In Machine Learning, the standard expectation of research papers is to empirically demonstrate the increased statistical power of the model over benchmarks, and I wish the authors demonstrated that their model aligns with historical reality.
I attended a talk recently by Aswath Damodaran where he explained that there are two types of investors, most often lying in disjoint sets: 1) number crunchers and 2) story tellers. Perhaps the success of tech companies (e.g. AMZN, GOOG, FB) can be explained in aggregate via the Wright model, but my feeling is that the majority of 'optimal portfolio distortion' these companies caused is mostly idiosyncratic to those specific companies, e.g. the "story tellers" have more information in the long-term behavior of a company. Consequently, aggregate modeling captures statistical correlation rather than causation, which can still make money but is not robust to long tails.
I think number crunching is only useful for short horizon portfolio selection.