NonCorrelated News Report: Looking back at the 2007 global “quant meltdown,” where numerous equity long-bias firms simultaneously crashed in August of that year, Goldman Sachs partner Gary Chropuvka has a scared memory. Chropuvka, who today is one of two partners managing the New York-based Quantitative Investment Strategies group for the bank, remembers seeing nearly $123 billion of the firm’s $165 million under management leave the division in the wake of an equity-focused quant meltdown. Now, having built the assets under management to $110 billion, the strategy has changed and Chropuvka has a different outlook.

Key quantitative lesson learned: Strategy diversification

“We view this as a turnaround,” Chropuvka told Bloomberg of the build in assets under management.

When Chropuvka looks back at the causation points of the “quant crash” and changes his strategy going forward, recognizing important differences and similarities between the quant crash and the current environment.

Much like concerns voiced over volatility targeting, risk parity and other systematic strategies common today, there was concern after the August, 2007 quant meltdown, which was confined to equity-based quantitative strategy, that systematic programs executed sell signals as the market was falling, exacerbating the move. Goldman then had all their assets allocated towards the equity quant funds. But they learned from their lesson.Today the quant team is managing factors through smart beta investment products and mutual funds and allocates accordingly based on big data choices. The benchmark for the team is not absolute returns or “beating” a market benchmark, but rather consistent performance during a variety of market environments.

In a market beset by “quant overcrowding,” Chropuvka noted that quant signals sometimes lose their effectiveness, as do analysts once they become more widely followed.

The quant division’s success is not all attributed to the computer, however. “People play a paramount role,” he was quoted as saying, pointing to a deeper fundamental explanation for algorithmic market signals. “We use economic intuition and ask: why does something work and will it work going forward?”