【原版杂志阅读】25期 Day 29：Lessons from finance’s experience with artificial intelligence part 3

16665

Finance & economics | A techy tug-of-war

Lessons from finance’s experience with artificial intelligence

Humans can take on the machines 

When markets reversed in 2022, many of these trends flipped. Retail’s share of trading fell back as losses piled up. The quants came back with a vengeance. AQR’s longest-running fund returned a whopping 44%, even as markets shed 20%.

This zigzag, and robots’ growing role, holds lessons for other industries. The first is that humans can react in unexpected ways to new technology. The falling cost of trade execution seemed to empower investing machines—until costs went to zero, at which point it fuelled a retail renaissance. Even if retail’s share of trading is not at its peak, it remains elevated compared with before 2019. Retail trades now make up a third of trading volumes in stocks (excluding marketmakers). Their dominance of stock options, a type of derivative bet on shares, is even greater.

The second is that not all technologies make markets more efficient. One of the explanations for aqr’s period of underperformance, argues Cliff Asness, the firm’s co-founder, is how extreme valuations became and how long a “bubble in everything” persisted. In part this might be the result of overexuberance among retail investors. “Getting information and getting it quickly does not mean processing it well,” reckons Mr Asness. “I tend to think things like social media make the market less, not more, efficient...People don’t hear counter-opinions, they hear their own, and in politics that can lead to some dangerous craziness and in markets that can lead to some really weird price action.”

The third is that robots take time to find their place. Machine-learning funds have been around for a while and appear to outperform human competitors, at least a little. But they have not amassed vast assets, in part because they are a hard sell. After all, few people understand the risks involved. Those who have devoted their careers to machine learning are acutely aware of this. In order to build confidence, “we have invested a lot more in explaining to clients why we think the machine-learning strategies are doing what they are doing,” reports Greg Bond of Man Numeric, Man Group’s quantitative arm.

There was a time when everyone thought the quants had figured it out. That is not the perception today. When it comes to the stockmarket, at least, automation has not been the winner-takes-all event that many fear elsewhere. It is more like a tug-of-war between humans and machines. And though the machines are winning, humans have not let go just yet. 

一、词汇部分

1. reverse /rɪˈvɜːs/ v. 使 (决定、政策、趋势) 转向; 逆转

前缀：re- 往回(return)

词根：vers-转动

2.  derivative/dɪˈrɪvətɪv/ n. （金融）衍生工具（产品）

前缀：de-加强语气，向下（低）

词根：riv- river

后缀: - ative 名词后缀

3.  perception /pəˈsɛpʃən/ n 理解; 看法

前缀：per- 从头到位，从始至终，穿过，透过（perfect)

后缀：-cept- 拿(except)_

后缀：-ion 名词后缀

4. vengeance  /ˈvɛndʒəns/ n. 复仇

词根：venge- 复仇（玩哥）

后缀：-ance名词后缀

5. exuberance  /ɪɡˈzjuːbərəns/ n.快乐有活力的行为

联想：ex + uber + ance 

二、文章讲解

1. When markets reversed in 2022, many of these trends flipped. Retail’s share of trading fell back as losses piled up. The quants came back with a vengeance. AQR’s longest-running fund returned a whopping 44%, even as markets shed 20%.

参考翻译： 市场在2022年发生了逆转，这些趋势中有很多也随之反转。随着亏损不断增加，散户的交易占比回落。量化基金强势回归。就在市场跌去20%之际，AQR运营时间最长的基金回报率却高达44%。 

2. This zigzag, and robots’ growing role, holds lessons for other industries. The first is that humans can react in unexpected ways to new technology. The falling cost of trade execution seemed to empower investing machines—until costs went to zero, at which point it fuelled a retail renaissance. Even if retail’s share of trading is not at its peak, it remains elevated compared with before 2019. Retail trades now make up a third of trading volumes in stocks (excluding marketmakers). Their dominance of stock options, a type of derivative bet on shares, is even greater.

参考翻译： 这样的起起落落，以及机器人日益重要的作用，为其他行业提供了借鉴。首先，人类面对新技术可能会做出意想不到的反应。交易执行成本的下降似乎让机器投资大行其道——直到有一天交易成本降为零，又推动散户东山再起。即使目前散户的交易占比不在峰值，但仍高于2019年前。如今散户交易占到股票交易量的三分之一（不包括做市商）。它们在股票期权（一种押注股票的衍生品）中的占比甚至更大。

3. The second is that not all technologies make markets more efficient. One of the explanations for AQR’s period of underperformance, argues Cliff Asness, the firm’s co-founder, is how extreme valuations became and how long a “bubble in everything” persisted. In part this might be the result of overexuberance among retail investors. “Getting information and getting it quickly does not mean processing it well,” reckons Mr Asness. “I tend to think things like social media make the market less, not more, efficient...People don’t hear counter-opinions, they hear their own, and in politics that can lead to some dangerous craziness and in markets that can lead to some really weird price action.”

参考翻译：其次，并非所有技术都会让市场变得更高效。AQR的联合创始人克利夫·阿斯内斯（Cliff Asness）认为，AQR一度表现不佳的原因之一是估值变得非常极端以及“到处都是泡沫”的情况长期持续。某种程度上这可能也是散户投资者过度狂热造成的。“能获取信息并且获取速度很快并不代表就能处理好信息，”阿斯内斯认为，“我倒是认为，社交媒体之类的东西会降低而不是提高市场效率……人们不去听反对意见，他们只听和自己一致的意见。这在政治上可能导致一些危险的疯狂之举，而在市场上可能导致一些非常不可思议的价格行为。” 

4. The third is that robots take time to find their place. Machine-learning funds have been around for a while and appear to outperform human competitors, at least a little. But they have not amassed vast assets, in part because they are a hard sell. After all, few people understand the risks involved. Those who have devoted their careers to machine learning are acutely aware of this. In order to build confidence, “we have invested a lot more in explaining to clients why we think the machine-learning strategies are doing what they are doing,” reports Greg Bond of Man Numeric, Man Group’s quantitative arm.

参考翻译：第三，机器人需要时间找准自己的位置。使用机器学习的基金已经存在有一段时间了，其表现似乎超过了（至少略微超过了）人类操盘的基金。但它们并没有积聚起大量资产，部分原因是销路很难打开。毕竟少有人理解其中的风险。那些致力于机器学习技术的从业人员太清楚这一点了。为了建立客户信心，“我们已经显著加大投入，向客户解释为什么我们认为这些机器学习策略行之有效。”英仕曼集团旗下量化投资部门Man Numeric的格雷格·邦德（Greg Bond）表示。

5. There was a time when everyone thought the quants had figured it out. That is not the perception today. When it comes to the stockmarket, at least, automation has not been the winner-takes-all event that many fear elsewhere. It is more like a tug-of-war between humans and machines. And though the machines are winning, humans have not let go just yet. 

参考翻译：有那么一段时间，所有人都认为量化投资机构已经解决了这个问题。但今天人们不这么看了。至少在股市，自动化还不像在其他领域里那样，形成令许多人忧惧的赢家通吃的局面。目前更像是人类和机器在拔河。虽然绳子向着机器那边移，但人类还没有放手。

三、段落翻译

市场在2022年发生了逆转，这些趋势中有很多也随之反转。随着亏损不断增加，散户的交易占比回落。量化基金强势回归。就在市场跌去20%之际，AQR运营时间最长的基金回报率却高达44%。 

这样的起起落落，以及机器人日益重要的作用，为其他行业提供了借鉴。首先，人类面对新技术可能会做出意想不到的反应。交易执行成本的下降似乎让机器投资大行其道——直到有一天交易成本降为零，又推动散户东山再起。即使目前散户的交易占比不在峰值，但仍高于2019年前。如今散户交易占到股票交易量的三分之一（不包括做市商）。它们在股票期权（一种押注股票的衍生品）中的占比甚至更大。

其次，并非所有技术都会让市场变得更高效。AQR的联合创始人克利夫·阿斯内斯（Cliff Asness）认为，AQR一度表现不佳的原因之一是估值变得非常极端以及“到处都是泡沫”的情况长期持续。某种程度上这可能也是散户投资者过度狂热造成的。“能获取信息并且获取速度很快并不代表就能处理好信息，”阿斯内斯认为，“我倒是认为，社交媒体之类的东西会降低而不是提高市场效率……人们不去听反对意见，他们只听和自己一致的意见。这在政治上可能导致一些危险的疯狂之举，而在市场上可能导致一些非常不可思议的价格行为。” 

第三，机器人需要时间找准自己的位置。使用机器学习的基金已经存在有一段时间了，其表现似乎超过了（至少略微超过了）人类操盘的基金。但它们并没有积聚起大量资产，部分原因是销路很难打开。毕竟少有人理解其中的风险。那些致力于机器学习技术的从业人员太清楚这一点了。为了建立客户信心，“我们已经显著加大投入，向客户解释为什么我们认为这些机器学习策略行之有效。”英仕曼集团旗下量化投资部门Man Numeric的格雷格·邦德（Greg Bond）表示。

有那么一段时间，所有人都认为量化投资机构已经解决了这个问题。但今天人们不这么看了。至少在股市，自动化还不像在其他领域里那样，形成令许多人忧惧的赢家通吃的局面。目前更像是人类和机器在拔河。虽然绳子向着机器那边移，但人类还没有放手。