Time series regression forecast next month from now (random forest,Lasso,Ridge)
You could try to predict difference $dy = Y_{t+1} - Y_{t}$, not the $Y_{t+1}$ itself. Intuitively, the weather today is good starting prediction for the weather tomorrow.

Secondly, with time series, the seasoning is quite important. You usually would like to use models like ARIMA that takes this into account.

Lastly, it could be just don't enough data to make good predictions. I don't know what is these indexes exactly, but I'm not sure you can use data from one fund to predict another. This leaves you with about 100 points of training data (8 years monthly) and it's not a lot.
I would recommend you to solve 1-d (predict one fund by only it's data) with $dY$ trick, then try ARIMA