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Facebook discloses new measurement errors, continues to hone its math

Facebook misreported engagement numbers for links and live videos and is refining the ad reach estimates it shows advertisers.

marketingland.com

Math is hard, even for Facebook. And it’s getting harder to trust Facebook’s math, even as the social network fine-tunes its calculations.

For the third time since September, Facebook is disclosing new measurement errors. The two new errors affected the reaction counts Facebook reports on Pages’ Live videos, as well as the engagement figures Facebook reports for off-Facebook links; the latter link engagement metrics were recently used in investigations by BuzzFeed and The New York Times into fake news articles’ performance on Facebook.

Facebook has confirmed a discrepancy exists between two measures of the Facebook engagement rates for outside links, but the company has not been able to identify the reason for the measurement anomaly.

The two metrics in question are: 1) the total number of shares, likes and comments a link has received on and off Facebook, reported through Facebook’s Graph API; and 2) the number of people who have shared or commented on that link, reported through search results in Facebook’s mobile apps.

Inside a Moneymaking Machine Like No Other

The Medallion Fund, an employees-only offering for the quants at Renaissance Technologies, is the blackest box in all of finance.

www.bloomberg.com

Sixty miles east of Wall Street, a spit of land shaped like a whale’s tail separates Long Island Sound and Conscience Bay. The mansions here, with their long, gated driveways and million-dollar views, are part of a hamlet called Old Field. Locals have another name for these moneyed lanes: the Renaissance Riviera.

That’s because the area’s wealthiest residents, scientists all, work for the quantitative hedge fund Renaissance Technologies, based in nearby East Setauket. They are the creators and overseers of the Medallion Fund—perhaps the world’s greatest moneymaking machine. Medallion is open only to Renaissance’s roughly 300 employees, about 90 of whom are Ph.D.s, as well as a select few individuals with deep-rooted connections to the firm.

The fabled fund, known for its intense secrecy, has produced about $55 billion in profit over the last 28 years, according to data compiled by Bloomberg, making it about $10 billion more profitable than funds run by billionaires Ray Dalio and George Soros. What’s more, it did so in a shorter time and with fewer assets under management. The fund almost never loses money. Its biggest drawdown in one five-year period was half a percent.

For outsiders, the mystery of mysteries is how Medallion has managed to pump out annualized returns of almost 80 percent a year, before fees. 

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Competitors have identified some likely reasons for the fund’s success, though. Renaissance’s computers are some of the world’s most powerful, for one. Its employees have more—and better—data. They’ve found more signals on which to base their predictions and have better models for allocating capital. They also pay close attention to the cost of trades and to how their own trading moves the markets.

Simons is already well-known: math genius, professor at MIT and Harvard, recipient of the Oswald Veblen Prize in Geometry, and co-creator of the Chern-Simons theory. He was also a code breaker for the Institute for Defense Analyses, where he worked finding messages amid the noise.

The goal of quant trading is similar: to build models that find signals hidden in the noise of the markets. Often they’re just whispers, yet they’ll help predict how the price of a stock or a bond or a barrel of oil might move. The problem is complex. Price movements depend on fundamentals and flows and the sometimes irrational behavior of people who are doing the buying and selling.

“Renaissance was started by a couple of mathematicians,” Brown said in a 2013 conference for computational linguists. “They had no idea how to program. They’re people who learned how to program by reading computer manuals, and that’s not a particularly good way of learning.” He and Mercer had learned how to build large systems—with many people working on them simultaneously—which was a skill set they used to Renaissance’s advantage. Not that their new field was without challenges. “It’s all noise in finance,” he said.

More IBM veterans joined them on Long Island, including Stephen and Vincent Della Pietra, the string-theorist twins; Lalit Bahl, who had created algorithms to recognize human speech; Mukund Padmanabhan, whose specialty was digital-signal processing; David Magerman, a programmer; and Glen Whitney, who wrote software as a summer intern. “The takeaway from IBM was that the whole is greater than the sum of its parts,” says Chan. “They all worked together.”