前Two Sigma量化分析师：你应该反向押注乐观的分析师

这就是所有买方阿尔法捕获项目的核心概念。

And that's really the notion of all these buy side alpha capture programs.

前提是你合作的人足够优秀。

The assumption is you're working with people who are good enough.

然后问题就变成了，你现在有机会捕捉细微差别。

And then the question is now you have an opportunity to capture nuance.

而细微差别才是真正强大的部分，因为边际信息正是从这里来的。

And nuance is the part that's actually really powerful because that is the part that really where marginal information comes in.

记得我们说过，变化比绝对水平更重要。

Remember, we talked about this concept of the change is more important than the actual level.

所以如果你在捕捉信息的变化，你实际上是在进入人们所说的公司信息传播流。

So if you're capturing changes in the information, what you really are entering into something people call the propagation flow of a company.

想想投资者。

Think about investors.

投资者总是在投资。

Investors are always investing.

他们每天都在做交易，对吧？

They're making trades every day, right?

他们买进，卖出。

They're buying, they're selling.

而且，基本上有时候新信息进入市场时，并不是同时到达所有人的，对吧？

And there's a, there is essentially sometimes when new information comes in into the marketplace, it doesn't come in all at the same time, right?

不是所有信息都在同一时间出现，然后每个人都在下午5点52分就知道了所有信息。

Not everything comes in and everybody knows all the information right then on, you know, 05:52PM.

每个人都知道一切。

Everybody knows everything.

不是的。

No.

实际情况是，信息来了，有人先知道了。

What happens is information comes in, someone learns about it.

有人形成观点，进行交易，下一个人知道了，形成观点，然后交易。

Someone formulates an opinion, someone trades, the next person learns about it, formulates an opinion and trades.

当然，没错。

Now, sure.

在财报季，信息会大量释放，但在财报之间的时间段里，你会看到很多有趣的时期，实际上会有某种信息释放、一定量的分析，然后是传播，也就是说股票开始波动，而且不是一下子全部波动。

During earnings, that's a big release of information, but intra earnings during intra periods, you're going to see a lot of interesting periods where you actually have information release of some kind, some amount of analysis, then a propagation, meaning the stock starts to move and it doesn't move all at once.

它开始一天一天地小幅波动。

It starts to move a little bit day by day.

这是因为人们正在消化这些信息。

And that's because people are assimilating this information.

他们在对Bavvy进行投资。

They're making investments on Bavvy.

顺便说一下，有时候他们会花好几天来建仓。

Sometimes by the way, they're putting on positions across multiple days.

所以他们不会一次性全部买入或全部卖出。

So they're not buying it all at once or selling it all once.

他们会一点一点地买，一点一点地买，再一点一点地买，然后反过来一点一点地卖。

They'll buy a little bit and buy a little bit and buy a little bit and buy a little bit and do the opposite sell.

所以，如果你理解了这些细微差别，你就可以构建一个投资策略，基本上能顺应股价的传播曲线，随着公司因信息公布而上涨或下跌，你就能抓住机会。

And so when you have that approach, if you understand all those nuances, you can build an investment strategy that basically fits into the propagation curve as the company starts going up in price because information becomes known or down because information becomes known negatively.

你就能捕捉到与这种传播相关的超额收益。

You're able to capture the alpha associated with that propagation.

而这最终成为数学上非常非常有趣的部分。

And that ultimately becomes the part that is very, very interesting mathematically.

你实际上可以证明这在市场中确实会发生，但这就是很多人在玩超额收益捕捉游戏时的玩法。

You you can actually prove it They actually happens in the marketplace, but that's the game that a lot of people are playing in alpha capture.

他们明白自己不会是第一个进场的人。

They recognize that they're not going to be the first in.

他们也不想成为最后一个，但他们希望至少能处于传播曲线的前四分之一，比如第一个四分位，因为这样你就能捕捉到足够的超额收益，让这个策略持续盈利。

They also don't want to be the last in, but what they want to be is at least in the top quartile, let's say the first quartile of the propagation curve, because then you're able to capture enough alpha to make it a profitable strategy to continue.

在这些变化中，有多少是真正的超额收益？

In those changes, how much is truly alpha?

因为我想可能有一些因素，比如其中可能有一些动量效应，我知道这可能不是一个好例子，但像财报或重大新闻事件，我猜动量会是一个驱动因素，因为可能整个市场都会以类似的方式移动。

Because I imagine there are some factors, maybe there's some momentum in there, I know this is probably a bad example, but an event like earnings or something big happening in the news, I imagine a lot of momentum, I imagine momentum is a driver in those things because maybe the whole market will move in a similar way.

对，我就是好奇你怎么看这个问题。

Yeah, just curious how you think about that.

我觉得任何变化，就像你说的，任何信息的发布以及相关的超额收益，都取决于市场对这个信息的惊讶程度。

The way I think about any kind of change, as you discussed, any information release and the alpha associated with it, has to do with how much surprise there is to the market information.

惊讶程度通常和你能获得的超额收益成正比。

The amount of surprise is usually proportional to the amount of alpha you're going to get.

动量以及你提到的所有因素，不管是贝塔，还是增长转向价值，还有轮动，这些都会发生。

Momentum and all factors that you're talking about, whether it's a beta, know, and you know, growth is moving in value and there's rotation.

当然。

Sure.

这些都会发生。

That's going to happen.

之所以会发生，是因为现在市场上超过60%的资金流都是被动投资。

And that's going to happen because 60% plus of all the flows in the market today are passive.

ETF，你知道的，就是那些大型被动投资工具，还有养老基金、主权财富基金等等。

ETFs, you know, I mean, passive, big, big vehicles and pension funds and sovereign wealth funds and all that.

他们都在做被动投资。

They're doing passive investing.

他们没时间逐一分析每只股票，对吧？

They don't have the time to analyze every single stock, right?

他们只是投资于动量、质量、成长或者你提到的某个因子。

They're just investing in momentum or quality or growth or some aspect of a factor that you're talking about.

这相当于同时买入数千家公司，然后每天一点点地推高它们。

And that's buying thousands of companies at the same time and just pushing them along a little by little every day.

这就是你所说的那种让股票大幅波动的东西。

That is the thing that you're talking about that are sort of moving the stock a lot.

不过，你想想财报发布时会发生什么，对吧？

However, if you think about what happens during an earnings release, right?

尤其是当财报结果让市场其他人大吃一惊的时候。

And especially if the earnings release is a big surprise to the rest of the street.

再说特斯拉的产量数据，大家本来预期也就那样。

Again, Tesla's production numbers were expected to like be, you know, whatever.

我，某个数字，你知道的，50万，结果他们公布的是60多万，比预期大得多，市场根本没准备好。

I, some number, you know, 500,000 and it turns out that they came in 600,000 something surprisingly larger and the street was not ready for it.

你会看到股价上涨，那就是阿尔法，对吧？

You're going to see a pop in the stock and that's going to be alpha, right?

这不是传统的、你提到的那些风险，也就是人们通常说的因子溢价所能捕捉到的。

That's not going to be captured by the traditional, all the risks that people you're talking about, the, what people typically refer to as premium factor premium.

好的。

Okay.

那就是阿尔法。

That's alpha.

另外，有时候每天也能看到一点点阿尔法，就是当有边缘信息出来，人们开始模拟和交易的时候。

Also there's sometimes little bits of alpha that you can see every day when again, there's a marginal piece of information that comes out and people are starting to simulate and trading it.

而市场其他部分还没有真正跟上。

While the rest of the market isn't truly like caught up on it yet.

所以你会看到在信息发布之间，时不时有一些小的阿尔法出现。

So you'll see little pieces of alpha come across, in, in, you know, in between information releases.

但伊桑，最重要的事情可能是要明白，这真的取决于投资者感到的意外程度。

But Ethan, the most important probably thing to think about is it really comes down to the level of surprise that the investors have.

投资者做出决定，然后说，哇，现在情况比我预想的要好得多，或者糟糕得多。

And the investors make a decision and say, oh, wow, it's now going to be much better than I thought, or much worse than I thought.

我必须改变我对这项投资的看法。

I have to change the way I think about the investment.

这就会在阿尔法上产生大的波动。

And that creates a big move in alpha.

有哪些方法可以量化这种意外？

What are some of the ways you can quantify surprise?

通常，意外是通过某种共识指标来量化的。

I mean, surprise typically is quantified using some kind of a consensus metric.

想想看，市场对公司的盈利、销售以及公司将要报告的任何数字都有共识指标。

So think about, the street has consensus metrics on earnings, on sales, on any kind of numbers that the company is going to report.

你可能熟悉FactSet，对吧？

Think about you're probably familiar with, FactSet, right?

你听说过这个词，你知道，FactSet 有一个共识预期。

You've heard of the word, you know, FactSet has a consensus.

你知道，彭博也有共识预期，所有主要的提供商，那些大型分析公司，他们做的事情就是查询整个卖方，也就是所有分析师，所有大银行，然后问他们：告诉我你对盈利的看法，或者你认为销售额会是多少。

You know, Bloomberg has consensus, all the major, providers out there, these big analytics companies, what they do is they query the entire sell side, you know, base of analysts, all the big banks, and they ask them, tell me your opinion on the, on earnings or what you think sales are going to be.

而且，这不仅仅是，再说一遍，这不仅仅是打个电话问问什么的。

And there's, it's not just, again, it's not just a, a phone query or something.

我的意思是，现在所有这些都已经结构化，都在模型里，而且都是系统化的，但数据基本上会进入数据库系统，然后你最终得到一个共识指标。

I mean, all this is now structured and these are in models and they're all systematic, but the data basically goes into the database systems and then you end up getting a consensus metric.

所以它告诉你：这是我们的预期。

So it tells you here's what we expect.

今天早些时候，美光发布了财报，对吧？

Earlier today, Micron reported, right?

我觉得他们的营收超出了预期一些。

I think they beat, revenues by a handful.

我记得可能超出幅度超过10%，营收甚至超出12%。

Like I think it was probably over well over 10%, 12% beat on even revenues.

嗯，当时确实有一个共识指标。

Well, there was a consensus metrics.

分析师们预期美光会产生一定数量的收入，结果他们实际收入比预期还要高。

Analysts expected Micron to generate a certain amount of revenues and then they came in even higher than what they expected.

这个惊喜就是美光实际收入和共识指标之间的差距。

That surprise is the delta between what Micron came in and what the consensus metric was.

你在Two Sigma运行或构建这个项目的时候，会不会特别看重某些专家或某些人的意见，比其他人权重更高？

When you were running the program or building out the program at Two Sigma, would you weight certain people's, certain experts' opinions much more heavily than others?

这也是你尝试过的一个方面吗？

Was that another thing you played around with?

这是每个人进入alpha capture时都会有的想法，他们相信自己能找出最优秀的人并给予高权重，同时找出那些表现较差的，对吧？

It's the one thing everyone comes into alpha capture with the idea that they believe they're going to figure out how to find the best people and weight them heavily and find the, you know, the lesser, right?

然后给这些人较低的权重。

The laggards and weight them less.

但这种方法有个问题，而且这真的是一个大家没意识到的实际问题，那就是要在很长一段时间内持续表现出色是非常困难的。

The problem with that approach, and this is, you know, this is again, a real issue that people don't realize is that it is very tough to be consistently good across a long period of time.

很多时候，你会遇到手气正旺的情况，但随后会发生一种叫做均值回归的现象。

Oftentimes, you end up having a hot hand, something happens to you called mean reversion.

你可能听说过均值回归。

You probably heard mean reversion.

均值回归就是当事情变得太好了，然后就会回落。

Mean reversion is when things got too good and now they're going to revert back.

所以你得小心。

So you have to be careful.

你想押注在手气正旺的人身上，觉得应该多给他们一些权重。

You want to bet on someone with a hot hand and you think you want to wait them more.

但当你刚给他们增加权重，他们很可能就会回归，就像我之前提到的看涨看跌的例子一样。

The moment that you wait them more, they're most likely going to revert very similarly to the whole bullish and bearish example I had earlier.

所以，你通常要找的其实是他们底层观点中的动量。

So the thing that you usually often look for is an actually is essentially is a momentum in their underlying view.

如果他们早早发现了一家表现很好或很差的公司，并且是第一批发现的人，那么他们的想法就有了一些动量，你或许可以跟着他们一段时间，直到市场其他人跟上。

So if they've caught on into a company that's doing really well or doing really poorly, and they're able to be one of the first to catch on, and now they have some momentum in their ideas, You may be able to follow them for a little period of time while the rest of the street catches up.

所以他们现在领先一步，正在引导大家，所有人都跟着他们走。

So they're now ahead of the curve and now they're guiding everyone and everybody's moving to them.

而且他们仍然是比较大胆的那个。

And they're still kind of the bold one.

这时候你就想跟着他们押注一段时间。

And that's when you want to kind of make a bet behind them for a little while.

一旦所有人都跟上了他们的观点，你再给他们加权重就没有意义了。

Once everybody catches up to their opinion, you you you wanna you you have no more use to weight them more than others.

这很大程度上是一种视情况而定的权重。

It's very much a situational weight.

看来在运行和构建alpha捕获程序的过程中，你学到了很多关于人们在市场中的行为方式。

It seems in running the alpha capture program and building it out, you've learned a lot about the way people behave in markets.

我想问，你学到的最重要的基本见解是什么？

I guess what are the most important fundamental insights you've learned?

我不知道能不能接着问这个问题，但你肯定是在利用这些见解进行交易。

And I don't know if I can ask this question on the back of that, but surely you trade on them.

我的意思是，如果你能理解所有这些情况，我猜那些超额收益其实就散落在各处。

I mean, if you if you if you understand all these things, I imagine it just you know, there's alpha lying around there.

超额收益到处都是。

There's alpha lying everywhere.

现实情况是，想要系统性地捕捉到这些机会，需要纪律性，也需要资金，对吧？

The reality is is to capture it in a systematic manner requires discipline, requires capital, right?

还需要你有能力、有决心去专注于这项特定的技能。

Requires the ability for you to, you know, be dedicated to that particular craft.

所以我是建议大家在主业之外，试着做点投资。

So doing it on the side, I tell people trying to be an investor on the side.

说实话，很多操作其实就跟赌博差不多。

You know, a lot of it's going to be just gambling, honestly.

很大程度上也靠运气。但如果你把这当成职业，每天醒来就是为了解决这个问题，你的工作本质上就是创造收益，并且你已经摸索出了一套靠谱的系统化策略来实现这一点。

A lot of it's going to be just sort of luck, but if this is your profession, if this is what you do waking up every day and your job is to basically generate returns and you figured out how to put together a good systematic strategy to do so.

那这就很棒了。

Okay, great.

那就更好了。

More power to you.

你应该能够每天系统地创造超额收益，希望在你的余生中都能如此。

That's you should be able to create systematic, generate systematic alpha every day, you know, hopefully for the rest of your life.

但你必须不断学习，不断创新，还要记住门槛在不断提高，对吧？

But you have to keep learning, you have to keep innovating and you have to keep remembering that the bar moves, right?

更多的信息会被公开。

More information becomes known.

环境在变，入场门槛也在变，对吧？

The context changes, the table stakes change, right?

上周或去年的入场门槛，和明年的门槛会不一样。

The table stakes of last week or last year are going to be different than the table stakes of next year.

如果你不明白市场中的门槛在不断提高，人们因为数据分析、人工智能等变得越来越聪明，你就会被甩在后面。

And if you don't understand that the stakes continuously move in the market and people become increasingly more sophisticated because of data analytics, AI, whatever it is, you're going be left behind.

而且你真的不应该停下来。

And then you really shouldn't stop.

到那个时候，你就应该停止投资了。

You should stop investing at that point in time.

这就是我跟别人说的。

And that's what I tell people.

我说，如果你觉得自己已经功成名就，创造了一些了不起的东西，现在可以坐享其成了。

I say, if you feel like you've now rested on your laurels and you've created something amazing, and now you could just sit back.

不幸的是，那可能就是你该退休的时候了。

That's unfortunately the time that you probably should just retire.

希望你已经赚够了，可以退休了，但就是这个意思，对吧？

And hopefully you've done enough well enough to retire, but that's the idea, right?

市场上有很多阿尔法机会，但现实是，你必须非常自律才能抓住它们。

There's a lot of alpha lying around, but the reality is you have to be very disciplined to collect it.

生成式AI改变了阿尔法捕获吗？

Has generative AI changed alpha capture?

我问这个问题是因为现在我们有几乎无限数量的模拟分析师。

And I asked that question with the context of now we have essentially an unlimited number of simulated analysts.

它的运作方式还是一样的吗？

Does it work in the same way?

这个问题问得非常好。

That's a very, very cool question.

我喜欢这个问题。

I like it.

我真的很喜欢。

I really like it.

现在理论上你可以有一个数字分析师，对吧？

The idea that you can now have theoretically a digital analyst, right?

基本上，你知道，一个智能到足以担任分析师的智能体，能够做出分析师能做的某些决策和判断，考虑到他们看的是什么，以及他们能接触到的数据，无论是新闻、文件、会议记录，还是因子数据等等，数字分析师理论上都能理解所有这些并做出预测。

Basically, you know, you know, an agent that is actually smart enough to be an analyst and make certain decisions and judgments that an analyst can make given, you know, what are they looking at given the data that they have access to, whether it's news, filings, transcripts, you know, factor data, whatever it is, the digital analyst is able to understand all this theoretically and make forecasts.

我觉得这可能会稍微改变游戏规则。

I think that that might change the game a little bit.

不只是稍微。

Not a little bit.

实际上，这可能会带来很大的变化，尤其是在捕捉阿尔法收益方面。

It could change it a lot actually, for alpha capture.

那会怎么改变呢？

How will it change it?

这是个好问题。

I, it's a good question.

这真的是个非常好的问题。

It's a really, really good question.

我还不确定答案是什么，但我可以给你几个假设。

I'm not sure I have the answer yet, but I will give you a couple of hypotheses.

好的。

Okay.

我会提出两个可能会发生的假设。

I'll throw out, you know, two hypotheses that will happen.

第一个是，你想想看，数字分析师需要的是已经写下来的信息。

The first one is, is that if you think about it, digital analysts, right, require information that is written down.

我们所有的信息都是被写下来或记录下来的。

We're all written down or captured.

那意味着什么呢？

What does that mean?

这意味着他们必须阅读网上的内容，并能够将其融入自己的背景中。

That means that they have to read something online and be able to put that into their context.

AI需要有上下文，并且必须将其纳入。

AI has to have context and it has to put that in.

随着AI积累越来越多的上下文，你希望它能做出更好的判断，并能做出更准确的预测。

As the AI builds in more and more context, you hope that it has better judgment and it's able to make better forecasts.

但实际上可能发生的情况，这也是我的一个假设，就是AI，除非它们在某个特定方面变得更好，否则它们可能会遭遇所谓的“上下文过载”。

But what actually can happen, and this is one of my hypothesis, is that the AIs, unless they get better in a particular way, they may actually get something called context overload.

我不知道你是否熟悉这个概念。

I don't know if you're familiar with that.

上下文过载就是当你给它们太多上下文时，它们就不太确定该依据什么做决策，结果变得更加随机。

But context overload is when you throw too much context at them and now they're not quite sure what to make a decision and now they become more random.

所以最终你可能会看到，数字分析师们在投资时产生各种幻觉，行为变得随机。

So you could end up having random digital analysts making all sorts of possible hallucinations in the way that they invest.

这是一个假设。

So that's one hypothesis.

这个假设可能是错的，但如果你使用更多的数字分析师，你会看到他们在预测股票走势和基本面方向时，行为更加随机。

It could be wrong, but you'll see more random behavior from analysts and how they forecast the direction of stocks and direction of fundamentals if you use more digital analysts.

第二种可能是，它们确实变得更聪明，判断力也更强。

Now, the second approach is that they do become smarter and they do become more judgment.

也许我们有人类在旁边，非常认真地确保所有防护措施到位，确保它们不会偏离轨道，并且真正理解自己在做什么。

And maybe we have humans sitting there and really, really making sure that all the guardrails are there and everything is, you know, they don't go off the rails and they really understand what they're doing.

好吧，那也行。

Okay, fine.

我们就假设这种情况会发生。

Let's assume that that happens.

然后所有这些分析师会变得越来越聪明。

And all these analysts become smarter and more smarter.

这些数字分析师会变得越来越聪明，因为它们是经过人类精心筛选和培养的。

These digital analysts become smarter and smarter because they've been curated by humans.

我们一直在监督它们，确保它们做出正确的决策。

We're watching them making sure that they're making the right decisions.

但问题在于，我认为到那个时候，关键就变成了：你的数字分析师拥有哪些其他数字分析师没有的独家信息？

The problem with that is, I do believe in my opinion at that point in time is that then the issue is going to come down to what proprietary information does your digital analyst have that another digital analyst doesn't have?

因为如果它们都能获取信息，并做出合理、深思熟虑且理性的判断和决策，那么最终比拼的就是那些边缘性的独家信息。

Because if the world that they can all suck in information and make judgments and decisions that are sound and thoughtful and rational, it's going to come down to the marginal proprietary information.

所以，任何信息一发布，它们就能瞬间分析完毕。

So any information release that comes out, their ability to analyze it will be instantaneous.

它们会立刻知道。

They'll know it.

它们会立刻看到。

They'll see it.

它们会立刻做出判断。

They'll make a judgment on it.

而且可能会和其他数字分析师做出的判断差不多。

And it's probably gonna be a similar judgment that other digital analysts make.

但真正有意思的是，我们如何用各种不同的方式获取专有数据，然后把这些数据喂给我们的数字分析师，同时不让其他分析师接触到这些专有数据。

But what could be very interesting is how we end up capturing proprietary data in various different ways and feed it into our digital analysts and keep that proprietary data away from other analysts.

这和第一种情况完全不同，第一种情况里随机性太大，人们不信任分析师，因为他们经常犯错。

That's a world that's very different than the first world where there's too much randomness and people don't trust the analysts because they make a lot of mistakes.

那种情况下，我们基本上都是在创造一些非常有趣的专有数据和信息的小圈子。

That's a world where we're basically all, in essence, creating very interesting pockets of proprietary data and information.

而这些背景信息就像黄金一样，实际上可能会成为未来人们交易的货币。

And that context becomes essentially like gold that actually will become potentially the future currency that people will trade by.

你给我你的背景信息，我给你我的，或者你付钱买我的背景信息，对吧。

You give me your context and I'll give you mine or you pay me for your context, Right.

因为这是非常非常强大且专有的背景信息。

Because it's really, really powerful and proprietary context.

你乐观看待人类分析师吗？

Are you bullish human analysts?

我觉得人类分析师在很长一段时间内都有他们的位置，因为我还没真正见过机器能思考。

I think that human analysts have a place for a long time because I haven't really seen machines truly think.

也许我，再次，我要冒险说一句，我知道大家都在谈论总有一天会有通用人工智能之类的，但实际上我今天看到的大多数情况，并不是说它们不好。

And maybe I, again, I'm going to be, I'm going to go on a limb and I say, I know everybody talks about at some point they will be able to, there'll be AGI or something, but really most of what I see today, it's not that they're bad.

只是它们并不真正思考，它们擅长的是基于非常强大的数据库和预测引擎来预测下一步的行动、语句或代码。

It's just that they, they don't really think they're good at anticipating what the next action statement code right to right on the basis of really having a very, very powerful database and a prediction engine.

这其实挺有意思的。

It's actually interesting.

我给你举个例子。

I'll give you one example.

这是安德鲁。

This is, Andrew.

不知道你知不知道安德鲁·洛，他是我在麻省理工的一位教授。

I don't if you're familiar with Andrew Lowe is, one of my professors at MIT.

如果你查一下他，你会发现他多年来发表了很多开创性的论文，做了很多很酷的事情。

He, if you look him up, he's, you know, seminal papers on a lot of really cool things that he's done over the years.

但他有一次真的来Two Sigma给我们做了一场演讲。

But he actually came to Two Sigma one time to do a presentation to us.

他来了，那真是太棒了。

And that was really awesome, that he came by.

他说，临走前他说，我要给你们留一个东西。

And he said, look, he said at the end, before he left, he said, I'm going to leave you guys with one thing.

我要把人类智能归结为两件事。

I'm going to boil down human intelligence into two things.

大家都说，哦，真的吗？

And everyone was like, oh, really?

就这些。

That's it.

人类智能就两件事。

Two things for human intelligence.

我想，这就是我们的本质吧。

I guess that's, that's who we are.

他说人类智能就是——他用了这个词，层级记忆，我们待会儿会详细讲——还有预测。

And he said human intelligence is, and he used this word hierarchical memory, which we'll go into in a minute and prediction.

他说，层级记忆就是当你还是孩子时学到的东西，然后你又学了别的东西。

Said, hierarchical memory is the thing that you learn when you're a kid and then you learn something else.

你在此基础上继续构建。

You build on it.

这就是积木块，对吧？

It's just the building blocks, right?

你不断构建、构建、构建，然后你学到新东西。

You keep building and building and building and you learn something.

当你学到新东西时，你就把它放进记忆里。

And when you learn something, you put it into your memory.

你不需要回到最初的原则。

You don't have to go back to first principles.

你总是在之前的基础上学习。

You always learn it on the basis of something before.

所以他的意思是，让我给你举个例子。

So the idea is, he said, let me give you an example.

你半夜走在一条黑暗的小巷里。

You're walking in the middle of the night down a dark alley.

你看到前面有人穿着风衣走着。

You see someone walking in front of you with a trench coat.

你的第一反应是什么？

What's your first emotion?

害怕。

Fear.

不开心吗？

Are not happy?

害怕。

Fear.

就是这样。

There you go.

你不高兴，对吧？

You're not happy, right?

恐惧。

Fear.

然后你注意到那个人，走近一看，他戴着徽章。

And then you notice that that person, as they get closer, they have a badge.

好。

Okay.

还戴着帽子。

And they have a hat.

你觉得接下来的情绪是什么？

What's the next emotion you think is?

如释重负。

Relief.

对，没错。

Yeah, exactly.

你的身体，你，是你自己做到的。

Your body, your, you did this.

这些情绪的处理大概用了不到十秒，甚至五秒。

Those emotions were processed in probably less than what, ten seconds, five seconds.

不是因为你的身体需要回去翻遍你所有的记忆数据库，然后说，嗯，这个人穿着这个，还有那个风衣，可能是黑色的。

Not because you, her body had to go back and go through all your database of everything and say, well, this person is wearing this and they have, you know, this trench coat and maybe it's black.

机器永远也搞不明白。

Machine would have never figured it out.

层级记忆和预测对人类来说真的非常强大，显然对人类思维及其运作方式至关重要。

Hierarchical memory and prediction is human is really, really powerful for obviously the human mind and how it works.

现在我们到了一个有趣的节点，机器和人工智能已经能够模拟其中的一部分。

Now we are getting to an interesting point where machines, AI is able to simulate some of that.

我们看到了，对吧？

We're seeing it, right?

它能够处理大量信息，你可以看到它做出的决策和预测，和我们可能会做的那些预测和决策很相似。

It's able to take a lot of information and you can see that the kind of decisions and predictions that it's making are similar to what the kind of predictions and decisions that we might make.

但现在还是这样，也许未来会更好，但就目前而言，你仍然能看到它需要扫描很多可能性和选项，而人类已经建立了那种层级记忆结构，并且做了很多非常快速的迭代，能够很快地达到我告诉你的那种判断。

But it's still at a point today, and maybe in the future will be better, but at the point today where you still kind of see that it's having to scan a lot of possibilities and a lot of options, whereas humans have built that hierarchical memory structure and have done a lot of very, very quick iteration and been able to get to the kind of that judgment that I tell you about very fast.

而且我告诉你，多年来很多研究人员都试图构建这样的系统。

And that's still something that I'm telling you plenty of researchers have tried to build over the years.

我听过很多播客。

I've listened to plenty of podcasts.

我读过很多关于层级记忆的论文。

I've read plenty of hierarchical memory papers.

模拟人类的层级记忆很难。

It is hard to simulate human hierarchical memory.

非常非常难。

It's very, very difficult.

而数据库以及AI多年来积累的所有东西和上下文等等，唯一的解决办法就是投入大量的GPU。

And databases and all the things that the AIs have built over time and the context and everything like that, the only way to solve it has been just throw lots and lots and lots of GPUs at it.

有很多GPU，让计算更快，用蛮力解决。

There are lots of GPUs, make compute even faster, make it brute force.

最终，它似乎会开始模拟出类似我们的东西。

And eventually it seems like it's going to start simulating something that looks like us.

但这就是我们今天的现实情况。

But that's kind of where we are today in reality.

所以，你一开始问我的问题，可能绕了一大圈，我的回答是，是的，我仍然认为人类分析师有他们的位置和作用，因为他们拥有这种非常有用的分层记忆结构，并且能够基于此快速做出预测。

So, you asked me the question at the beginning, maybe a long way of saying is, yes, I still believe the human analysts have a place and a role because of their ability to have this very, very useful sort of hierarchical memory structure and their ability to make predictions very fast on the basis of it.

有道理。

That makes sense.

我同意。

I agree.

那数字方面呢？

What about numbers?

因为我们有很多人类分析师。

Because we have a lot of human analysts.

大家都明白，你仍然需要人。

I everyone understands that you will still need people.

我的意思是，就算是为了构建那些智能体需要调用的数据库之类的，你也需要人来干这个，还得思考在大家都在用这些新技术的情况下，我们的竞争优势是什么。

I mean, talked about even to build the, say, database or whatever it is that the agents will have to pull from, you'll need people to do that and to think about what is our competitive advantage among all the other people who are utilizing all this new technology.

所以我理解你仍然需要人。

So I understand that you will still need people.

我想听你说的是，你需要多少人？

What I want, I guess, to hear from you is how many do you need?

因为当我退一步认真想想，我觉得分析师的很多技能都是跑腿的活儿。我们真的需要那么多对冲基金吗？

Because when I take a step back and truly think, I feel like a lot of the analyst skill set is runt work, And do we need that many hedge funds?

我们需要吗？需求会扩大吗？

Do we need, does the demand expand?

我不太确定它会扩大。

I'm not exactly sure it does.

你能说说你的看法吗？

Can I get your thoughts on that?

你问了好几个问题，我想我会试着按顺序回答，实际上我会试着从后往前答。

There's a number of questions in there, and I think I'm going to try to answer them succession and actually gonna try to start backwards.

你刚才说的，提到了一个很有意思的点。

What you just said, you said an interesting point.

这有点像，顺便说一句，我之前听投资者这么说过。

It's kind of like, and this is coming from, by the way, I've heard these, I've heard investors say this before.

他们说对冲基金就像餐厅一样。

They said hedge funds are like restaurants.

就是说，你喜欢去餐厅，对吧？

I mean, you like going to a restaurant, right?

你可以在餐厅坐下来，但如果明天这家餐厅没了，你会难过吗？

You could sit down in a restaurant, but if the restaurant went away tomorrow, are you going to be sad?

有些人会。

Some people are.

也许那是你最喜欢的餐厅，但大多数人只是继续前进，去找另一家餐厅。

Maybe it was your favorite restaurant, but most people just move on and they go find another restaurant.

所以对冲基金的世界，以及整个投资管理行业，在很多人的看法里，不只是我的看法，而是我所听到的和了解的。

So the world of hedge funds and in general investment management, you know, in, in many people's opinions, it's not even just mine, but what I'm hearing and what I know about.

基本上你做的就是，你在培育那些能让市场变得更有效的参与者。

It's basically what you're doing is you're seeding participants who are going to make a market efficient.

而且市场里的参与者越多，市场就越有可能变得有效，因为毕竟不是每个人都有完全的信息。

And the more participants that are in the market, the more likely you have some efficiency because not everybody again has the perfect information.

所以他们会用不同的方式投资，互相交易。

So they're all going to kind of invest in a couple of different ways, trade against each other.

有些人可能在某些方向上犯错，另一些人在其他方向上犯错。

Some might make, you know, some mistakes in one direction, others make mistakes in other directions.

但最终你做的就是，你有足够的资本分散，让市场相对健康，这样你明天想买或卖什么，总有人愿意跟你交易。

But ultimately what you're doing is you have enough diversification of capital that the market is relatively healthy that you, if you step in tomorrow and you want to buy or sell something, there's always somebody willing to do it right with you.

想象一下，如果所有对冲基金和投资管理公司都消失了，或者变得极度集中，只剩下那些巨头，他们手里攥着几千亿、几千亿美元的资金。

Imagine a world where all the hedge funds went away and the investment management houses went away, or they became extremely concentrated and they're only stindles and the big kids, you know, all hung out there with hundreds and hundreds and hundreds of billions of dollars.

你觉得那时候市场会是什么样子？

What do you think the market will look like then?

所以好好想想吧。

So think about it.

所以这就是我想说的。

So this is what I'm saying.

所以市场集中并不是好事。

So concentration in a market is not great.

竞争是好事。

Competition is good.

这和餐馆的道理是一样的。

And it's the same idea with like restaurants.

所以，也许你并不为那家餐馆离开而难过，但你挺喜欢这条街上有很多餐馆这个想法，因为如果没有的话，就只剩两家餐馆了。

So maybe, maybe you're not sad that the restaurant left, but you kind of like the idea that there's a lot of restaurants on the block because if there weren't, there's only two restaurants.

那就不太好了。

It wouldn't be too cool.

你周六晚上出去，感觉不会太好。

You're going out on a Saturday night, you don't feel great about it.

你会想，好吧，大家都去同样的餐馆。

You're like, well, everybody's going to the same restaurants.

那里很拥挤。

It's crowded.

空气很闷。

It's stuffy.

里面臭烘烘的。

It's stinky in there.

我不喜欢。

I don't like it.

但如果这条街上有各种餐馆，我可以随便挑，气氛热闹，还有街头艺人表演，这些有趣的东西，让你感觉就像在纽约，对吧？

But if I have all the restaurants on the block and I can pick and choose and there's a lively atmosphere and there's street performers out there, all this fun stuff, you know, telling you feels like New York, right?

你会有一种感觉，就是这里充满活力，有一种真正的精神，这个区域、这个地带正在发生着一些事情。

You have this feeling of like, okay, there's a lively, you know, a true kind of a spirit, an action that's happening in this region, this zone.

金融市场给人的感觉就是这样。

And that's how the financial markets feel.

金融市场应该始终保持活跃。

The financial markets should feel always lively.

总是有交易在进行。

There's always action.

总有人愿意买，有人愿意卖，有人愿意接手别人不愿意做的交易。

There's always somebody willing to buy someone willing to sell someone willing to step in, take the trade that somebody else is not willing to do.

有人愿意做多，有人愿意做空。

Someone willing to take the over someone willing to do the under.

这就是为什么我认为你需要一个持续健康的生态系统，这是它的美妙之处。

That is the beauty of why I think you need to have a constant healthy ecosystem.

你应该总是这样想：每当你看到一个新的对冲基金成立，不要想‘天哪，又一个对冲基金’，而是想‘又多了一个市场参与者’。

And you should always consider that anytime you see a new hedge fund form, instead of saying the idea of like, Oh God, yet another hedge fund actually think to yourself, Another market participant.

你觉得五年后对冲基金行业会是什么样子？

What do you think the hedge fund world looks like in five years?

我不确定会和现在有多大不同。

I'm not sure it's that much different than it is today.

我觉得现在有很多因素让成立对冲基金变得非常容易。

I think that there's a number of things that are happening that are making it really easy to have a hedge fund.

我想说的是这个。

I guess what I would say is this.

我看到的是对冲基金越来越多，而不是越来越少。

I see more hedge funds, not less.

为什么？

And why?

因为成立对冲基金的门槛正在快速降低，对吧？

Because the barriers to entry to build a hedge fund are falling pretty fast, right?

你知道，门槛包括你得有一套自己觉得靠谱的策略，可能还需要几个数字助手帮你搭建起来。

You know, the barrier to entry involves obviously being able to have some kind of a process that you are comfortable with, maybe with a couple of digital agents that help you build that out.

你当然需要一定的资金去投资，替你的投资者赚钱。

You obviously need a certain amount of capital to be able to invest, to make it on your behalf of your investors.

然后，你还需要满足一些监管要求，而现在很多合规引擎都是自动化的。

And then, obviously, you need to meet some regulatory standards, which a lot of compliance engines now are all automated.

所以它们实际上降低了向监管机构做报告和分析的成本。

So they're actually reducing the cost of being able to do reporting and analytics to the regulators.

所以如果你把这些都考虑进去，实际上意味着对冲基金的数量可能会增加，而不是减少。

So if you think about all that, it would actually suggest that there is a likelihood that more hedge funds will actually form not fewer hedge funds.

而且会有更多的人有机会参与到这个领域中来。

And that there will be the opportunity for a lot more people to be participating in that world.

我确实觉得我可能是错的，但我愿意大胆预测一下，今天和五年后的对冲基金数量相比，如果数量翻了十倍，我也不会感到惊讶。

I actually believe I could be wrong, but I would go out on a limb and saying that to count the number of hedge funds you have today and in five years, I wouldn't be surprised if you have 10 times the number.

十倍？

10 times?

十倍的数量。

10 times the number.

好的，你是在Odds On Open节目上第一次听到这个观点的。

Well, you heard it here on Odds On Open first.

这就是一个数量激增的游戏。

It's a proliferation game.

我觉得人们会找到各种机会的缝隙。

I think it's going to be, people are going to find pockets of opportunities.

你还没算上固定收益领域可能涌现的对冲基金数量呢。

You haven't even looked at the number of hedge funds that could form out of the fixed income world.

那个领域现在真的很冷清，真正在玩的对冲基金玩家少之又少。

That's a really dark place right now with a lot of very, very few real players on the hedge fund space.

想想看，有多少对冲基金可能去做大宗商品交易。

Think about the number of hedge funds that could be doing commodity trading.

再看看私募投资，真正让私募投资变得更透明、更透明、更多AI、更多新东西融入进来。

Think about private investing and really making private investing more transparent, more transparency, more AI, more things come into it.

AI也可能让事情变得更透明，因为它让数据更容易获取，从而更透明。

AI makes things also potentially more transparent if it's making data more transparent because it's easy to access it.

突然间，人们开始交易私募公司，私募公司交易所应运而生，比如二级市场，然后对冲基金也开始在二级市场交易。

All of a sudden people are trading private companies, private company exchanges come along like secondary markets, all of a sudden trade hedge funds are trading secondary markets.

所以这肯定会发生，因为会有更多资产类别的机会。

So it's going to happen because there's going be more asset class opportunities.

更多流动性会涌入那些历来对冲基金很少涉足的资产类别。

More liquidity is going to come into asset classes that historically have not been frequented by hedge funds.

而且在我看来，建立和管理对冲基金的门槛会降低。

And the barriers again to entry to building and managing a hedge fund, in my opinion, are going to go down.

这只能说明会有更多机会和渠道向人们敞开。

That only tells me that there's going be more access to people and opportunities for people.

婴儿潮一代的人，你知道，我觉得他们最后都会变成那种在自家衣柜里做交易的人。

The baby boomers are all going to be, you know, I feel like they're all going be trunk closet traders anyway.

他们现在都在退休，而且正在交接财富——顺便说一句，这是另一个你应该注意的有趣现象。

They're all retiring now and they're handing and by the way, that's another interesting thing you should be aware.

你可能知道，我们正经历着有史以来最大规模的财富转移之一，婴儿潮一代把财富直接传给他们的子女和孙辈。

You probably know, we're dealing with one of the biggest wealth transfers, one of the largest ever wealth transfers that's happening for baby boomers right to their kids and their grandkids.

而且，你知道，这已经在发生了，但未来十年到二十年会加速进行。

And then, you know, that's already actively happening, but it's happening right over the next decade or two.

光是这一点就会推动人们产生更多投资的需求。

And that alone is going to drive opportunities for people to want to make more investments.

那他们会把钱投到哪里呢？

Well, where are they going to invest their money?

他们会把钱投到标普500那些指数基金里吗？当然会，但那太无聊了。

Are they going to invest their money in a, you know, I mean, yes, S and P and all that indexes, but that's boring.

人们更想投资那些刚创办对冲基金的朋友，因为他们觉得这些朋友很聪明，能做出些酷炫的事情。

People want to invest in their friends who just started a hedge fund because they think that they're really smart and they could do something cool.

这种情况肯定会发生。

That's going to happen.

所以，我就说到这儿吧，但这是我的看法。

So anyway, I'll stop there, but that's my belief.

那这些情况你都亲眼见过吗？

And are you seeing all these things firsthand?

因为你服务了很多对冲基金，有顶级的、大型的，也有小型的。

Because you service plenty of hedge funds, top hedge funds, large hedge funds, small hedge funds.

我想知道你的客户们对此怎么看，他们对新技术感到兴奋吗？实施的速度是不是在加快？

I guess what your clients tend to say about this, are they excited about all the new tech, and is the rate of implementation just speeding up?

你在第一线看到了什么？

What are you seeing on the front lines?

是的，我们今年看到，和所有人一样，数据获取和分析能力出现了巨大的加速，速度简直前所未有，对吧？

Well, we're seeing a massive this year, a massive acceleration like everyone else in the ability to access data and analyze it, literally at, you know, at speeds that are unheard of, Right?

过去，要扎实、数学上严谨地获取和分析数据，通常需要花很多时间。

The analyzing data and and and and actually accessing and analyzing data in a very robust, mathematically sound way, historically, it's taken time.

你得确保数据是干净的，还要确保分析质量高。

You have to make sure that the data is clean and to make sure that there's obviously that the analysis is high quality.

你得确保没有数学错误，人们称之为数据清洁度，但之后他们还得深入进行所谓的验证，数据验证，要非常深入地验证，确保数据不仅正确，而且背后的数学运算确实生成了正确的数据。

You have to make sure that all the mathematical mistakes, people call it sort of data cleanliness, but then they have to actually look at you know, what they call validation, you know, data validation, and they have to go into the validation very deeply and understand that the data is actually not just correct, but the math math behind it is actually generating the right data.

可能会有很多数值上的不准确，这会导致你做出错误的数值预测。

You can have a lot of numerical inaccuracies, which will cause you to make numerical forecasts that are incorrect.

所有这些事情，过去要花几天甚至几周的时间，才能得出一个真正有用的、强有力的见解。

All those things used to take days and weeks to make, to actually generate a strong, like a really useful insight.

而现在，几个小时就能完成，对吧？

And now it's happening in hours, right?

你打开Claude，把想法扔进去，或者用一些健壮的代码和防护措施，随便哪个想法，都能快速处理。

You pop open Claude and you just throw into it or tragedy fatigue, but certainly, you know, thrown, any one of these, you know, ideas that you have with some robust piece of code and guardrails around it.

这个东西就能帮你生成数据、做验证，然后把数据给你。

And this thing's going to be able to generate, do the validation and get you the data.

所以你说到数字运算，数字分析师和AI在数字运算方面确实非常擅长，尤其是你给它们设定了明确的规则之后。

So to your point around number crunching, digital analysts and AIs are really, really good at number crunching, especially given the guardrails that you can give to them.

如果你只是让它们随便编点什么，它们可能会产生幻觉，但如果你能引导它们去关注数学本身，就像给马戴上眼罩一样，让它们专注向前看，它们就非常擅长。

They're not great at number crunching if you just ask them to come up with something that's hallucinations, but they're really good if you're able to point them to the math and say, like a horse with blinders, keep your keep your, your eyes forward, Look at and look ahead.

只要专注于数学，它们就能像闪电一样快速处理。

Just focus on the math, and they can run through it like lightning.

而且能很快完成大量分析。

And do a lot of analysis very fast.

所以这让大家能够轻松获得大量分析结果，这是他们以前想都不敢想的。

So that is driving the ability for people to have access to a lot of analytics at their fingertips that they never thought that they would have.

但现在的问题是，你有了这么多分析结果，我给了你成千上万的分析数据。

The problem is now you have all these analytics, and I've showered you with thousands and thousands of analytics.

接下来你需要做什么呢？

What's the next thing that you need to do?

做决定？

Make a decision?

你需要所有这些分析做什么？

Why do you need all this analysis?

你打算拿它来干什么？

What are you gonna do with it?

这就回到了我刚才说的另一个词，判断力。

And that comes back to another word I just said, it's judgment.

所以突然间，谁来做出判断？

So all of a sudden, who's going to make the judgment?

是那些生成所有这些分析的AI吗？

The AIs that just generated all those analytics?

也许吧，也许其中一些会。

Maybe, maybe some of them will.

也许你可以有一个超级代理，对其他代理做出判断。

Maybe you can have a super agent that makes judgments on the others.

但实际情况是，在所有这种情况下，我们又回到了之前提到的那个点，也就是量化分析师几十年前就明白的道理：即使机器在跑所有数据，你仍然需要人类来做判断。

But the reality is in all these situations, we're coming back to the same thing that we mentioned earlier, which is something that Quants learned decades ago, is that you still need humans for the judgment, even if the machines are running all the numbers.

你觉得生成式AI会让市场更有效率还是更没效率？

Do you think Gen AI leads to more efficiency or more inefficiency in markets?

我觉得这跟你问的关于对冲基金数量多少的问题类似。

I think it's a similar question to your question on more or less hedge funds.

这里有一个推论，跟你说的那个话题是平行的。

There's there's a corollary here, a parallel to what you're talking about.

生成式AI能让你更快地分析信息。

Gen AI allows you to analyze information more quickly.

这点我们知道。

We know that.

你能快速获取信息。

You're able to get the information quickly.

你能接触到这些信息。

You're able to make access to that.

那会带来什么影响？

What does that do?

这样更多人就能接触到这些信息，理论上也能做出决策。

That allows more people to get access to that information and theoretically make decisions.

但问题是，如果大家都基于同样的信息做决策，那我唯一能依靠的就是我对信息的解读和别人不一样，因为我对这些信息的意义有不同的判断。

Now, the problem is if I make decisions on the same information that everybody else makes decisions on, the only thing I can rely on is that I'm going to interpret that information differently than they have because I have a different set of judgments about what that information means.

这就又回到了经验和市场理解上。

And that's where it comes back down to experience, understanding of the markets.

不同的人显然有不同的经验背景，来自不同的地方，投资过不同的领域，甚至家庭背景和成长经历也会影响他们对投资方向的判断。

Different people have different, obviously, experience sets where they come from, where they invested in, Even what their family backgrounds are and how, how diff you know, how they grew up will also cast those judgments on whether they decide to invest in one way or another.

所以，当观点多样化时，市场其实并不是很有效，对吧？

So when you have a diversity of opinion, you actually don't have a very efficient market, right?

如果所有人观点一致，都知道一旦处理完这条信息，答案就是这个。

If everybody had the same opinion and they all knew that once I process this piece of information, this is the answer.

而且每个人都在三十秒内得出同样的答案。

And everybody comes to the answer within thirty seconds.

这就是有效市场假说，对吧？

That's the efficient market hypothesis, right?

信息直接反映到价格上。

That gets you to information gets you to price.

所有人都对价格达成一致。

Everybody agrees on the price.

砰。

Boom.

价格就移动到它该在的位置。

The price moves to where it's supposed to be.

然后事情就结束了。

And now we're done.

但如果人们对信息的含义有不同的看法，他们都能快速处理，但对信息的判断各不相同。

But if people have different variable opinions on what the information means, they can all process it fast, but they all have different judgment calls on the information.

价格上仍然会有很多噪音，对吧，很多噪音。

You will still have, right, a lot of noise and a lot of noise that happens in the prices.

而且我觉得最终还是会这样。

And I think that that's still ultimately what will happen.

实际上，可能比现在还要更嘈杂。

And in fact, it might even be more noisy than what it is today.

这其实挺有意思的。

It's actually interesting.

大家以前总觉得，如果所有量化模型都以同样的方式运作，那我们就真的不需要人类交易员了。

Everybody always thought if all the quant models were all working the same way, we would have really no reason to have any more human traders.

但过去二十年发生的情况恰恰相反。

And the opposite has happened over the last two decades.

顺便说一句，这在我还在Two Sigma的时候也让我很惊讶。

And that's something, by the way, that surprised me when I was Two Sigma.

我以前总觉得未来的趋势是，我们都在朝着某种奇点迈进。

I always thought that the the wave of the future is, we're all just moving toward singularity or something.

但现实是，现在市场里的混乱程度比以前更高了。

But the reality is there's even more entropy now in the markets than before.

我觉得这是因为获取信息和分析变得太容易了，所以现在有更多人在做判断。

And I think it's because access to information and analysis has become so easy that more people are now making judgements.

散户在做判断，理财顾问也在做判断。

Retails making judgements, financial advisors making judgements.

所有人都在根据自己的想法决定买什么、卖什么。

All people are making judgements on what they want to buy and what to sell.

这实际上造成的混乱比人们预想的还要多。

And that's actually creating even more entropy than what people realized would happen.

我觉得用熵来打比方很有意思。

I think using entropy as an analogy is fascinating.

说到这个，Omer，你职业生涯中有很大一部分时间都在做这个。

And on that, Omer, you spent a good chunk of your career.

我称之为把人类行为映射并汇总成洞察，供顶级对冲基金交易使用。

I call it mapping and aggregating human behavior into insight for one of the best hedge funds to trade on.

你觉得所有新的生成式AI工具会不会让某些行为偏差变得更明显？

Are there any behavioral biases you think will accentuated because of all the new Gen AI tools?

我仍然认为，只要人类需要做决定，不管生成式AI工具多好。

I still think that if humans have to make decisions, no matter how good the Gen AI tools are.

不管生成式AI工具多厉害，能在信息出来三毫秒内就把你想要的分析送到你手边。

And no matter how great the Gen AI tools are going to give you the analysis that you've wanted at your fingertips within, you know, three milliseconds of the information coming out.

如果你还是得做出判断，市场里就两样东西说了算：恐惧和贪婪。

If you somehow have to make some judgment call, two things are going to rule in the markets, fear and greed.

就这些。

That's it.

它们从人类存在起就有了。

They've been around as long as humans have been around.

只要市场还在，只要人类还在对市场做判断，这两样东西就一定会一直在，作为主导情绪决策的力量。

And as long as markets will be around, as long as humans will be making judgment calls on markets, you get the guarantee that those are the two things that are going to always be there as reigning emotional decisions.

不幸的是，我们就是这样的人。

That's unfortunately the animals that we are.

不管你想多理性，投资者最终都会这样。

No matter how rational you want to be, that ends up what ends up happening to investors.

最后一个问题。

Final question.

假设现在有一位对冲基金经理正在看这个播客，你看到我们说的所有内容，生成式AI会改变很多，但也不会从根本上改变我们作为生物的本质。

Let's say there's a hedge fund manager watching this podcast right now, and you're seeing everything we're saying, gen AI is gonna change a lot, but it's also not gonna change fundamentally what we are as creatures.

他问你，他说，Omer，你见过各种各样的对冲基金经理，你通过Omega Point为他们服务，人们犯的头号错误是什么？

And he asks you, he says, Omer, you've seen all sorts of different hedge fund managers, you service them through Omega Point, what is the number one mistake people make?

我需要停止做的一件事是什么？你看到那些年复一年表现优异的客户，那些表现好的，他们都有什么共同点？

What is the one thing I need to stop doing that you see your clients who perform year after year after year, the ones that perform, what's something they all do?

这是个好问题。

It's a great question.

这确实是个好问题，要把它归结为一件事。

That's actually a great question to boil it down to one thing.

所有成熟的对冲基金经理在他们的投资流程中都会做的一件事是什么？我觉得他们能经常指出这一点，但仍然不去纠正它。

What is the one thing that all sophisticated hedge fund managers do in their investment process, I would say that they can point to regularly and yet still don't correct it.

对吧？

Okay?

而且我觉得这取决于对冲基金经理和他们的策略，但咱们还是想想人吧。

And again, I think that it depends on the hedge fund manager and their strategy, but let's think about human.

咱们还是聚焦在人身上，而不是那些只做系统化模型的。

Let's stay on the human, not the ones that are just building only systematic models.

我觉得在那些非常聪明、超级聪明的基金经理身上，你经常能看到两种行为偏差。

I think that there's really two pieces of behavioral bias that you see regularly in this in essentially very, very intelligent and super smart human managers.

第一种，我称之为“起步时的保守”。

The first one is what I'm going to call conservatism out of the gate.

他们有了一个想法。

They have an idea.

他们做了研究。

They've done the research.

他们做了分析。

They've done the analysis.

他们真的很了解这只股票。

They really know the stock.

他们真的很了解这家公司。

They really know the company.

但结果他们只投了1%或2%的仓位。

But yet they only put a 1% or 2% position down.

为什么？

Why?

为什么不投4%？

Why not a 4%?

为什么不投5%的仓位？

Why not a 5% position?

如果你这么了解，那好吧，咱们保守一点。

If you know it so well, well, let's be conservative.

先看看情况再说。

Let's see how this plays out.

这不是在Robinhood上赌博下注。

This is not, you know, we're not betting and gambling on Robinhood here.

我们是在认真思考。

We're being thoughtful.

我们是自己资本的管理者，也是投资者资本的管理者。

We were stewards of our capital, of the capital of our investors.

我们想做出好的决策。

We want to make good decisions.

让我们认真对待这件事。

Let's be thoughtful about it.

结果发现，很多超额收益其实在最初那个保守阶段就流失了。

It turns out that a lot of the alpha is actually lost in that initial stage of being conservative.

所以，当你是一个非常聪明、有才华且自律的机构投资者时，偏见并不一定是过度自信。

So the bias is not to be overconfident necessarily when you're a really smart and talented and disciplined institutional investor.

偏见往往在相反的一面，就是过于保守，没有在初期就抓住一定量的超额收益。

The bias is oftentimes on the opposite side to be too conservative and not capture a certain amount of alpha upfront.

而这最终实际上有时会成为区别所在——比如一个经理每年赚5%到6%，而另一个经理每年赚10%到11%。

And that ultimately ends up actually being sometimes the difference between we're talking a manager earning, call it five to 6% a year and a manager earning 10 to 11% a year.

这些偏见有时候影响就这么大。

That's how big those biases can sometimes be.

而且他们自己也知道。

And they know it.

你要是跟他们聊，他们会告诉你，我们知道自己有这种偏见，这种保守的倾向，但我们不得不这么做。

They'll tell you, if you talk to them, they'll tell you, we know that we have that bias, that conservatism bias, but we have to act that way.

这是天生的。

It's innate.

就像我们建仓的时候必须保持纪律性。

Like we have to be disciplined about how we put our positions into the market.

我们不能一下子全押进去，这确实没错，但相对于超额收益来说，你还是太保守了，数据会一次又一次地证明这一点，你还是太保守了，可你依然会犯同样的错误。

We can't just go all in, which is true, but you're still too conservative relative to the alpha and the data will disprove you again and again, that you're still being too conservative and yet you still make the same mistake.

我很喜欢。

I love it.

非常感谢你来上我们的播客。

Thank you so much for coming on the pod.

这真是太棒了，Omer。

This was wonderful, Omer.

非常感谢你，Ethan。

Really appreciate it, Ethan.

祝你今天剩下的时间愉快。

Have a good rest of your day.

我们很快再聊。

We'll talk soon.