Hi,

I'm wondering if anyone could answer some of my AlphaLens/probability related questions I have in my search for alpha factors and finding reasonable weighing options between the factors:

1. Does a positive IC skew mean that there's more predictive information in the top quantile (for longs) and vice versa for negative skew? If not, what does the IC skew tell me?

2. If p-value is below 5% for the 5 day holding period but above for 1 day (or vice versa), do I accept or reject the 'null hypothesis?'

3. Does choosing a longer test period reduce the likelihood of getting a false-negative p-value? (maybe I should be more concerned with false-positives?) What's a good balance? Is one year + one month of future returns sufficient?

4. If I still 'believe' in a factor, but p-value > 0.05, is it wrong / bad practice to test a different time series to see if it returns a p-value < 0.05? Essentially p-value hunting to 'fit' my hypothesis (did I just answer my own question there?). It's possible that a factor has predictive power during some time periods, and not during others though, correct?

5. If a 'combined_factor' has some individual factors with p_values above 0.05 and negative IC during the test period, but when removing them from the 'combined_factor' results in significantly lower returns in backtests, what could be the reason for this? Any advice on what to do with the 'bad' factors? Should I try to find which combination on non-predictive factors that when combined have a p-value of < 0.05?

6. Does a higher IC Kurtosis mean that there are more extreme outliers and therefore a stronger case for 'winzorizing' the factor? My factors tend to have fairly low IC Kurtosis values (well below 3, which I believe is the peak for a normal distribution). Is this good or bad?

I would really appreciate if anyone could help me answer any of these, or point me in the right direction (e.g. lecture or other resource). Thanks.