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Since the CAPM model Sharpe (1965) and the first “fundamental” model by King (1966) the use of “factors” in alpha generation and risk modeling has become mainstream. However, the types of factors we employ and the techniques we use to model relationships have in general not progressed much since. In addition, many of our favorite techniques assume that the world is static, whereas of course markets evolve and change dramatically; as we have seen so vividly illustrated over the last few years.
We review fundamental, macroeconomic, and statistical factors, describing the advantages and disadvantages of each, and review some newer techniques that explicitly allow for evolving relationships in data sets and harness emerging technologies that can capture much more nuanced relationships than simple correlation: “flexible” leastsquares regression, artificial immune systems, singlepass clustering, semantic clustering, social network influence measurement, layerembedded networks, blockmodeling, and more.
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