AI 为什么取代不了选股者：Jack Woodruff 与 Vinoo Ganesh 谈投资研究

简要描述Brief Description

一场关于人工智能在基本面投资中的炉边对谈：更快地完成准备工作、人类判断力依然不可或缺、数据可审计、专有研究、基金结构，以及为何完全自动化且没有分析师的基金可能难以跑赢由人类主导的团队。A fireside conversation about AI in fundamental investing: faster preparation, the continuing need for human judgment, auditable numbers, proprietary research, fund structure, and why fully automated analyst-free funds may struggle to outperform human-led teams.

目录Table of Contents

• 引言
  Intro
• 选股方式会改变吗？
  Will stock picking change?
• 一位分析师能覆盖 400 只股票吗？
  Could one analyst cover 400 stocks?
• 初级分析师将不再做的事
  What junior analysts will stop doing
• 更聪明了，还是只是更忙了？
  Smarter or just busier?
• 永远不要相信无法审计的数字。
  Never trust a number you can't audit
• 你绝不会让 AI 供应商看到的内容
  What you'd never let an AI vendor see
• 当所有人都拥有相同模型时，优势从何而来？
  Where edge comes from when everyone has the same models
• 2027年基金：自建还是购买
  Build vs. buy for a 2027 fund
• 小型基金与巨型基金
  Small funds vs. giant funds
• 被过度炒作的理念：没有分析师的对冲基金
  The overhyped idea: the no-analyst hedge fund

简介Intro

发言人 1：我是 Candlestick Capital Management 的创始人兼前首席投资官。这是一家总部位于康涅狄格州格林威治的多空股票对冲基金。创立 Candlestick 之前，我曾任 Surveyor Capital 的合伙人兼投资组合经理。Surveyor Capital 是 Citadel 旗下的一个部门。Speaker 1: I am the founder and former CIO of Candlestick Capital Management, a long-short equity hedge fund based in Greenwich, Connecticut. Before founding Candlestick, I was a partner and portfolio manager at Surveyor Capital, which is a division of Citadel.

发言人 2：太棒了。谢谢你，Jack。我们之所以这样安排，是因为之前聊过这个话题。我觉得，Jack 是最值得交流的人之一，因为他的整个职业生涯都在选股，而如今的环境正以前所未有的速度变化着。Speaker 2: Very cool. Thanks, Jack. So, the reason we did this way is we've had this conversation before, and I think this is one of the most enlightening folks to chat with, given that Jack has spent his career picking stocks in an environment that's changing more than it ever was before.

选股方式会改变吗？Will stock picking change?

发言人 2：我们就把这当作一场炉边谈话吧。杰克，请给我们描绘一下未来的图景。比如说，5 年后，人工智能时代的基本面投资会是什么样子？Speaker 2: We will treat this as a fireside chat. Jack, paint the picture for us. What does fundamental investing look like in the age of AI, say 5 years from now?

说话人 1：嗯，这很难说。比如说，长期选股，我不认为它会有太大变化。一直以来，市场信息都是一块块拼起来的，而找到与众不同的观点也一直是创造超额收益的方式。Speaker 1: Well, it's it's hard to say. Like, long-term stock picking, I don't believe it's really going to change much at all. There's always been a mosaic, and there's always been alpha creation by finding a divergent view.

所以，这一点完全不会改变。我认为它仍会保持不变。而人们如何整合所有信息、如何了解大家都具备什么以及基本门槛是什么，还有如何整合这些数据，这些都会彻底改变。So, that isn't going to change at all. I think that still will stay the same. Now, how people synthesize all of the information and where table stakes go to and to learn in terms of like what everybody has, and then how you synthesize that data is going to change completely.

比如投资组合经理或分析师这些传统岗位的职责，将会被彻底颠覆。我仍然认为，现在就断言这一切最终会是什么样子还为时过早，因为这些工具实在太新了，尤其是对用户来说。很多人已经在用它们做一些事情，但我不太确定我们是否真的已经到了那个阶段，也不确定是否真的会出现那些令人惊叹的新事物。The roles the traditional roles of say a portfolio manager or an analyst are going to be completely thrown upon their heads, and I still think it's like too early to say what that's all going to look like as these tools are just so new, especially to the users. You got a lot of people doing a few things, but I it's just not clear to me that it's like we're there and oh my gosh, it's going to be these new things.

我们还有很多其他事情要聊，不过我觉得，看看这一切最终会如何发展会非常有意思。Like we have we have a bunch of other things to kind of talk about, but I think it's going to be super fascinating to watch how it all plays out.

一位分析师能覆盖400只股票吗？Could one analyst cover 400 stocks?

说话者 2：非常酷。那么，在 Citadel，你们有 18 个人负责跟踪 400 只股票；在 Candlestick，你们有 30 位选股分析师。到 2030 年，一位顶尖分析师能覆盖全部 400 只股票吗？Speaker 2: Very cool. So, at Citadel, you had 18 people covering 400 stocks, and at Candlestick, you had 30 stock pickers. Could one great analyst cover all 400 by 2030?

发言人 1：所以，最大的问题是人类仍然是瓶颈。我的意思是，说到底，你必须把信息装进自己的脑子里。所以，关键在于人和机器如何协同工作。投资管理本质上是一项时间管理工作。Speaker 1: So, the biggest issue is humans are still the bottleneck. I mean, like at the end of the day, you have to get the information into your head. So, it's really all about sort of how man and machine can work together. investment management is a time management exercise.

我的意思是，如果我把所有时间都花在研究一只股票上，那当然很好。我大概会比世界上任何人都更了解它，但要是那只股票没什么可做的，我就会有点……没什么事可做。嗯，如果我研究两只，那花在每只上的时间就只有一半，依此类推，直到我离开。What I mean by that, if I spent all of my time working on one stock, that'd be great. I'd probably know better than anyone else in the world, but like if there wasn't anything to do in that stock, I'd be kind of there isn't much to do. Well, if I do two, well, then I have half as much time, and so forth until I go.

所以，你在任何一只特定股票上投入更多时间，获得的回报都会逐渐递减。那么，AI 能做些什么呢？AI 能帮我覆盖更多标的吗？So, you start getting diminishing returns on how much time you could spend on any particular stock. So, you know, what can AI do? Can AI help me cover more names?

也许吧。也许它能让我提升自己在那些只是略有了解的方面的基础水平，因为过去我得花好几个小时做准备，才能为那项工作做好准备；而现在，也许 AI 程序能在 5 分钟内帮我完成很多准备工作，以前这得花我 6 到 8 个小时。Perhaps. maybe it allows me to raise the table stakes on what I just kind of like have as a general knowledge, because that work used to take me hours to prepare to get me ready for what it was, and now maybe the, you know, an AI program can, you know, help me prepare a lot of that work in 5 minutes, where it would used to take me 6 to 8 hours.

所以，这让我可以做其他事情。再说了，这是个竞争非常激烈的行业，所以并不是说如果我裁掉所有这些人，或者把分析师从原来的30人减到5人，那些人就能做更多事。没错，也许他们能做更多，但现在突然之间，这就腾出了时间，我可以用来做更多专有研究、渠道调研，或者更多行业背景方面的工作，因为说到底，你得找到这些不同的观点。所以，如果我还是像以前那样做同样的事，心想“嘿，哇，我两小时就能做完”，那别人还是会一天工作16个小时，努力找出那个能让你赚钱的额外机会。So, that allows me to do other things. Again, like it's a very competitive industry, so it's not like if I get rid of all these people, or if I have like five analysts, whereas before I had 30, yeah, maybe those people can do more, but now all of a sudden this opens up other time this opens up time that I could spend doing either more proprietary research, channel checks, more sort of like industry backdrop work because again, like you're going to need to find these divergent views. So, it's like if I do the exact same thing I was doing before and it's like, "Hey, wow, I can get my work done in 2 hours." Well, someone else is going to be still working 16 hours a day trying to find like that extra thing that's going to that's going to make you money.

因为，你知道，光是做这些事情，显然就跟我们一两年前还会付钱让分析师做的事情差不多；你知道，一年后我们就不会再付钱让分析师做这些了。Because, you know, just doing this stuff is obviously like what we used to pay an analyst to do, you know, a year or two ago is not what we're going to be paying an analyst to be doing, you know, in a year.

初级分析师将不再做的事What junior analysts will stop doing

很酷。顺着这个话题，今天初级分析师做的事情中，有什么是你觉得如果他们五年后还在做就会显得很荒谬的？Speaker 2: Very cool. And on that, what's What is something today that a junior analyst does that you think will be absurd if they're still doing in 5 years?

说话人 1：嗯，这还挺有意思的。就好像在说：“哦，你不会去读 10-K 文件，也不会去读那些，你知道的，会议文字记录。”但我其实不相信这是真的。Speaker 1: Well, it's funny. Like it's like, "Oh, you're you're not going to be reading 10-Ks. You're not going to be reading, you know, transcripts." I actually don't believe that to be true.

比如说，如果你真的很了解某个名字，你还是需要处理大量这类信息。现在，你或许可以获取一些摘要版本，缩短完成这些事情所需的时间，但你仍然需要把这些信息吸收到脑子里。以前我们有很多分析师，会梳理大量诸如此类的大数据。Like if you're truly close to a name, you're going to still need to like process a lot of that information. Now, you might be able to get like summarized versions that can cut down the time it takes you to do those things, but you still need to get that information sort of into your head. Like we used to have a lot of analysts that would be going through a lot of like sort of like big data and things like that.

我确实觉得这类事情可以在某种程度上外包出去，而且会非常简单。你知道，如果很多人没有在做这类事情，我会觉得很惊讶。不过，我认为 AI、智能体或其他任何东西将会做的，是推动你朝着正确的方向前进，让你研究合适的名称。I definitely think that can sort of be outsourced and that'll be super easy to you know, I would be surprised if if a lot of these guys aren't necessarily doing that stuff. But what I believe the AI is going to do or the agent or whoever it is going to be pushing you in the right direction to work on the right names.

正如我所说，这关乎时间管理。关键在于，我怎么知道该把精力放在正确的事情上？最终的赢家将会是那些能弄明白如何使用这些工具，让你花适量的时间去寻找可能存在的不同观点的人。Like I said, this is time management. It's all about how do I know to work on the right things? And that's where the winners are going to is going to be is the people who can figure out how to use these tools to get you to spend the right amount of time working on potentially finding like a divergent view.

更聪明了，还是只是更忙了？Smarter or just busier?

发言者 2：非常酷。在大数据这方面，分析师每周要花大量时间查找、清理和检查数据。清理和检查数据。Speaker 2: Very cool. And on that big data side, so analysts spend a huge portion of the week finding, clearing, and checking data. Cleaning and checking data.

当那种情况消失后，他们会变得更聪明，还是只是更忙？When that goes away, do they get smarter or just busier?

发言者 1：跟所有事情一样，嗯，既然现在我们有了更多时间，就会去寻找更多数据。我们想要更多信息。我需要进行更多专有研究访谈。Speaker 1: Like anything, Well, now that we have extra time, we'll be looking for more data. We want more information. I need to be doing more proprietary research calls.

我需要参与更多行业项目的搭建。我需要做越来越多的事情。这只会提高每个人的工作量标准。I need to be doing more industry builds. I need to be doing more and more things. And it's just going to raise the bar on the amount of work that everybody's doing.

所以，当每个人做的事情都一模一样时，这就成了入场门槛。就像理所当然的事，我必须拥有它。And so, when everybody's doing all the exact same things, that just becomes the table stakes. It's like it's just automatic. I have to have it.

我不行，我，你知道，我不是……我不是在竞争。然后，关键就是找到下一层信息，了解为什么会出现不同的观点。就是这样。I can't I you know, I'm not a I'm not competing. And then it's all about finding that next layer of information about how there's going to be a divergent view. It's like that's that's it.

这只是拼图的一部分。也不是说你只要找到这一个数据点，而它就会成为关键所在。That's part of the mosaic. And it's not like you're just finding this one data point, and that data point's going to be the thing.

投资拼图的关键在于找到三四个彼此吻合的数据点，而这些数据点不一定来自同一个地方。我认为，这通常是人们普遍认可的创造阿尔法的方式。The investment mosaic is all about finding three or four sort of data points that all sort of are lining up that aren't necessarily coming from the same place. And that's generally, I think the widely accepted view of how alpha is created.

永远不要相信无法审计的数字。Never trust a number you can't audit

说话人 2：太酷了。你的背景相当有意思。你最初是注册会计师。Speaker 2: Very cool. You have a pretty interesting background. You started off as a CPA.

你会在不知道模型给出的数字从何而来的情况下，就批准它吗？Would you ever sign off on a number a model produced without seeing where it came from?

说话人 1：不，不，不，绝对不是。我的意思是，Kepler 目前正在做的事情中，最令人兴奋的就是我们有能力审计每一个数字。Speaker 1: No, no, no. Absolutely not. I mean, that's like actually the most exciting thing about what's going on at Kepler is just our ability to, you know, audit every single number.

而且你知道，当这些工具开始失控时，你——每当我们犯错时，因为这些内容是由人工录入的，这种情况非常罕见。但对我们来说，最重要的是数字必须准确。不过，错误还是时不时会发生。你知道，一次类似的数据录入错误就可能造成数百万美元的损失。And, you know, when these tools start running amok and you Whenever we made mistakes, cuz we had humans entering these things, it was very rare, but like the most important thing for us is that we got the numbers right. But still, mistakes were made from time to time. And like, you know, millions of dollars could be lost off of a single sort of like data entry mistake.

挺有意思的，我……我们绝不可能让 AI，你知道，准确地告诉我们该做什么、运行交易算法之类的事情，尤其是你没法相信它提供的信息的话。因为这样一来，它突然就可能开始……可能会失控，什么情况都有可能发生。It's funny like I there's no way we're going to let AI, you know, tell us exactly what to do, run trading algorithms, all these kinds of things, if you can't trust the information cuz then all of a sudden it could start it could run a muck it could be anything.

就像数字的准确性，以及你审核这些数字的能力，绝对是整个流程中最重要的部分，因为只有这样，你才能在一定程度上信任其中一些结果。你知道，现在有很多人在用这些工具，他们会让 Claude 或其他工具帮忙搭建模型之类的，但之后还是得逐一审核数字，确保一个不漏。所以，自动化这件事真的能帮我们省下那么多时间吗？我觉得大家都想当然地认为，哦，它会越来越好，会越来越好。但话说回来，如果你能审核所有内容，我觉得这将会是一个极其重要的因素。Like the accuracy of the numbers and your ability to audit it is absolutely the most important part of this entire process of being able to sort of like trust some of these things and you know, I know a lot of people that are using these tools today and so they'll ask, you know, Claude or somebody to build help them build a model and stuff and then they still have to go through and audit the numbers to make sure that they're all there. So it's like are we really saving that much time by automating this? It's it's I think everybody assumes oh it'll get better it'll get better but again like when you know you can actually audit everything it's I think that's going to be just a huge important factor.

这似乎很明显。It's it seems obvious

发言者 2：是的。Speaker 2: Yeah.

说话人 1：而且很简单，但显然是根据……Speaker 1: and easy but it's apparently according to the

说话人 2：不，不是这样的。Speaker 2: No, it's not so.

你绝不会让 AI 供应商看到的内容What you'd never let an AI vendor see

说话人 2：太棒了。所以，对于这里的很多 AI 公司来说，Jack 就是买家。也就是说，在对冲基金，你要说服这个人购买软件。Speaker 2: Awesome. So for a lot of the AI companies here, Jack is the buyer. So like this is the person you convince at hedge funds to buy software.

所以我很好奇，在你的投资流程中，哪些部分你绝不会让 AI 供应商看到？And so I'm curious, what part of your investment process would you refuse to let an AI vendor see?

说话人 1：所以这个问题问我就问错人了，因为这不是我的专长，比如数据安全之类的。你知道，这完全是 CTO 才该回答的问题。我有个很棒的 CTO，他是我会交给他处理这件事的人，他会跟我们说明情况。我对任何形式的内部数据都会非常紧张。听着，只要能避免，我就不希望任何东西泄露出去。虽然我甚至不知道该用什么术语，在你们这些工程师面前听起来肯定像个白痴，但你知道，我不想让我的东西传到那里去。Speaker 1: So I'm the wrong guy to ask this question because like this is not my like security of data and you know, like this is a total CTO question and so I have a great CTO and like he's the guy who would let handle it and he would sort of like be telling us. I would be very nervous about any sort of internal data. Like listen, you don't want anything to get out as long as you can kind of I don't even know what the terminology is I'll sound like an idiot in front of all you engineers but like, you know, I don't want my stuff to go there.

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说话者 1：你告诉我那叫什么。Speaker 1: you tell me whatever that's called.

发言者 2：数据安全隔离。Speaker 2: Data security isolation.

发言人 1：对，没错。所以我会想确保你知道，这些都非常关键。我是说，我觉得我们接下来的方向是，很多事情都得先做。我想，Venu 和我一直把它称作差不多是 1.0 版本。Speaker 1: Yeah, exactly. So I would want to make sure all of that you know, is very critical. And I mean I think to like where we're going is like so much of like what needs to happen first is I think you know, Venu and I have been calling it like almost like this is like 1.0.

就像我们正在做自动化这部分。我们会把所有这些工具都加进去，也会逐步积累起所有数据。Is it's like we're working on the automation part. We're going to get all these tools in there. We're going to have all the data sort of built up.

但这样一来，我们就能真正开始做一些有意思的事情了，也可以利用这些工具帮助分析师明确：“你应该把时间花在这里，这部分会更有意思。”因为归根结底，投资管理的核心是——你看，投资管理不是做业绩前瞻，也不是搭建 DCF 模型。But then we can actually like really start doing interesting things and we can use the tools to help the analyst to say, "Here's where you should be spending your time. This is where it gets more interesting." because again, it's all about It's Look, investment management isn't about doing earnings previews. It's not about building DCFs.

不是关于那个。这是在赚钱。It's not about that. It's making money.

所以，要赚钱，你需要有不同的观点。要找到这些不同的观点，可以借助一些工具来识别，但工具本身并不是最终产出。所以，这才是真正关键的事情。任何你认为能成为你赚钱方式中独有优势的东西，都很重要。And so, you to make money, you need divergent views. And to find divergent views, these are tools that help you identify those types of things, but they're not the output. so, that's going to be like the real critical thing. So, anything, you know, that you feel like becomes proprietary in your way of making money.

你知道，投资管理中最难的事情之一就是寻找竞争护城河，比如寻找某种套利机会。这些机会通常持续不了太久。你知道，参与其中的都是非常聪明、积极进取、薪酬丰厚的人。Now, one of the hardest things in investment management is you know, competitive moats. like finding some sort of arbitrage. They generally last pretty short. You know, it's all very smart people, motivated, highly compensated.

当别人赚了很多钱时，其他人也会慢慢摸清门道，然后通过套利把利润空间抹平。And when someone else is making a ton of money, other people sort of figure it out and arbitrage gone.

当所有人都拥有相同模型时，优势从何而来Where edge comes from when everyone has the same models

发言者 2：一旦每家基金都拥有相同的模型，优势究竟来自更好的数据、更好的问题，还是更好的判断力？Speaker 2: Once every fund has the same models, does edge come from better data, better questions, or better judgment?

说话人 1：我觉得全部都是。我的意思是，最终每个人都会拥有相同的信息。所以关键就在于你如何提示它，以及你能多快地把人和机器结合起来。Speaker 1: I think all of it. I mean, like it's everyone is ultimately going to have all of the same information. So, it's all going to be about how you prompt it, how fast you're able to sort of like, you know, combine man versus machine.

就像我们过去一直以来的思路是，我们做了大量工作，而我觉得这些工作现在其实可以自动化。它并没有告诉我们：“嘿，去买这只股票。”它说的是：“嘿，这是件值得你花很多时间去研究的事。这是一个你需要深入学习的项目。”然后你就会去寻找机会，因为不是所有人都在关注这个领域。Like the way that we used to always think about is like we did this enormous amount of work that I think can actually be automated now. that it didn't tell us, "Hey, go buy this stock." It said, "Hey, this is something you should go spend a lot of time on. This is a project where you need to go and like really educate yourself. And that's when you go and you find something because not everybody is looking at that.

其他人都在看这些别的东西，大家也都有那一类东西，但你要找的是那根针。你会说：“这可能挺有意思的。如果我能……如果我能对这件事更有信心，而这事要是成了，我们就能赚很多钱。”Everybody else is looking at all this other stuff and everybody has that sort of thing, but you're looking for that sort of needle. You're saying like this might be interesting and if I can get myself if I can get more conviction around this and if this happens, we're going to make a lot of money.

事情就是这样。这就是所有实地工作的目的。接下来会发生的就是诸如此类的事情。And so that was it. That was the purpose of all of the field work. And so like what's going to happen is all those types of things.

所以，这会是关于人们如何利用这些工具，真正识别出那些可以成为特别项目的事情。如果人工智能直接就能做到，那我也不知道该说什么。我的意思是，你懂我意思吧？And so it's going to be about how people utilize the tools to really identify these types of things that we can become special projects. If AI can just do it, then I don't know. I mean it's you know what I mean?

我们还需要自己做什么呢？不过我觉得，这会是一种利用这些工具来弄清楚你还需要在哪些方面做更多研究，或者找些不一样的事情来做的方法，因为一个人能做的事情是无穷无尽的。所以就像你说的：“哦，我们只要变得很棒就行了。”What do we need us for? So but I think it's going to be you know a way to use the tools to to figure out where you need to do more research or find something sort of like different to do because there's an unlimited amount of things one can do. And so it's like you say, "Oh, we'll just get great.

“AI什么都能做。”但事实未必如此，因为我们得找到合适的事情，而关键就在于识别出这些事情。AI can do it all." And it's like that's not necessarily true cuz it's like we got to find the right things and it's all about identifying those things.

关键在于比其他人都更快地识别出他们。It's about identifying them fast before everybody else.

2027 年基金：自建还是采购？Build vs. buy for a 2027 fund

发言者 2：你创办了一家非常成功的对冲基金。如果你在 2027 年创办一只基金，你会把哪些东西列入自建清单，哪些列入采购清单？Speaker 2: You launched a very successful hedge fund. If you launch a fund in 2027, what's on your build list versus your buy list?

说话人 1：嗯，我肯定会买开普勒。我的意思是，那就是Speaker 1: Well, I'd obviously buy Kepler. I mean that's

发言者 2：我同意。我同意。Speaker 2: I agree. I agree.

说话人 1：Speaker 1:

说话人 1：Speaker 1:

发言人 1：这个嘛，我还不确定。我显然知道，大家的职责会有很大变化。我还是需要资深选股人员。Speaker 1: This one is I'm not sure yet. I Obviously, I know there's going to be a lot of changes in people's roles. I still need senior stock pickers.

我可能不需要那么多分析师，但或许会需要其他具备不同技能的人，来替代分析师这类岗位。比如调查记者、能够深入开展项目工作的人，或者不同类型的数据工程师。我也不太确定最终会是什么样。I may not need as many analysts, but I might need other people with different skill sets that might replace like an analyst type of role. Investigative journalists, people who can do like really deep project work. you know, maybe different sort of data engineers. Like it's I'm not 100% sure what it's going to be.

情况会非常不同。我们的组织结构大致是金字塔式的：一大批分析师向资深选股人员提供大量信息，资深选股人员再向几位投资组合经理（PM）和最上层的首席投资官（CIO）汇报信息。It's going to be very different. The way that our you know, kind of structure was it was a pyramid and it was like we had a ton of analysts presenting a ton of information to senior stock pickers. Senior stock pickers presented information to a couple of PMs and the CIO on top.

所以事情就是这样。我觉得它可以再垂直一些。And so that was it. Like I kind of see it being a little bit more vertical.

比如，我还是需要所有这些……我想要生产者。我想要创作者，你知道，就是那些真正在那里赚钱的人。我不知道自己是否需要那么多准备者。但我也可能会想要他们，因为我可能需要更多……我可能是在寻找更多专有信息，来帮我填充——你知道，喂饱这头野兽，或者给这台机器供料，因为这些东西有望让我——你知道，找到我想要的这些不同观点。Like I still need all of the I want the producers. I want the makers, you know, the people who are really there to make the money. It's I don't know if I'm going to need as many of the preparers. but I also like might want them because I might need more I might be looking for more proprietary information to help me fill you know, feed the beast or feed the machine because that's those are going to be things that hopefully allow me to you know, find you know, like these divergent views that I'm looking for.

小型基金与巨型基金Small funds vs. giant funds

发言者 2：很棒。那么你认为 AI 更能帮助谁？是一个能像 50 人团队那样运作的五人基金，还是一个能像五人团队那样运作的 5000 人基金？Speaker 2: Very cool. So who in your opinion does AI help more? A five-person fund that can act like 50 or a 5,000-person fund that can act like five?

说话人 1：所以这挺有意思的。每个人都需要理解的一点是，每家对冲基金都有不同的策略，无论是对冲基金、投资基金，还是任何进行投资的人……Speaker 1: So it's funny. The one thing that everyone needs to like get their heads around is every hedge fund has a different strategy whether hedge fund, investment fund, like any anybody who's investing

说话人 1：Speaker 1:

说话人 1：而且他们的投资使命也不一样。有些人会说：“我一年要把整个投资组合周转 9,000 次”，这就像日内交易，频率非常高；还有些人会说：“我买入后持有 5 年。”所以归根结底，关键在于你的策略是什么？Speaker 1: and they have a different mandate. Some people are I am going to turn over my book 9,000 times a year and it's just like day trading and constantly high frequency and some people are like I buy and hold for 5 years. And so it's all sort of about like what is your strategy?

每个人都能利用现有资源做更多事情。那么，这会带来什么呢？如果一个团队只有五个人，我想，因为人手有限，创意产出的速度会比较慢，所以策略可能会更集中。如果我有更多创意可供选择，那么理论上命中率应该会更高。Everyone's going to be able to do more with what they have. And so but what's that going to do? So if I take a five-person per team I would imagine because they only have five people there's slower idea velocity so it's probably going to be a more concentrated type of strategy. if I have more ideas to choose from, then in theory my hit rate should be better.

所以，比如说，好吧，我覆盖100只股票，投资组合里得有20只个股，那我选的就是20%。嗯，如果突然我覆盖的股票增加到200只，那其中就得有10%进入这20只个股的组合里。So, if I have like, all right, I cover 100 stocks, I got to have 20 names in my portfolio, so I'm picking 20%. Well, if all of a sudden I'm covering 200 names, then I've got 10% of them needs to be in the 20.

如果我跟踪400个标的，只有5%会进入那个投资组合。所以，我判断正确的概率……不过，对这400个标的的研究必须和对那100个标的一样扎实。这些就是这类事情。If I'm covering 400 names, I only 5% are going into that book. And so, the chances of me being right, but the research for those 400 has to be just as solid as the research on the 100. So, the those are the types of things.

现在，假设我是一家有 5,000 人的公司，或者类似规模的公司，那人可真不少。显然，你们可能是一家多策略机构，涉足各种资产类别，所以情况就很不一样了。但话说回来，对一家投资公司来说，创意的产出速度，以及储备中的好点子有多少，至关重要。比如说，当某个策略奏效时，你首先想做的就是把它推出去，这样就能腾出位置，放进一个新的、同样有效的策略。Now, I'm a 5,000-person firm or whatever, and that's a lot of people. there's obviously there's probably you're a multi-strat, you're you're doing every asset class under the sun, so it's it's very different. But again, idea velocity and the number of good ideas you have in your pipeline is paramount for an investment firm. Like, when something works, the first thing you want to do is you want to get it out, so you can put something new in that works.

所以，就像一切都进展顺利的时候，每次有东西奏效，我就把它换成新的，然后把它去掉，再换成新的，然后我们——这就是我们，你知道的，随着时间推移打造阿尔法、创造回报的方式。And so, like when when everything's going perfect, every time something works, I replace it with a new thing, and then I get rid of it, and then I replace that with a new thing, and then we and that's how we, you know, build alpha and build build returns over time.

这就是我们一直想做的事情。所以，and that's, you know, what we were trying to do. So,

被过度炒作的概念：没有分析师的对冲基金The overhyped idea: the no-analyst hedge fund

发言者 1：Speaker 1:

发言人 2：太酷了。金融行业里，大家都很兴奋、但你觉得行不通的 AI 点子是什么？还有那个没人谈起、但你觉得能行的点子呢？Speaker 2: Very cool. What's an AI idea that everyone in finance is excited about that you think won't work, and the one no one's talking about that you think will work?

说话人 1：嗯，我倒希望自己知道那个没人谈论的东西，这样我就能赚点钱了。不过，大家多少都在谈论的，是这种没有分析师的对冲基金。你知道，就是“哦，我要弄一大堆 AI 智能体”。我确实相信，它们将能够复制一年前存在的某只基金。Speaker 1: Well, I wish I knew the thing that nobody's talking about, cuz then I can make some money. But the thing that everybody's sort of talking about is this no-analyst hedge fund, you know, oh, I'm just going to have all these AI agents. What I do believe is that they will be able to replicate a fund that existed a year ago.

它就是不会比明年的对冲基金那么好。所以我只是不认为真的能把人类完全排除在整个流程之外。而当你能从根本上完全把人类排除在外时——你知道，量化基金几十年来一直都把人类排除在决策流程之外。所以，这不是什么新鲜事。It's just not going to be as good as what today's hedge funds are going to be next year. And so, I just don't think it's ever really I don't think you're ever going to really be able to take humans out of the loop. and when you can take humans out of the loop completely on a fundamental and a you know, quantitative funds have taken human out of the loop for decades. So, that's that's nothing new.

不过，从基本原理的角度来看，当你能把人类从流程中移除时，那么，嗯，最终就会是拥有最多算力、最多……最多信息的一方胜出。But, from a fundamentals perspective, when you can take the humans out of the loop, then well, then it's just going to be whoever's got the most compute, the most the most information.

所以，我只是觉得事情不会那样发展。So, I just don't think that's how it's going to go.