Big Data techniques transform the landscape of portfolio management analytics. The classic portfolio allocation framework developed by Markowitz (1952, 1959) suggests that portfolio returns can be dramatically improved if investors distribute their holdings across many different assets and, potentially, asset classes. Markowitz portfolio allocation theory suggests that investors benefit from distributing their nest eggs among different investment baskets. Should one basket fall and the eggs contained within break, the other baskets will be unharmed. The key factors commonly considered in the Markowitz framework are the historical returns of the prospective portfolio constituents, which in turn allow for the measurement of historical variance and correlations of the returns among different “eggs”.
Modern Big Data analyses of factors that dominate variance-covariance show that factors other than the traditional measures, such as microstructure-related factors, improve portfolio analysis. Consider the following example: Russell 3000 equities, that is, 3000 of the most commonly traded U.S. stocks, and their 10 characteristics as recorded on December 29, 2015:
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