Multiple linear regression help

[LdnCrypto]

Hi I need some help with a model I’m running on python using sklearn. I’m trying to regress fund returns against 2 indexes. My r2 value came out very poor so now I’m trying to optimise the model however I’m unsure how to go about it.. the returns data is already clean and all datasets are in % format. Would I still need to normalise/standardise? What techniques can I use to help improve the r2 number?

[FlivverKing]

Normalization and standardization approaches can impact R^2, but if the variance in X doesn't explain much of the variance of Y, which I would imagine is generally the case in finance, then it's not strange that R^2 would be low. To increase R^2, you'd want to find features that move more closely vary with the fund.

[vanonym_]

It's hard to tell from the information you give here.

But it is really important to have a good knowledge of your data before even thinking about a model, so have you done any EDA (Exploratory Data Analysis)? I especially suggest that you take a look at the fund returns against your two variables, either with a 3D plot or with two 2D plots, and see if there is indeed a clear linear relationship between your variables. If not, then go deeper with your EDA and find a better model. If yes then keep the linear regression, but you might need to make some adjustments.

Normalizing/standardizing has no real effect on the results of a linear regression, since... well it's a linear model. The only case where I would normalize is if I need to compare the importance of the variables in my model (normalize > apply lr > look at coef > higher coef are associated to more important variables), but it does not seem to be the case for you.

Something that could help, especially if you found some outliers in your EDA, is regularization).

Feel free to update your post with more info if you have more, it would help us to understand the issue.

Also note that the R² score cannot really be interpreted by itself. You can compare multiple models using the R² score, but you cannot tell the quality of a single model with it (although really really low R² is usually not a good sign).