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February 17 - February 19, 2020
Bayes’ theorem of probability, which argues that, by updating one’s initial beliefs with new, objective information, one can arrive at improved understandings.
Laufer and Patterson began writing a computer program to track how far their trades strayed from the ideal state,
“I don’t know why planets orbit the sun,”
“That doesn’t mean I can’t predict them.”
cognitive biases,
Amos Tversky and Daniel Kahneman had explored how individuals make decisions, demonstrating how prone most are to act irrationally.
Among those identified: loss aversion, or how investors generally feel the pain from losses twice as much as the pleasure from gains; anchoring, the way judgment is skewed by an initial piece of information or experience; and the endowment effect, how investors assign excessive value to what they already own in their portfolios.
“Humans are most predictable in times of high stress—they act instinctively and panic. Our entire premise was that human actors will react the way humans did in the past . . . we learned to take advantage.”
“Visibility invites competition, and, with all due respect to the principles of free enterprise—the less the better.”
Laufer’s models, which measured the fund’s impact on the market with surprising precision, concluded that Medallion’s returns would wane if it managed much more money.
buying stocks that didn’t rise as much as expected based on the historic returns of these various underlying factors, while simultaneously selling short, or wagering against, shares that underperformed. If shares of Apple Computer and Starbucks each rose 10 percent amid a market rally, but Apple historically did much better than Starbucks during bullish periods, Kepler might buy Apple and short Starbucks.
There was too much noise in the market for Frey’s system to hear any of its signals.
“I liked smart people who were probably unhappy,”
Baum-Welch algorithm—codeveloped
adaptive, or capable of learning and adjusting on its own, much like Henry Laufer’s trading system for futures. If the model’s recommended trades weren’t executed, for whatever reason, it self-corrected, automatically searching for buy-or-sell orders to nudge the portfolio back where it needed to be, a way of solving the issue that had hamstrung Frey’s model. The system repeated on a loop several times an hour, conducting an optimization process that weighed thousands of potential trades before issuing electronic trade instructions. Rivals didn’t have self-improving models; Renaissance now had
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“We make money from the reactions people have to price moves.”
About 60 percent of investments that experienced big, sudden price rises or drops would snap back, at least partially, it turned out. Profits from these retracements
If a strategy wasn’t working, or when market volatility surged, Renaissance’s system tended to automatically reduce positions and risk.
Watching for patterns in how stocks traded following earnings announcements, and tracking corporate cash flows, research-and-development spending, share issuance, and other factors, also proved to be useful activities.
simple measure of how many times a company was mentioned in a news feed—no matter if the mentions were positive, negative, or even pure rumors.
portfolio’s risk by its Sharpe ratio,
basket options are linked to a group of shares.
Alexander Belopolsky, who had spent time at a unit of D. E. Shaw, the quant hedge fund.
Nick Simons Institute,
Most senior investors were on vacation, after all, so reading into the losses didn’t seem worthwhile.
More than ever, though, it was powered by complex equity trades featuring a mixture of complex signals, rather than simple pairs trades, such as buying Coke and selling Pepsi.
By analyzing and estimating hundreds of financial metrics, social media feeds, barometers of online traffic, and pretty much anything that can be quantified and tested, they uncovered new factors, some borderline impossible for most to appreciate.
“This interconnectedness is hard to model and predict with accuracy, and it changes over time. RenTec has built a machine to model this interconnectedness, track its behavior over time, and bet on when prices seem out of whack according to these models.”
If Medallion discovered a profitable signal, for example that the dollar rose 0.1 percent between nine a.m. and ten a.m., it wouldn’t buy when the clock struck nine, potentially signaling to others that a move happened each day at that time. Instead, it spread its buying out throughout the hour in unpredictable ways, to preserve its trading signal.
dreamed of tapping. The rage among investors is for alternative data, which
sales of farm equipment
satellite images of crop yields.
There are so many varieties of quant investing that it is impossible to generalize about the subject.
It’s not clear any expert or system can reliably predict individual stocks, at least over the long term, or even the direction of financial markets.
