Update README.md

README.md

@@ -23,7 +23,7 @@ In the simplest case the user provides a $T\times n$ pandas DataFrame
 of returns $r_1,\ldots,r_T$ and $K$ half-life pairs, and gets back covariance
 predictors for each time step. (The $K$ experts are computed as iterated
 exponentially weighted moving average (IEWMA) predictors as described in
-Section 2.6 of the [paper](https://web.stanford.edu/~boyd/papers/cov_pred_finance.html).)
+Section 2.5 of the [paper](https://web.stanford.edu/~boyd/papers/cov_pred_finance.html).)
 In the more general case, the user provides the $K$ expert predictors
 $\hat\Sigma_t^{(1)},\ldots,\hat\Sigma_t^{(K)}$, $t=1,\ldots,T$, and these are
 blended together by solving the convex optimization problems. In either case