diff --git a/README.md b/README.md
index 94ee1e66..3217a0e6 100755
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+++ b/README.md
@@ -271,9 +271,6 @@ The covariance matrix encodes not just the volatility of an asset, but also how
 ef = EfficientFrontier(mu, S, weight_bounds=(-1, 1))
 ```
 
-```result
-```
-
 - Market neutrality: for the `efficient_risk` and `efficient_return` methods, PyPortfolioOpt provides an option to form a market-neutral portfolio (i.e weights sum to zero). This is not possible for the max Sharpe portfolio and the min volatility portfolio because in those cases because they are not invariant with respect to leverage. Market neutrality requires negative weights:
 
 ```python
@@ -311,9 +308,6 @@ SBUX: 0.0330
 ef = EfficientFrontier(mu, S, weight_bounds=(0, 0.1))
 ```
 
-```result
-```
-
 One issue with mean-variance optimization is that it leads to many zero-weights. While these are
 "optimal" in-sample, there is a large body of research showing that this characteristic leads
 mean-variance portfolios to underperform out-of-sample. To that end, I have introduced an
@@ -439,9 +433,6 @@ PyPortfolioOpt provides a test dataset of daily returns for 20 tickers:
 ['GOOG', 'AAPL', 'FB', 'BABA', 'AMZN', 'GE', 'AMD', 'WMT', 'BAC', 'GM', 'T', 'UAA', 'SHLD', 'XOM', 'RRC', 'BBY', 'MA', 'PFE', 'JPM', 'SBUX']
 ```
 
-```result
-```
-
 These tickers have been informally selected to meet several criteria:
 
 - reasonably liquid