The world is changing for quantitative researchers in finance. Not long ago, they actually needed to know something about financial services; these days, they just need to be… quants.
So said Marcos Lopez de Prado, former head of machine learning at hedge fund AQR Capital Management and currently a professor of machine learning at Cornell University. Speaking at this week’s AI and Data Science in Trading Conference in London, Lopez de Prado said the nature of quantitative research in finance is changing as market participants stop employing their own quants and start crowdsourcing their quant research needs instead.
Under the “old paradigm,” investment managers each hired their own siloed teams of researchers with specific finance knowledge, Lopez de Prado said. However, these in-house teams were hard to staff; very few quants combined the “domain knowledge” and quant expertise necessary to tackle finance datasets. “Consequently, many complex investment opportunities were not arbitraged or exploited,” Lopez de Prado added, pointing out that only 0.65 percent of articles in key economic journals contain terms related to artificial intelligence.
Faced with a dearth of finance quants, Lopez de Prado thinks that finance firms have innovated. Instead of hiring their own quant finance teams who understand the market, buy-side firms are increasingly inviting quants of all backgrounds to compete in tournaments aimed at deriving forecasts from large, seemingly generic datasets.
“The data is obfuscated,” Lopez de Prado said. In other words, crowd-sourced quants compete to find patterns in financial data even though they have no idea what the data actually refers to: “You don’t know that the data refers to IBM or that a column is the PE ratio.” It’s all about looking for patterns, and the data could just as easily refer to migratory birds as stock tickers.
If this sounds like bad news for quant researchers in the financial services industry, it surely is. Lopez de Prado didn’t say so, but the implication is that steady jobs for finance-specific quantitative researchers who commanded a premium by virtue of their domain knowledge are being threatened by an army of cheap generalist quants sitting in their bedrooms and looking for patterns in generic data.
Instead of drawing a salary and receiving a bonus, Lopez de Prado said, these new “bedroom quants” are driven by the prospect of winning individual prizes for accurate forecasts. “Their reward is the payout for the week,” he said, using the example of Numerai, the San Francisco-based crowd-sourced hedge fund, which works on the basis that 40,000 crowd-sourced data scientists know best. Numerai offers a regular ‘bonanza’ for competitors with the most accurate predictions.
Funds using the tournament method potentially save money many times over. They don’t just cut spending on employment costs, but on the expenses of hardware and office space, too. “More and more people will realize the [old] silo approach is not scalable and that the advantages [of crowdsourcing] are offset by the disadvantages. We are going to see crowdsourcing more prevalent in quantitative research,” Lopez de Prado predicted. That sounds ominous if you currently have a quant job on the buy-side, but maybe good if you are mathematically inclined and interested in doing a little quant work from your couch.
This article originally appeared in eFinancialCareers.