Why it is difficult to forecast the future of a stock, using AR model if there is a low autocorrelations? I read the following sentences and was confused Why?  This book

In Figure 16.2 we see that autocorrelations are rather weak so that it is difficult to predict future outcomes using, e.g., an AR model.

I always thought that if we want to model time-series data, then we must remove any autocorrelation. Then why if we found that the autocorrelation is low it is difficult to model the data using the AR model?


Then why if we found that the autocorrelation is low it is difficult
to [predict new] data using the AR model?

You refers on financial returns, more precisely:

Many financial practitioners encounter this problem quite early. The fast reply is that the "low autocorrelation" is simply too low. This kind of problem is quite general in statistics. Indeed even if we have some significant statistical result this not mean that this result can be used fruitfully for some practical scope. The magnitude of the effect matters a lot. Exist a motto that sound like "statistical significance do not mean economic profitability", it come from this problem.
A more technical answer can involve concepts like parameters estimation errors and overfitting. Indeed if you split the sample can see that even if the part "in sample" shown low, but significant, autocorrelation, test set results are usually poor. I have seen cases when ACFs seemed even more interesting but do not passed this simple prove.
