Artificial market intelligence

Trading on the international futures markets has often been compared to the game of chess. There are so many inputs to consider in futures trading, and so many possible moves, it has even been likened to three-dimensional chess. As in chess, you are never actually trading the various inputs; you are actually trying to second-guess how other market participants–or your opponent–will react to those inputs.

As Keynes so aptly put it, “Successful investing is anticipating the anticipations of others.”

In his latest book, “21 Lessons for the 21st Century”, Yuval Noah Harari describes how artificial intelligence (AI) has transformed the world of chess. He writes,

“On 7 December 2017 a critical milestone was reached, not when a computer defeated a human at chess—that’s old news—but when Google’s AlphaZero program defeated the Stockfish 8 program. Stockfish 8 was the world’s computer chess champion for 2016. It had access to centuries of accumulated human experience in chess, as well as to decades of human experience. It was able to calculate 70 million chess positions per second. In contrast, AlphaZero performed only 80,000 such calculations per second, and its human creators never taught it any chess strategies—not even standard openings. Rather AlphaZero used the latest machine learning principles to self-learn chess by playing against itself. …

Can you guess how long it took AlphaZero to learn chess from scratch, prepare for the match against Stockfish, and develop its genius instincts? Four hours. That’s not a typo. For centuries chess was considered one of the crowning glories of human intelligence. AlphaZero went from utter ignorance to creative mastery in four hours, without the help of any human guide”.

Human chess players have sidestepped the problem (for them) of artificial intelligence by banning computers from human chess tournaments. Mr Harari writes,

“In human-only chess tournaments, judges are constantly on the lookout for players who try to cheat by secretly getting help from computers. One of the ways to catch cheats is to monitor the level of originality players display. If they play an exceptionally creative move, the judges will often suspect that this cannot possibly be a human move—it must be a computer move”.

As in chess, computers are now better than humans at trading futures. Fortunately—or unfortunately—futures markets cannot—or will not—ban computers from trading. This presents something of a problem for the physical trading houses, which have always relied on profits from trading futures to bolster/offset the tiny/negative margins that they make on trading physicals. As yet, the trade houses have failed to find a replacement for those missing profits.

But apart from the difficulties faced by the trading houses, what does it matter if computers now trade better than humans?

Futures markets have two roles to play: the first is to set a price (price discovery); the second is to provide a hedging medium. If computers are better at setting a price than humans, and if they provide lots of liquidity for physical hedging, then surely the world is better off.

As Mr Harari warns however, the difficulty arises when algorithms understand humans better than we understand ourselves. Once they do, computers can manipulate humans. This may already have happened in recent elections. If algorithms can nudge us into how to vote in elections, they can also nudge us into actions (such as selling at the bottom or buying at the top) in the futures markets.

Once futures market algorithms start to take money from physical hedgers, hedging becomes more expensive. When that happens, value is taken from producers and consumers of the physical commodity. Farmers are worse off, as too are consumers.

Some might argue that in any case trade houses always took value from the supply chain when they made profits from futures trading, already making farmers and consumers worse off. In that sense the owners of the algorithms have merely taken their place; the profits now go to the computers rather than the trade houses.

However, trade houses added value back into the process by efficiently moving physical commodities around the world. Apart from setting prices, it is hard to see what value algorithms return to the supply chain.

There is no obvious solution to this. Algorithms continue to get smarter while traditional physical trade houses continue to search for alternative business models. As Mr Harari writes,

“Already today, computers have made the financial system so complicated that few humans can understand it. As AI improves, we might soon reach a point where no human can make sense of it.”

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