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Portfolio diversification involves lowering the correlation between portfolio assets to achieve improved risk–return exposure. It is reasonable to infer from the classic Anscombe quartet that relying on descriptive statistics, and specifically, correlation, to achieve portfolio diversification may not derive the most optimal multiperiod portfolio risk-adjusted return because stocks in a portfolio can exhibit different price trends over time, even with the same computed pairwise correlation. This research applied a shape-based time-series clustering technique of agglomerative hierarchical clustering using dynamic time-series warping as a distance measure to aggregate stocks into like-trending clusters across time as a portfolio diversification tool. Results support the use of the shape-based clustering technique for (1) portfolio allocation and rebalancing,(2) dynamic predictive portfolio construction, and (3) individual stock selection through outlier identification. The findings will be a useful addition to the existing literature in portfolio management by providing shape-based clustering as an alternative tool for portfolio construction and security selection.