AI总结
- 核心结论：宏观交易不仅在于预判市场走势，更在于对头寸规模（仓位）的精准控制。即便是拥有“预知未来”优势的操盘手或AI，也常因过度冒险导致亏损或爆仓。
- 结构逻辑：
- 引入：以英国脱欧市场反应为例，说明已知信息不等于确定的投资收益。
- 实验设计：Elm Wealth开展的模拟交易实验，参与者可提前获取次日新闻进行交易。
- 结果呈现：多数参与者（含人类学生和AI模型）表现平平，部分甚至破产。
- 核心分析：交易失败的根源在于“仓位管理能力”匮乏，而非方向判断失误。
- 总结：专家与普通人的本质区别在于，前者能根据信心程度动态调整仓位，而非机械加杠杆。
- 关键信息：
- 实验机构：Elm Wealth（Jerry Bell, Victor Haghani, James White）。
- 关键数据：职业交易员平均收益率达130%；AI模型预测方向准确率约60%，但因杠杆过高（7-12倍）承担了灾难性风险。
- 核心挑战：即便掌握确定信息，如何决定“买多少”比“买什么”更难。
- 问题与回答：
- 问题：为何拥有信息优势的宏观交易依然困难？
- 回答：因为交易者往往无法在风险与回报之间取得平衡，习惯性采取过高杠杆，导致在市场波动中被清盘。
正文
More difficult than knowing what to buy is how much
Illustration: Satoshi Kambayashi
How much would you have paid ten years ago, as votes were counted for Britain’s Brexit referendum, to glimpse the next morning’s headlines and trade ahead of them? If you were betting on the pound, it would have helped a lot. The night of the poll £1 bought $1.50; a fortnight later, less than $1.30. But if you were trading stocks, a forewarning might have done more harm than good. Britain’s domestically focused FTSE 250 share index dropped at first, but only for two trading days. Then it began a bull market that lasted for a couple of years. Even the most fervent Brexiteer might not have predicted that.
Macro trading, meaning betting on how asset prices will move in response to political and economic trends, is enticing and glamorous. It is also hard, and a new study by Jerry Bell, Victor Haghani and James White of Elm Wealth, an investment firm, shows just how hard. They have designed a simulation in which both humans and leading artificial-intelligence models get access to the next day’s news in advance, and can place their bets before it breaks. In other words, they get to trade ahead of the rest of the market. Yet even with this advantage, it turns out to be difficult—for man and machine alike—to avoid ruin and turn a profit.
Mr Bell and his colleagues are updating an experiment they first ran in 2023. Then, they recruited 118 volunteers, most of whom were studying graduate-level finance at leading universities. Each was given $50 and a chance to grow it by placing bets on America’s S & P 500 share index and 30-year Treasury bonds.
They could do this once per asset per day, at the market close before each of 15 trading days, selected by the authors from between 2008 and 2022. Before trading, participants were shown the front page of the Wall Street Journal pertaining to the following day—so at Monday’s close they were shown Wednesday’s front page (with any actual price moves on Tuesday redacted). They could go long or short each asset and could leverage their bets by up to 50 times. This would translate a 2% price move, for example, to a double-or-nothing wager. Each trade was terminated at the following close.
Most participants did not make themselves proud. Roughly half lost money and one in six went bust; the average finishing pot was just $51.62, or a gain of 3.2%. Since then Elm has hosted a similar game on its website, with an imaginary starting stake of $1m. The 60,000-odd people who have played it have fared “substantially worse” than the original, paid cohort.
Perhaps the greater surprise is that, in the experiment’s latest iteration, several of the leading AI models did not truly excel, either. The Elm team gave each of Chat GPT, Claude, Gemini and Grok ten runs at the game, also starting with an imaginary $1m. They were told to play as a middle-aged American investor managing 100% of their financial wealth.
Only Chat GPT and Claude made money, with average finishing pots of $1.5m and $2.6m respectively. Grok’s was $970,000 and Gemini’s just $490,000. So what makes macro trading so hard, even for those with a crystal ball?
The biggest lesson is that both humans and AI are bad at sizing bets. The authors note that even Chat GPT and Claude were lucky as well as skilful in this respect. None of the models correctly predicted the direction of stocks and bonds more than about 60% of the time, yet their average leverage applied to their bets ranged between seven and 12 times. Given that American share prices moved by more than 5% on 23 days since 2000, and by more than 9% on seven days, the models were therefore running “too much risk of a catastrophic loss of capital”, including the possibility of a complete wipe-out.
The humans were even worse. In the original experiment, players in aggregate bet no more heavily when the news made price moves easy to predict. And like the AI models, they took too much risk overall. On 30% of days they used leverage above 20 times, which could easily have sent them bust.
Some humans, of course, are much better. The Elm team also invited five expert macro traders to play their original game. All five professionals finished in the black, with an average return of 130%. They did a bit better than AI at predicting directions (a hit rate of 63%). But, crucially, they varied their position sizes a lot, betting more when they felt confident and not at all when they did not. Even with excellent foresight, knowing which assets to buy is tough. Deciding how much, it seems, is tougher. ■
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