Fintech: Search For A Super-Algo

The rise of the machine learning :

The quantitative investment world plays down the prospect of machines supplanting human fund managers, pointing out that the prospect of full artificial intelligence is still distant, and arguing that human ingenuity still plays a vital role. But the confident swagger of the money management nerds is unmistakable. Already there are quasi-AI trading strategies working their magic in financial markets, and the future belongs to them, they predict.

→ Financial Times

Why Do High-Speed Traders Cancel So Many Orders?

Of course, honest traders change their minds all the time and cancel orders as economic conditions change. That’s not illegal. To demonstrate spoofing, prosecutors or regulators must show the trader entered orders he never intended to execute. That’s a high burden of proof in any market. One helpful fact is if most of a trader’s (canceled) orders were on one side (say to buy) when he was mostly actually trading on the other (selling). For instance Sarao allegedly put in huge orders to sell, so that he could buy a few contracts: All his trading was on one side, but most of his orders were on the other. Then he’d switch a little while later. That seems like a bad sign.

→ Traders Magazine

Risky Strategy Sinks Small Hedge

Follow-up on some less successful investment strategies by hedge-funds, to say the least :

The back-tested results for the Spruce Alpha fund may not have taken into account how markets and investors would react given the kind of circumstances that took place in August. The hypothetical results could have underestimated the fact that some E.T.F.s are used as trading instruments that big money managers move quickly in and out of in times of extreme market volatility.

In a disclaimer to its marketing materials, Spruce Alpha also noted some of the unreliability of back-tested returns. The hypothetical results “do not represent the results of actual trading” and “were achieved by means of the retroactive application of a hypothetical model that was designed with the benefit of hindsight and could be adjusted at will until desired or better performance results were achieved,” the disclaimer reads.

→ The New York Times

Markets : Can They Really Be Tamed ?

On computer-driven, automatic trading strategies :

Cobras are revered in Indian culture, but the British Raj took a dimmer view of the poisonous snake. Officials promised a lucrative reward for every dead serpent — a scheme that, according to economic lore, backfired horribly.

Enterprising Indians began breeding cobras to collect the bounty, which forced the colonial government to abandon the plan. The frustrated breeders then released the worthless cobras, worsening the infestation. The story has never been fully confirmed by historians, but was seized on by German economist Horst Siebert, who in 2001 published The Cobra Effect on perverse incentives and unintended consequences. The book turned the anecdote into a potent example of how solutions to a problem can make it worse.

→ The Financial Times

Black Box Trading : Why They All “Blow-Up”

While in Greenwich Ct. one afternoon I will never forget a conversation I had with a leading quantitative portfolio manager. He said to me that despite its obvious attributes “Black Box” trading was very tricky. The algorithms may work for a while [even a very long while] and then, inexplicably, they’ll just completely “BLOW-UP”. To him the most important component to quantitative trading was not the creation of a good model. To him, amazingly, that was a challenge but not especially difficult. The real challenge, for him, was to “sniff out” the degrading model prior to its inevitable “BLOW-UP”. And I quote his humble, resolute observation “because, you know, eventually they ALL blow-up“…as most did in August 2007.

→ Global Slant

Mastering the Machine

“In any given market, Bridgewater may have a dozen or more different indicators. However, even when most or all of the indicators are pointing in a certain direction, Dalio doesn’t rely solely on software. Unless he and Jensen and Prince agree that a certain trade makes sense, the firm doesn’t make it. While this inevitably introduces an element of human judgment to the investment process, Dalio insists it is still driven by the rules-based framework he has built up over thirty years. “When I’m thinking, ‘What is going on today?,’ I also need to make the connection to ‘How does what is happening today fit into our framework for making this decision?’ ’’ he said. Ultimately, he says, it is the commitment to systematic analysis and systematic investment that distinguishes Bridgewater from other hedge funds. “I hear a lot of people describing what’s happening today without the proper historical context and without the framework of how the machine works,” he says.”

The New York Magazine on the culture of Bridgewater :

The path to Principles began early in Bridgewater’s history, when Dalio began to think that employees, like economies, could be understood as following patterns. Transcendental Meditation informed his belief that a person’s main obstacle to improvement was his own fragile ego; at his firm, he would make constant, unvarnished criticism the norm, until critiques weren’t taken personally and no one held back a good idea for fear of being wrong. Dalio’s chosen investment system depended on such behavior. Unlike at a hedge fund such as Steven Cohen’s SAC Capital, where star traders are given chunks of the firm’s capital to run quasi-independent desks (and offset each other’s losses), everyone at Bridgewater essentially contributes to the same strategy as they work under Dalio and his longtime confidants and co-CIOs Bob Prince and Greg Jensen. Dalio thought radical transparency could optimize the hive mind. “The culture makes you have to listen to other people,” says Giselle Wagner, a former Bridgewater chief operating officer.

→ The New Yorker