Scientific Beta

ETF Stream: "Such would be the conclusion from recent research from Scientific Beta, which has released two recent papers looking into the definitions of factors used by many of the leading providers in this area. At the heart of the problem is the ability of researchers to wield ever greater computing power in search of factors which ‘work’ in a given dataset. But as Felix Goltz, research director at Scientific Beta, says, these factors will have no relevance outside the original dataset due to selection biases. Hence the backtest performance can evaporate once the factor goes live."

ETF Stream 07/05/2019

"(...) Smart beta has a problem and the issue lies in its use of data to justify newer, fancier factors which, it might be fairly reasoned, are more precisely structured to capture the imagination of investors rather than relying on the actual numbers. Such would be the conclusion from recent research from Scientific Beta, which has released two recent papers looking into the definitions of factors used by many of the leading providers in this area. At the heart of the problem is the ability of researchers to wield ever greater computing power in search of factors which ‘work’ in a given dataset. But as Felix Goltz, research director at Scientific Beta, says, these factors will have no relevance outside the original dataset due to selection biases. Hence the backtest performance can evaporate once the factor goes live. “Product providers explicitly acknowledge that the guiding principle behind factor definitions is to analyse a large number of possible combinations in short data sets and then retain the factors that deliver the highest backtest performance,” he says. “If this performance is due to patterns that are specific to the sample, we are unlikely to detect them in backtests for different regions, or in backtests with deeper histories. Likewise, we are unlikely to detect similar performance once the factor goes live.” (...)"