The Man Who Solved the Market

#statistical-section

The Man Who Solved the Market

Metadata

Highlights

Instead, they proposed searching for a small number of “macroscopic variables” capable of predicting the market’s short-term behavior. They posited that the market had as many as eight underlying “states”—such as “high variance,” when stocks experienced larger-than-average moves, and “good,” when shares generally rose. — location: 767 ^ref-53852

Their model foreshadowed revolutions in finance—including factor investing, the use of models based on unobservable states, and other forms of quantitative investing— — location: 792 ^ref-34172

Esquenazi — location: 918 ^ref-16088

The Baum-Welch algorithm provided a way to estimate probabilities and parameters within these complex sequences with little more information than the output of the processes. — location: 1019 ^ref-33816

Truth … is much too complicated to allow for anything but approximations. John von Neumann — location: 1304 ^ref-55192

Ax concluded it was time to bring in someone with experience developing stochastic equations, the broader family of equations to which Markov chains belong. — location: 1512 ^ref-33145

Though it clearly had elements of randomness, much like the weather, mathematicians like Simons and Ax would argue that a probability distribution could capture futures prices as well as any other stochastic process. — location: 1524 ^ref-39432

“The name of the game is not to always be right, but to be right often enough.” — location: 1530 ^ref-50755

Buying investments as they became more expensive and selling them as they fell in value was at odds with leading academic theory, which recommended buying when prices cheapened and taking money off the table when prices richened. — location: 1720 ^ref-19921

An order to sell a chunk of Coke shares, for instance, might send that stock down a percentage point or even two, even as Pepsi barely moved. Once the effect of their Coke stock selling wore off, the spread between the shares reverted to the norm, which made sense, since there had been no reason for Coke’s drop other than Morgan Stanley’s activity. Bamberger sensed opportunity. If the bank created a database tracking the historic prices of various paired stocks, it could profit simply by betting on the return of these price-spreads to their historic levels after block trades or other unusual activity. Bamberger’s bosses were swayed, setting him up with half a million dollars and a small staff. Bamberger began developing computer programs to take advantage of “temporary blips” of paired shares. — location: 2182 ^ref-6413

“Humans are most predictable in times of high stress—they act instinctively and panic. — location: 2500 ^ref-20130

which each step along the way is random, yet dependent on the previous step—a hidden Markov model. A speech-recognition system’s job was to take a set of observed sounds, crunch the probabilities, and make the best possible guess about the “hidden” sequences of words that could have generated those sounds. To do that, the IBM researchers employed the Baum-Welch algorithm— — location: 2798 ^ref-60926

Never send a human to do a machine’s job. Agent Smith in the film The Matrix — location: 4660 ^ref-44237

Links to this page:

  1. Statistical Section
Built with LogoFlowershow