collaborative real-time examples of how this craft works out in given examples. Today I just want to outline the techniques, and in later posts I will follow up with a post which goes into more detail on one or more points.
- Data cleaning: bad data (corrupt) vs. outliers (actual data which have unusual values)
- In sample/ out of sample data
- Predictive variables: choosing and preparing which ones and how many
- Exponential down-weighting of “old” data
- Remaining causal: predictive vs. descriptive modeling
- Regressions: linear and multivariate with exponentially down-weighted data
- Bayesian priors and how to implement them
- Open source tools
- When do you have enough data?
- When do you have statistically significant results?
- Visualizing everything
- General philosophy of avoiding fitting your model to the data
For those of you reading this who know a thing or two about being a quant, please do tell me if I’ve missed something.
I can’t wait!