Dimitri A Sogoloff, President and CEO
Horton Point
Introduction
A few weeks after the unusually large drawdowns attracted everyone’s attention to the perils of “quantitative” investing, the popular opinion of what may have happened, has been formed. The answer, apparently, lies in the quantitative space becoming overcrowded, with most models generating similar forecasts and thus similar portfolios. Losses began with the rapid unwind of a large market-neutral equity portfolio and have generated ripples throughout the quant world.
Still un-answered is the more fundamental question: why have different quant groups, using supposedly different investment tools, ended up with similar models.
The empirical evidence suggests that the majority of the equity market-neutral models are indeed similar in their approach to forecasting price behaviour. In fact, they use one form of statistical analysis or the other (hence the commonly used term, “Statistical Arbitrage”). The first problem with statistical analysis of financial data is that it assumes stable relationships between market factors, which we know not to be the case. The other problem is that everyone is looking at the same data sets (after all, every security generates only one time series of historical returns).
This paper suggest an alternative approach to development of quantitative investment strategies; the one which eschews statistics in favour of more dynamic sciences (eg physics), and postulates that the future of quantitative investing lies in continuous scientific innovation and applications of modern scientific principles to capital markets.
