How to show that the forecasting error is influenced by certain phenomenon/variable I am doing some research and I want to predict stock returns based on the so-called IVSkew variable (which is simply a variable based on the implied volatilities of option contracts).
It has already been shown to have predictive power in previously conducted research. However, I try to show that the predictive power has significantly decreased since the influx of retail investors in the stock market.
I therefore have the following challenge:
How can I show that the predictive power of a certain variable has decreased because of the influx of retail investors?
[By the way, I use the retail investors' stock volume to quantify the influx]
I was thinking about finding the correlation between the forecast errors and investors' stock volume.
But does that make any sense at all? Is there any other way that I can show that the predictive power (and thus forecasting error) has decreased because of the influx of retail investors?
Thank you very much in advance.


How can I show that the predictive power of a certain variable has decreased because of the influx of retail investors?

Naive way would be to generate two test sets for the predictive model's performance: one with retail investors one without. So the problem will be to investigate the effect of intervention, i.e., the influx of retail investors. Options of measuring is not unique. But naive way to see if predictive power decreasing with the intervention.
