史黛西·明茨——将定性基本面转化为定量因子（S7E33）

嘿，大家好。

Hey, everyone.

我是科里·霍夫斯坦。

Corey Hoffstein here.

在开始之前，如果你对资本效率和更聪明的投资组合构建感兴趣，请记下这个日期。

Before we get started, if you're into capital efficiency and smarter portfolio construction, mark your calendar.

第二届年度收益叠加研讨会将于2026年10月28日在芝加哥举行，由AQR的创始人兼首席投资官克里夫·阿斯内斯领衔。

The second annual return stacking symposium is coming to Chicago on 10/28/2026, headlined by Cliff Asness, founder and CIO of AQR.

这将是一整天的对话，讨论如何叠加收益流，更有效地利用你的资本，并重新思考你构建投资组合的方式。

It's a full day of conversations on how to stack return streams, put your capital to work more efficiently, and rethink the way you build portfolios.

无论你是顾问、资产配置者，还是只是对收益叠加感到好奇，这里都是你应该来的地方。

Whether you're an advisor, allocator, or just curious about return stacking, this is the room to be in.

请访问 www.returnstacked.com/symposium 了解更多信息并抢占席位。

Head to www.returnstacked.com/symposium to learn more and grab your spot.

网址是 returnstacked.com/symposium。

That's returnstacked.com/symposium.

现在，我们开始正题吧。

Now on with the show.

好的，Stacie。

Alright, Stacie.

你准备好了吗？

Are you ready?

我准备好了。

I'm ready.

好的。

Alright.

三，二，一。

Three, two, one.

我们开始吧。

Let's jam.

大家好，欢迎收听。

Hello, and welcome, everyone.

我是科里·霍夫斯坦，欢迎收听《与模型调情》这档播客，我们在这里揭开帷幕，探寻量化策略背后的人性因素。

I'm Corey Hoffstein, and this is flirting with models, the podcast that pulls back the curtain to discover the human factor behind the quantitative strategy.

科里·霍夫斯坦是Newfound Research的联合创始人兼首席投资官。

Corey Hoffstein is the cofounder and chief investment officer of Newfound Research.

由于行业规定，他不会在这档播客中讨论Newfound Research的任何基金。

Due to industry regulations, he will not discuss any of Newfound Research's funds on this podcast.

播客参与者的所有观点仅代表个人意见，不代表Newfound Research的观点。

All opinions expressed by podcast participants are solely their own opinion and do not reflect the opinion of Newfound Research.

本播客仅供信息参考，不应作为投资决策的依据。

This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions.

Newfound Research的客户可能持有本播客中讨论的证券头寸。

Clients of Newfound Research may maintain positions and securities discussed in this podcast.

更多信息，请访问thinknewfound.com。

For more information, visit thinknewfound.com.

本期嘉宾是Stacie Mintz，PGIM量化解决方案的董事总经理兼量化股票主管。

My guest this episode is Stacie Mintz, managing director and head of quantitative equity at PGIM Quantitative Solutions.

Stacie在PGIM工作了三十三年，她见证了公司量化股票业务每一个关键时刻。

Stacie has spent thirty three years at PGIM, and she's been there for every defining moment of the firm's quant equity effort.

第一个策略源于客户挑战，要求超越指数投资；1999年决定放弃Barra，将风险模型内部化；经历了2007年8月的量化地震，以及金融危机后意识到，在拥挤的因子世界里，仅仅做一个量化是不够的，你必须成为一个与众不同的量化。

A first strategy born from a client's challenge to move beyond indexing, a 1999 decision to abandon Barra and bring the risk models in house, surviving the quant quake of August 2007 and the post GFC realization that in a crowded factor world, it's not enough to be a quant, you have to be a different quant.

我们深入探讨了PGIM的“基本面量化”标签在实践中的真正含义，从考察公司如何为增长融资的融资因子，到包含一个不常见的“关联”组的因子分类体系，而且与典型的量化机构不同，完全排除了动量因子。

We dig into what PGIM's fundamental quant label actually means in practice from a financing factor that asks how a company funds its growth to a factor taxonomy that includes an unfamiliar Linkages group and unusually for a quant shop excludes momentum entirely.

在后半部分，我们转向前沿领域，将董事会构成和创新等定性信号转化为系统性因子，构建能够实时评估COVID和AI等突发冲击的模型。

In the back half, we turn to the frontier, turning qualitative signals like board composition and innovation into systematic factors, building models that assess emergent shocks like COVID and AI in real time.

以及为什么Stacie将大型语言模型称为“火箭筒”，这些工具强大到足以摧毁已经有效的东西，这正是为什么你应该从洞察开始，然后才拿起工具。

And why Stacie calls LLMs bazookas, tools powerful enough to blow up what already works, which is exactly why you start with the insight and only then reach for the tool.

请欣赏我与Stacie Mintz的对话。

Please enjoy my conversation with Stacie Mintz.

Stacie Mintz，非常感谢你今天参加我的播客。

Stacie Mintz, thank you so much for joining me on the podcast today.

很高兴能和你一起聊量化股票的一切。

Excited to have you with us to talk all things quant equity.

我有一阵子没请到真正优秀的量化股票嘉宾了。

I haven't had a real great quant equity guest in a while.

这是个常谈常新的话题，但有时候我会经历一些阶段，现在很高兴能和你一起重新深入探讨。

It's a perennial topic, but sometimes I go through seasons and I'm excited to dive right back in with you.

所以，谢谢你来做客。

So thank you for joining me.

谢谢你邀请我。

Thank you for having me.

我觉得现在是做量化分析师的好时机，所以很高兴我们能一起聊这个。

I think it's an exciting time to be a quant, so I'm happy that we're getting together on this.

我有点偏见。

I'm biased.

我觉得做量化分析师一直都很令人兴奋，但特别是最近几年，我觉得变得更加令人兴奋了。

I think it's always an exciting time to be a quant, but particularly in the last couple of years, I think it's gotten a little bit more exciting.

很期待聊到所有这些话题。

Excited to touch all those topics.

不过我想，咱们还是先从头说起吧，因为你在PGIM已经待了，如果我没记错的话，刚过三十年。

Though we'll start, I guess, rewinding the clock because you have actually been with PGIM for, correct me if I'm wrong here, just a little over three decades.

你职业生涯一开始就在PGIM，这让你有了一个很棒的视角，能看到整个量化股票团队在PGIM内部是怎么一步步发展成今天这个样子的。

You started your career at PGIM and that gives you the wonderful perspective of getting to see how the entire Quantitative Equity Group has grown within PGIM to become what it is today.

而且你在我们之前的通话里跟我讲过一个故事，说最初的量化股票策略其实是源于PGIM最大的客户提出的一个挑战，他们要求你们超越单纯的指数跟踪。

And you told me a story in our pre call that sort of the original quant equity strategies started as a challenge to PGIM from your largest client requesting you to move beyond indexing.

我希望你能带我回顾一下，讲讲当时的背景。

And I was hoping you could take me back, maybe talk a little bit about that context.

另外，当时你和团队其他成员是看到了哪些证据，才让你们相信量化股票是可行的，是你们真正能做的事情？

But also at that time, what was sort of the evidence that you and the rest of the team were looking at that convinced you that quant equity was feasible, was something that you could really do?

那时候，我们是一家量化管理人，但主要专注于多资产策略，而这家客户正好是我们多资产客户之一。

At the time, we were a quant manager but focused on multi asset strategies and this happened to be one of our multi asset clients.

他们的主动管理人业绩表现不够稳定，没有达到他们期望的水平，而我们当时在为他们管理一只指数基金。

Their active managers didn't have the consistency of performance they were looking for and we were managing an index fund for them.

当时有很多关于行为偏差的讨论，这些偏差被用来解释市场上一些众所周知的异象，比如低市盈率效应和动量效应。

At the time, there was a lot of discussion around behavioral biases and how they were explaining some well known anomalies in the market such as, you know, the low PE effect and high momentum effect.

这些异常现象大家早就知道，但这时候投资者开始把这些讨论了数十年的行为偏差和这些异常联系起来。

They were anomalies that people were aware of, but this is where investors are starting to connect this idea of behavioral biases that had been discussed for decades to those anomalies.

比如投资者在选股时过于自信，或者有损失厌恶心理，或者锚定在自己买入股票的价格上，即使信息变化、股价跌破了也不肯卖。

Thinking about things like investors being overconfident in their ability to choose stocks, being loss averse or anchoring to the price that they bought a stock at and not being able to sell even if information changed and it went below.

这些不仅解释了这些异常——传统基本面经理几十年来一直在利用这些异常——还提出了一个想法：如果这是一个持续的异常，你能不能利用它？

Not only was that describing these anomalies, and these are anomalies traditional fundamental managers had been taking advantage of for decades, but it brought to light this idea that if it is a persistent anomaly, can you take advantage of it?

这正是这个客户感兴趣的。

And that's really what this client was interested in.

我们一向喜欢挑战，所以马上就投入进去了。

We always love a challenge, so we dove right in.

我们做了很多自己的研究，专注于这些偏差以及它们在哪些地方最明显。

And we did a lot of our own research that focused on those biases and where they were most relevant.

那么这些偏差对哪些类型的股票影响最大呢？

So what types of stocks were these biases most relevant for?

这就是我们带到市场上的一个框架，并在1999年写了一篇论文，叫《行为偏差、估值与主动管理》。

And so that was a framework that we brought to the marketplace and wrote a paper behavioral bias valuation and active management back in 1999.

但这个策略是在1996年推出的，到今天，我们的策略里仍然保留了很多当时的概念。

But this strategy was launched in 1996, and today, we still have a lot of those same concepts in our strategy.

市场变化很大，我们的策略也变化很大。

The market has changed a lot, and our strategy has changed a lot.

但那种核心的投资理念贯穿了我们这三十多年的历程。

But having that core philosophy to our process is something that's endured through those three plus decades.

你做过的一件事，我觉得在历史上相对于其他股票量化基金来说是独一无二的，或者至少比其他股票量化基金早得多，那就是你在1999年就转向了内部风险模型。

One of the things that you did that I think is unique in the course of history compared to other equity quants, or at least certainly did a lot earlier than other equity quants, is you actually moved to your own internal risk model back in 1999.

我知道大家都喜欢谈论阿尔法信号，但对于股票量化来说，风险模型真的非常重要。

I know everyone loves to talk about the alpha signals, but for equity quants, risk models are really, really important.

很长一段时间里，行业标准就是那种现成的Barra模型。

Sort of the industry standard for a long time was sort of this off the shelf Barra model.

但你的团队对此提出了反对意见，说，不，我们不想那样做。

But your team pushed back on that and said, no, we don't want to do that.

我们想自己构建。

We want to build it in house.

可以说，原因可能有很多种。

Arguably could be for a variety of reasons.

那你带我回顾一下当时的情况吧。

So can you take me back?

在我看来，那时候自己做风控模型的人还非常少。

That was very early in the game, in my opinion, of people doing this in house.

是什么促使你们做出这个决定的？

What drove that decision?

背后的推动因素是什么？

What was the motivating factor?

我们当时已经建立了一个我们非常有信心的阿尔法模型。

We had built an alpha model we had a lot of confidence in.

我们意识到，投资组合实际上是由阿尔法模型和风险模型共同决定的。

We realized that your portfolio is really the combination of obviously your alpha model and your risk model.

而风险模型对投资组合的影响非常大。

And the risk model really was having a big influence on the portfolio.

具体来说，我们有一些想要融入投资组合的见解。

And specifically, we had insights that we were trying to get into the portfolio.

其中一些见解，风险模型视为风险，就会把它们抵消掉。

And some of those insights, the risk model saw as a risk and would unwind them.

所以那时候，我们主要关注公司的成长特征，这和动量有点不同，还有价值特征。

So back then, it was really looking at growth characteristics of a company, which were a little different than momentum, and then value characteristics.

我们在模型里让它们保持平衡，也希望在投资组合中保持平衡，但风险模型只把价值部分视为风险并抵消掉。

And we had them balanced in our model, wanted them to be balanced in the portfolio, but the risk model was only seeing the value piece as risk and unwinding it.

结果我们的投资组合就失衡了。

So our portfolio wound up being imbalanced.

我们反复调整，努力让它运作，然后我们想，我们这是在干什么？

We fought it and fought it and made it work, and then we decided, what are we doing here?

我们只需要自己做这件事。

We just need to do this ourselves.

这确实帮了我们很多，因为随着信息改善和模型改进，显然这些现成的模型也改进了。

And this has really helped us through time because as information improved and models were improved, obviously, these off the shelf models improved.

我们能够掌控风险模型的演变，最终这让我们有机会真正定制风险模型，去应对我们真正认为重要的风险，并按照我们想要的方式去平衡它们。

We had control over how our risk model was evolving, and that in the end gave us the opportunity to really tailor that risk model to attack those risks that we really thought were important and balance them the way we wanted to.

拥有自己的风险模型，即使在今天，也真的帮助我们为客户进行定制化服务。

And having our own risk models really helped us with customization for clients even today.

所以，将其他想法、价值观或信息整合到我们的模型中很容易，因为我们对风险模型的细节了如指掌。

So it's easy to integrate other ideas or values or information into our model because we do have our hands all over the details of the risk model.

你在我们之前的通话中提到了一个我觉得很有启发性的例子，你说，如果你和竞争对手都同样喜欢两只股票，一个现成的模型可能会推动你们俩都集中投资其中一只。

You brought up what I thought was a pretty illuminating example on our pre call where you sort of said, well, what happens if you and a competitor both like two names, two stocks equally, an off the shelf model might nudge you both to concentrate into one of those names.

如果你把这种观点传播到整个量化生态系统或所有竞争对手中，最终每个人都会集中在同一只股票上。

And if you sort of propagate that view across the entire quant ecosystem or all the competitors, everyone ends up concentrating in the same name.

因此，那只股票可能变得比看起来风险大得多。

And so that name becomes potentially a lot riskier than it looks.

显然，对于所有的拥挤动态，我认为这可能与2007年8月的量化地震或我们现在所称的任何事件有关。

And obviously for all the crowding dynamics, I think that may have arguably been a factor related to like August two thousand and seven quant quake or whatever we're calling it nowadays.

你能跟我谈谈PGIM是如何利用自己的内部风险模型度过那段时期的，你们的情况如何，以及市场动态是怎样的吗？

Can you talk me a little bit about how PGIM went through that period with its own in house risk model, how things played out for you and how market dynamics looked?

这绝对是个优势。

It was definitely an advantage.

说到量化地震，一开始是某个量化对冲基金开始平仓，然后就像滚雪球一样，演变成了一场更大范围的去杠杆事件。

With the quant quake, you start with some unwinding of a quant hedge fund and then it snowballed into something that was a broader deleveraging event.

这确实凸显了量化策略之间的关联性，也让拥挤交易这个概念浮出水面。

It really did highlight the connectedness of the quant strategies and did bring to light this idea of crowding.

而我们的策略，表现比如果我们用那些现成模型要好得多，原因正是你刚才说的那些。

And for our strategies, we did much better than we would have done if we were in or using one of those off the shelf models because of exactly what you described.

有些东西因为历史数据看起来有风险，但这并不意味着你可以把它外推到未来。

Something that looks risky because of historical data, that doesn't mean you can extrapolate that into the future.

那也不意味着未来风险就会更低。

That means it'll be less risky going forward.

所以当时我们的风险模型更像是一个分散化引擎，让我们去分散那些特质风险，而不是试图预测哪只股票风险更低。

So what our risk model did at the time would be more of a diversification engine and have us diversify that idiosyncratic risk instead of trying to predict which stock was less risky.

因为用了不同的风险模型，我们自然就和别人有点不一样。

By using a different risk model, we were naturally just a little bit different than others.

所以像那样的事件显然也影响了我们，但它确实让我们更加确信，朝着自己的风险模型方向前进绝对是值得的。

So clearly an event like that affected us as well, but it did give us even more confidence that moving in the direction of our own risk model was definitely worth it.

你提到全球金融危机时期让你和团队意识到，在一个拥挤的因子世界里，引用你的话，‘仅仅做一个量化是不够的，我们需要做一个与众不同的量化。’

You described the GFC period is driving a realization for you and the team that in a crowded factor world, quote, it's not enough to be a quant, we need to be a different quant.

所以风险模型在一个维度上起作用。

So you have the risk model working on one axis.

也许我们可以稍微聊聊阿尔法模型，作为另一个差异化的维度。

Maybe we can talk a little bit about the alpha model for a second as being another axis of differentiation.

从那个时期走出来后，‘不同’对你们的研究议程到底意味着什么？

What did different really come to mean for the research agenda going forward coming out of that period?

我觉得每当有像全球金融危机这样的市场事件发生时，你总是需要回到起点，看看你的模型表现如何，并剖析哪些地方需要改变。

I think anytime there's a market event like the GFC, you always have to kind of go back to the drawing board and see how your model did and dissect what needs to be different.

那段时间也是聪明贝塔开始变得流行的时候。

This was also a period of time where smart beta was becoming a bigger thing.

所以投资者可以以非常低的成本通过ETF形式购买这些简单的风险敞口。

So investors can go out and buy these simple exposures in an ETF form at a very low cost.

所以如果他们愿意让我们管理他们的钱，我们真的需要做一些比简单的东西更了不起的事情。

So if they were going to allow us to manage their money, we really needed to do something bigger than what was simple.

所以我们抓住那个机会，真正专注于如何通过使用另类数据源来实现差异化，超越那些像动量和市盈率这样简单的因子。

So we took that opportunity to really focus on how to be differentiated using alternative data sources, looking beyond these simple factors that look at the biases like momentum or PE.

我们投入了大量精力去判断一家公司的质量，不只是看净资产收益率，而是从不同角度审视公司，以了解这家公司能否实现它计划中的增长，或者它是否是一家真正有价值的公司，还是一个价值陷阱。

We invested a lot in determining what a quality of a company looked like, so not just ROE, but looking at the company from different angles to understand whether this company could achieve the growth that it was planning to achieve or whether it was a real good value company or whether it was a value trap.

所以我们花了很多时间研究更多质量因子。

So we spent a lot of time looking at more quality factors.

但我认为更重要的是我们的研究议程是如何演变的。

But I think what was more important is how our research agenda evolved.

我们当时看的不仅仅是可能的阿尔法来源，还有那些非常差异化、可扩展且可行的东西。

We were really looking at things, not just probable alpha sources, but also things that were very differentiated and that were scalable and feasible.

所以我们现在每年十二月都会举办一个活动。

So what we have today is actually an event that we have every year in December.

我们称之为“研究鲨鱼坦克”，我们所有人聚在一个房间里，研究员们站起来，像鲨鱼坦克那样推销三四个想法。

We call it our research shark tank, where we all get in a room and our researchers get up and they pitch three or four ideas in kinda like a shark tank situation.

我们都有机会提问、挑毛病、挑战他们。

And we all get a chance to ask questions, to poke holes, to challenge them.

我们的研究议程就是这样形成的。

That's how our research agenda comes together.

但我们真正想做的是寻找那些符合我们理念、可扩展、可行、有差异化、且相关的想法。

But what we're really trying to do there is look for things that align with our philosophy, are scalable, are feasible, are differentiated, are relevant.

我觉得在这样的环境里，团队每个人都能提出意见，共同想出那些不同的点子，这真的很重要。

And I think an environment like that where you're getting input from everyone in the team to come up with those different ideas is really important.

从最资深到最初级的研究员，还有投资组合管理团队，大家都会参与进来。

The most senior to the most junior researcher along with the portfolio management team all chimes in.

我觉得这样才能产生一些真正好的想法，最终让你和其他量化策略区分开来。

And I think that's how you get some really good ideas that will ultimately make you different from other quant strategies.

我想深入探讨一下这个理念，因为PGIM把自己描述为一家基本面量化公司。

I wanna drill down into that philosophy idea for a second because PGIM describes itself as a fundamental quant shop.

很多公司可能也会标榜这个标签，类似“量化基本面”之类的。

There's a lot of firms that might claim that label, something like quantamental.

所以我真的很想了解，当你说基本面量化而不是纯量化时，你觉得真正的区别在哪里？

And so I really want to get an understanding of when you say fundamental quant versus just quant, where is the real distinction lie to you?

是在你使用的信号类型上，还是在验证这些信号与基本面故事的过程上，或者是你瞄准的时间框架上？

Is it in the types of signals you use, the process of validating those signals versus a fundamental story or the time frame you're targeting?

这对你来说到底意味着什么？

What does that really mean to you?

我们试图做的是找到那些好公司，任何投资者都会想投资的公司。

What we're trying to do is find companies that are just good companies that anybody would want to invest in.

它们有良好的增长前景。

They have good growth prospects.

它们质量很高。

They're high quality.

它们的价格合理。

They're reasonably priced.

这很大程度上是在模仿传统选股经理的做法。

It's very much trying to replicate what a traditional stock picker manager would do.

在这样做的时候，我们确实希望模型里的每一个因子都有扎实的理论基础。

In doing that, we're really looking for every factor in our model to have a really solid theoretical underpinning.

原因在于，如果我们发现一个有效的信号想加进模型，我们得明白它为什么有效。

And the reason for that is because if we find a signal that works that we wanna add to our model, we wanna understand why it works.

这样我们才能确信这是个好因子。

That will solidify that we think that this is a good factor.

我们理解它为什么有效。

We understand why it works.

我们也知道它在不同的市场周期中什么时候有效，这样我们就有信心这个因子未来也会继续有效。

We understand when it works in different market cycles, and it gives us confidence that that factor will work in the future as well.

所以我们觉得模型里的空间非常宝贵。

So we find that space in our model is very valuable.

如果你已经有很多好因子，还想再加一个，那就会挤占其他因子的空间。

If you've got a lot of good factors and you're looking to add something, you're taking away space from something else.

而且你希望模型里的各个部分能相互配合，在不同市场环境下都能很好地平衡。

And you want your model components to all work in harmony with each other and balance each other well in different market conditions.

所以，说了这么多，作为基本面管理者，我们不会去碰那些回测表现不错但不太明白为什么有效的想法。

So all of that being said, being a fundamental manager keeps us away from some of these ideas that maybe backtest well.

你不太清楚它们为什么有效，放进模型里，它们占用了其他因素的空间，而且将来可能就不灵了。

You're not quite sure why they work, you put them in the model, they're taking space away from something else, and then maybe they may not work in the future.

所以我们有一份精心筛选的因素清单，每个都有真正的逻辑支撑，如果我跟你解释清楚，你肯定会说，哦，这有道理。

So we have a very carefully curated list of factors that have true rationale to them that if I explained to you exactly what it was, you'd say, oh, that makes sense.

我觉得这让我们有信心，我们正在构建一个稳健且合理的模型。

And I think that gives us comfort that we're building a resilient model that makes sense.

在市场压力大的时候，你知道你投资组合里的公司都是好公司，它们应该能扛得住。

And in periods of stress, you know the companies in your portfolio are good companies and that they should be resilient.

也许我们可以用一个例子来说明这个观点。

Maybe we can ground this idea with an example.

在之前的通话中，你提到其中一个因素是融资因素。

And in our pre call, you mentioned one of these factors is the financing factor.

这个因素挺特别的。

This was sort of a unique one.

这是我第一次听到有人讨论这个想法。

One of the first times I've heard anyone talk about this particular idea.

我希望你能解释一下这个因子是什么，以及它如何体现基本面量化与普通量化之间的区别，甚至可以说，它在实践中是怎么运作的。

I was hoping maybe you could walk through what that factor is and how it might illustrate the distinction between being a fundamental quant and a generic quant, dare I say, and how that actually looks in practice.

这是金融危机后我们模型新增的一个因子，目的是评估公司的质量。

This was one of the additions to the model after the GFC and trying to look at a quality of a company.

我们不只是看一些基本质量指标，而是关注公司如何为其增长融资。

Instead of just looking at some basic quality metrics, we were interested in the concept of how a company finances its growth.

所以，如果在一个行业里有一组成长型公司，其中一些通过内部现金流来支持增长，我们的研究显示，这些公司往往比那些不断从市场融资的公司表现更好。

So if you've got a group of growth companies in an industry and you've got companies that finance their growth through organic cash flow, what our research showed is that those companies tend to outperform ones that consistently tap the markets to get more capital.

背后的逻辑是，公司通常面临边际回报递减的机会。

And the rationale behind that was that companies typically have opportunities with diminishing marginal returns.

而一家能轻易获得外部融资的公司，可能会倾向于投资质量越来越低的项目。

And a company that has abundant access to external financing might be tempted to fund increasingly lower quality projects.

相反，受限于内部现金流的公司，必须认真思考真正该投资什么，什么对公司真正有意义。

Whereas a firm that is bound by its internal cash flow will really have to face the decisions on what do we really want to invest in, what really makes sense for this company.

这个概念挺有意思的，我们做了统计检验之后，效果非常好。

It was an interesting concept, and when we tested it statistically, it worked very well.

这也是一个很好的例子，说明因子是随着时间演变的。

This is a good example too of a factor that's evolved over time.

我们一开始用的方法非常简单。

We started with a very simple approach to this.

这么多年过去了，这个概念还在我们的投资组合里，但随着信息越来越多、技术也越来越好，这个因子已经进化了很多。

And over the years, we still have that concept in the portfolio, but the factor has evolved quite a lot as there's more information and better techniques to use to get at that concept.

我最喜欢问股票量化研究员的问题之一，就是他们投资组合里用的特征或因子是怎么分类的。

One of my favorite questions to ask equity quant is about the taxonomy of their characteristics or the factors they use in their portfolios.

我觉得你跟一个典型的股票量化研究员聊，最后都会聊到价值、质量、动量，可能还有防御性，不管是统计上的还是更偏基本面的质量。

I think when you talk to your typical equity quant, you're going to end up with sort of a hierarchy of value and quality and momentum and maybe defensive, whether that's statistical or more fundamental quality.

但你也会遇到一群更偏统计或价格属性的研究员，不过跟基本面量化研究员聊的时候，情况不一定就是这样。

But you do have a cohort of what are more statistical or price based properties often, not necessarily the case when you talk to a fundamental quant.

所以我很好奇，你们内部是怎么组织这些因子的？

So I'm curious, how do you think about organizing your factors internally?

你们用的分类体系是什么样的？每一类主要是想捕捉什么？

What's the taxonomy that you use and what's each group really meant to capture?

我们总体上是在寻找那些增长前景好、质量高、价格合理的公司。

We're looking for those companies at a high level that have good growth prospects, are high quality, and reasonably priced.

所以我们确实有分类。

So we do have categories.

有意思的是，当我们谈论这些分类时，听起来挺简单的。

What's interesting is when we talk about these categories, they sound kind of simple.

但模型本身其实非常复杂，各个部分之间有很多交互，所以分类有时候真的只是为了组织讨论我们的策略，以及从业绩和归因的角度跟客户分享信息。

But the model itself is really complex, and there's so much interaction between these individual pieces that the taxonomy sometimes is really just used for a sort of organization to talk about our strategy and to share information with clients performance wise, attribution wise.

我们用的分类包括增长。

The groups that we use are growth.

我们想找的是有良好增长潜力的公司，利用投资者对信息反应不足或者信息传播缓慢的机会。

So we're looking to find companies that have good growth potential and take advantage of investors that are under reacting to new information or take advantage of slow information diffusion.

我们关注关联性，对我们来说这是看待公司增长的另一种方式，试图理解信息如何从一家公司流向另一家公司，以及一个冲击如何影响其他公司，无论是正面还是负面的冲击。

We're looking at Linkages, which for us is another way of looking at the growth of a company and trying to understand how information is flowing from one company to another and how a shock to one company might affect other companies, whether it's a positive or negative shock.

我们看质量，也就是公司经营得怎么样。

We look at the quality, so how well is a company run?

管理层是否稳固？

Does it have solid management?

它是否对其他公司构成竞争威胁？

Is it a competitive threat to other companies?

期权市场有没有什么信号，暗示这里存在令人担忧的问题，而我们的模型在其他地方没有捕捉到？

Is there something in the options market that's signaling something is concerning here that maybe our models aren't picking up elsewhere?

所以，就是从其他有见识的投资者那里获取信息。

So getting information from other informed investors.

然后当然还有估值，确保公司定价合理，或者看起来被低估、被忽视。

And then of course, valuation, looking to make sure that companies are reasonably valued or maybe they look undervalued, underappreciated.

这些类别——增长、价值、关联、质量——的一个重要特点是，我们会根据所评估的公司类型，动态地使用它们。

An important part of these groups, the growth, the value, the linkages, the quality is that we use them dynamically based on the kind of company we're evaluating.

对于增长更快、更年轻的公司，我们会更倾向于那些增长型的因素。

For companies that are much faster growing, younger companies, we're gonna tilt more towards those growthier factors.

但如果是成熟稳定的公司，我们就会更关注估值因素，因为这时候你买的不是未来的增长，你的回报来自于成熟收入流的实现，而且是以折扣价获得这个收入流。

Whereas if you're a mature stable company, we're really gonna focus more on those valuation factors because there, you're not paying for some future growth, your return is going to come from the realization of a mature income stream and getting that income stream at a discount.

我猜我们的听众可能对成长、价值和质量这些概念已经有了相当固定的认知模式。

I would suspect that our listeners likely have pretty strongly established mental models for generally what growth and value and quality mean.

关联性这个概念不太常被提到，对很多人来说可能是一个比较新颖的因子类别。

Linkages is one which doesn't get spoken about very often and likely is a novel factor category for a lot of folks to think about.

我希望你能花点时间讲讲，关联性这个想法到底捕捉了什么，为什么它能在这些比较经典的因子分类中占有一席之地？

I was hoping you could spend a little time there discussing what does this idea of Linkages capture and why does it earn a place among these more sort of classical factor groupings?

如果你看我们的成长因子和相关的洞察，我们其实是在直接看公司本身。

If you look at our growth factors and our insights there, we're really looking at the company directly.

我们是在寻找那些能告诉我们关于这家公司及其增长前景的信号。

And we're saying what are signals that are telling us something about this company, about its growth prospects.

而关联性所做的，就是尝试从另一个角度来切入。

What Linkages is doing is just trying to come at that from a different angle.

也就是说，它关注的是这家公司与市场上其他公司之间的联系。

So it's saying how is this company connected to other companies in the marketplace?

那这怎么能给我们带来信息优势呢？

And how can that give us an informational advantage?

也就是说，市场某个地方出现了一个冲击，这个冲击会逐渐传导开来。

Meaning, there's a shock somewhere in the market and that shock is going to trickle through.

举个简单的例子，比如某个行业。

Now a simple example of this is like an industry.

你可以想象，如果某个行业发生了什么事，整个行业都会跟着一起动。

So you can think if something's happening industry, you're gonna see this whole industry move together.

而且行业内发生的冲击，确实会影响到该行业的每一家公司。

And there are shocks that happen within an industry that truly do affect every company in that industry.

这是一种更精细的做法，因为现在各行各业、各个国家之间都紧密相连。

This is a more sophisticated way of doing that because these days, everything is so interconnected across industries, across countries.

你会发现与其他公司之间那些不那么明显的联系。

You're finding other linkages to other companies that are not as obvious.

一个例子就是客户与供应商之间的关系。

One example is customer supplier relationship.

所以如果你有公司A，而公司A发生了某种冲击，比如说非常正面的消息，那么你会预期公司A的供应商会从中受益。

So if you've got company a and a shock happens to company a, say something really positive, you would expect the suppliers to company a to benefit from that.

这种影响可以往下传导好几层。

That can go several layers down.

那些供应商的供应商应该也会表现得非常好。

The suppliers to the suppliers of that company should do really really well.

所以你可以描绘出不同的关系，甚至超越客户与供应商的关系。

So you can map out different relationships even beyond customer supplier relationships.

你可以衡量这些关系的强度，从而衡量新信息对不同公司的影响有多大。

You can measure the strength of those relationships so that you can measure how impactful that new information may be across different companies.

我们发现这其实是另一种映射信息及其如何影响市场中不同公司的方式。

We found that this is kind of another layer of mapping information and how it's affecting different companies around the market.

在你的分类中，你明显没有包括动量，这也许不令人意外，因为你们是基本面量化公司，动量可以说是一个非基本面的指标。

Now in your taxonomy, you notably did not include momentum, which maybe isn't a surprise being a fundamental quant shop, momentum being arguably a non fundamental measure.

但对于量化公司来说，这并不常见。

But it is unusual for a quant shop.

而且你确实经常看到，即使是基本面量化公司，也会想办法以某种方式引入动量，哪怕只是在交易和执行层面。

And you do often see that even fundamental quant firms do find a way to introduce momentum in some way or another, even if it's just in trading and execution.

我很好奇你怎么看，把动量排除在外会不会损失什么，还是说它已经被你关注的其他基本面因子所覆盖了？

Curious as to your thinking about is there anything lost in leaving momentum out or is it captured through some of these other fundamental factors you look at?

它确实被很多其他基本面因子覆盖了。

It is captured through a lot of the other fundamental factors.

但我们没有在我们的模型中包含或自动包含价格动量的原因之一是，作为基本面量化，价格动量有很多元素与你评估的公司相关，但也有许多元素与公司无关。

But one of the reasons that we did not include or automatically include price momentum in our models is because as a fundamental quant, price momentum has a lot of elements to it that are related to the company that you're evaluating, but there are a lot of elements to it that are not related to the company.

所以它可能是……

So it could be...

你刚才提到的，比如一个行业冲击，可能影响也可能不影响那家公司。

You were just talking about, like, an industry shock that may or may not affect that company.

它可能是投机。

It could be speculation.

我们就称之为噪音吧。

Let's just call it noise.

所以你看，一家公司的股价变动。

So you've got the price movements of a company.

其中一部分跟这家公司有关，另一部分纯粹是噪音。

Some of it is relevant to that company, and some of it is just noise.

所以我们做的是，通过观察这些公司实际发生的事情，把相关的那部分提取出来。

And so what we do is we pull out that relevant piece by looking at things that are actually happening to those companies.

也就是这家公司的关键事件，以及股价围绕这些事件如何波动。

So key events for that company and how is the price moving around that event.

也就是说，投资者对这家公司特有的事件反应如何？

So how are investors reacting to that event that's specific to that company?

然后忽略那些相关性较低的股价波动。

And then ignore all of the other price movements that they're less relevant.

通过从价格动量中提取出信息部分，把噪音剔除掉，你就能得到更丰富的信号。

And by calling out that informational piece from price momentum and leaving the noise out of it, you get a much richer signal.

所以我们发现，我们称之为信息动量的指标，长期来看和价格动量一样有效，但它的崩盘风险要小得多，下行风险大约只有一半。

So we find that our measure, we call it information momentum, is as effective over time as price momentum, but it has a lot less of the crash risk, so about half the downside.

所以我们在构建因子的时候，不仅关注基本面，还会尽量让这个因子在风险和收益的权衡上尽可能有吸引力。

And so when we are building factors, not only are we focusing on the fundamentals, but we're also looking at making that factor as attractive as we can from a risk and return trade off.

所以我们的alpha模型里本身就包含了风险控制的部分，我们会尝试各种调整，仔细研究所有细节，从风险和收益两个角度来打造最好的因子。

So we have an element of risk control built right into the alpha model, trying to find the adjustments that we can make, looking at all the detail to get the best factor we can from both the risk and return perspective.

你提到这个策略最早是在1996年上线的。

You mentioned that this strategy first went live in 1996.

那也就是说，我们有三十年的实盘表现和三十年的研究经验。

So we're talking thirty years of live performance and thirty years of doing research.

而且你们肯定反复研究过篮子里的那些个股。

And surely you've looked at some of the same individual names that have been in that basket.

肯定有八千种、一万种不同的方式。

It must be 8,000, 10,000 different ways.

说实话，你觉得真正独立的信号能有多少个，最终能推动公司基本面的变化？

Realistically, how many truly orthogonal signals do you think exist for things that ultimately move company fundamentals?

可能比你想的要少。

It's probably less than you would think.

我们的模型，根据不同地区，大概有25到40个信号，或者我称之为概念，因为很多信号包含了很多组成部分。

Our models, depending on the region, have maybe 25 to 40 signals or I would call them concepts because a lot of them have a lot components to them.

而且每一个，就像我提到的，都是精心设计的，并且因为它们在彼此之间以及在不同市场中都有益处，所以被纳入模型中。

And each one, as I mentioned, is carefully designed and are in the model for a real reason because they have a benefit alongside each other and through different markets.

我们希望确保模型中的所有信号都在并且有影响力。

We wanna make sure all of the signals in our model are there and have an impact.

如果你的模型里有400个信号，那每个信号真正贡献了多少？你又怎么能真正剖析出是什么在驱动你的表现呢？

If you have a model with 400 signals in it, how much is each one really contributing, and how can you really dissect what is driving your performance.

所以这对我们来说也很重要。

So that's something that's important to us as well.

让有意义的权重与经济基础挂钩，对我们来说，数字更接近四十而不是四百。

Having a meaningful weight being tied to an economic underpinning, for us, the number is more in the forties as opposed to the four hundreds.

我想谈谈你们最近研究的一些领域。

I want to talk about some areas in which you've been leaning into for recent research.

其中一个领域就是信息如何在市场中流动，试图在公司真正出现在传统的损益表和资产负债表数字中之前，提前获取对公司的了解。

And one of those areas has been how information flows through the market, trying to get a read on a company before it actually shows up in those traditional income statement balance sheet type numbers.

既然目标和方向是这样，我很好奇想问你一个问题，这个问题我最近也问过不少嘉宾，那就是你觉得现在最大的Alpha信号来源在哪里？

Given that goal and objective, I'm curious to ask you a question I've been asking a number of guests lately, which is where do you think the largest source of Alpha signal is living today?

是获取那些别人可能接触不到的独特数据吗？

Is it in accessing unique data that maybe no one else has access to?

还是从这些数据中挖掘出独特的特征，你懂的，对吧？

Is it engineering unique features from that data, you know, right?

用不同的视角看数据，或者用独特的方式去分析它？

Getting a different look at the data or looking at it in a unique way?

还是说，用大家都有的数据和特征，去更准确地预测，或者开发更复杂、更完整的预测模型？

Or is it in actually forecasting more accurately or developing more complex or more complete forecasting models from the same sort of data and features everyone else has?

我觉得以前是数据。

I think it used to be the data.

我觉得以前是那些能拿到独特数据渠道的人。

I think it used to be people getting that unique access.

但现在，我觉得数据基本上大家都能拿到了。

But these days, I think the data is pretty much available.

不过数据很贵。

Data is expensive though.

所以作为PGIM这样的大型机构的一部分，能够大规模获取这些数据很重要。

So being part of PGIM, which is a very large organization, being able to get that data at scale is important.

所以我认为这是我们的一个优势。

So I think that's an advantage that we have.

但如今，我认为光有数据还不够。

But today, I think the data alone isn't enough.

你必须能够利用这些数据。

You have to be able to take that data.

第一，要筛选出哪些是真正相关的，哪些不是。

Number one, sort through what's really relevant and what isn't.

我的意思是，几十年前，很难找到数据源。

I mean, decades ago, it was hard to find data sources.

现在每天都有人给我发邮件，说他们有什么新数据源，但你真的要仔细辨别哪些非常有价值，哪些没有。

Now people are emailing me every day with a new data source they have, but you really have to ferret out what is very valuable and what isn't.

但我觉得更重要的是你拿这些数据来做什么。

But I think what matters more is what you do with that data.

所以，你怎么把它变成一个能补充你模型的因子呢？

So how do you turn this into a factor that is going to be a complement to your model?

所以你得想清楚用什么工具来分析这些数据。

So you gotta figure out what kind of tools you're gonna use in order to analyze this data.

你得想好怎么处理这些数据。

You have to figure out how you're gonna transform the data.

你要不要做标准化？

Are you going to normalize it?

你考虑多长的时间跨度？

What time horizon are you going to consider?

你要不要按时间加权？

Are you gonna time weight the data?

数据缺失的时候你怎么处理？

How do you deal with missing data?

对我们来说，这些小小的决定都会产生影响。

To us, all of those little decisions make a difference.

所以你得花时间仔细做出这些决定，而不是随便试几个版本，看看哪个看起来最好。

And so you've got to take the time to go through and make those decisions thoughtfully, not just try a bunch of different versions and see what looks the best.

所以关注这些细节很重要。

So paying attention to those details is important.

我觉得这也取决于你怎么使用这些因子。

I think it's also how you use those factors.

我之前提到过，我们策略的一个原始概念就是在因子最有效的地方使用它们。

So I'd mentioned before that one of our original concepts for our strategies was using factors where they're most effective.

对我们来说，这个决定是在公司层面做出的。

And for us, we make that decision at the company level.

所以，对于增长较快、处于生命周期早期的公司，我们会更看重那些增长因子。

So again, faster growing companies who are earlier in their life cycle, we're going to wait those growth factors more.

而对于更成熟的公司，我们会更看重那些估值因子。

And for more mature companies, we're going to wait those valuation factors more.

这确实是在公司层面做出的决定。

And that's really at the company level.

这真的增强了我们因子的效力。

That really increases the power of our factors.

像这样的细微差别和深思熟虑的方法，当你只考虑投资101时，其实很直观，但确实会产生很大的不同。

Nuances like that and thoughtful approaches like that that are pretty intuitive when you think about just investing one zero one really do make a difference.

所以我认为，重要的不仅是你拥有的数据，还包括你如何使用这些数据，以及你在对不同股票做决策时，在哪些地方应用这些因子。

So I think it's not just the data you have, but how you use the data and then where you use those factors in your decision making for different stocks in the market.

你能稍微深入讲讲对不同公司采用不同权重因子评分这个想法吗？

Can you dive a little bit into that idea of weighting those factor scores on different companies differently a little bit?

我很好奇，这个过程有多少是主观判断，又有多少是完全系统化的，而且你是从哪里找到证据来证明这种方法是合适的？

I'm curious how much of that process is discretionary versus purely systematic and where you derive the evidence from that that's the appropriate approach to take.

这完全是系统化的，没有任何主观判断。

It is all systematic, not discretionary at all.

这个概念源于对一只股票的基本评估。

The concept comes from the idea of just evaluating a stock.

所以股票的价格主要由两个因素决定。

So the price of a stock is going to be driven by mainly two components.

你买一家公司是因为它未来的增长。

You buy a company because of the future growth of the company.

如果公司增长强劲，股价就会上涨。

And and if the company has strong growth, that price is going to go up.

你会获得未来的收益。

You'll get future earnings.

公式的第二部分是现有的收益。

The second part of the equation is the existing earnings.

公司相对于现有收益的定价是否合理？还是说它是一只价值股或折价股？

And is the company priced appropriately for the existing earnings or is it a value stock and a discount stock?

如果你看的是快速增长的公司，你更应该关注未来的增长前景，而不是股价是否昂贵。

If you're looking at a fast growing company, you wanna pay attention to future growth prospects much more than whether that stock is expensive.

现在行业里有很多股票，相对于它们当前的收益来说可能看起来很贵，但它们的增长潜力巨大，这正是你买这些股票的原因。

Can think of a lot of stocks in the industry right now where they might look horribly expensive relative to their current earnings, but their growth potential is huge and that is why you're buying that stock.

所以我们的模型在看这类股票时，会更像成长型基金经理那样操作，不会太担心股价贵不贵。

So our models want to act more like a growth manager when we're looking at that stock and not really worry too much about how expensive it is.

另一方面，当你看一家更成熟的公司时，即使管理层吹嘘说马上会有惊人的增长，这真的会误导投资者，人为抬高股价，或者忽视公司过去的坏消息，导致股价被压得很低，更像一只价值股。

On the other hand, when you're looking at a more mature company, even if you're hearing hype from management that maybe there's some super wow growth coming around the corner, that can really trick investors to bumping up prices artificially or discounting historical bad news for a company and it's really beaten down and it's more of a value stock.

对于这些更成熟的公司，我们要对价格更敏感。

For those more mature companies, we wanna be more price sensitive.

我们要寻找便宜货。

We wanna look for bargains.

质量仍然重要，但未来增长这部分就没那么关键了。

Quality is still important, but this future growth piece is not as important.

所以在那个极端，我们要更像价值型基金经理那样操作。

So on that end of the spectrum, we wanna act more like a value manager.

所以当我们把股票组合起来时，我们会选那些虽然贵但有良好增长前景的股票。

So when we're going to pull our portfolio together of stocks, we're having stocks we like because even though they're expensive, they're good growth prospects.

这些就是我们核心投资组合中偏成长的股票。

So those are the growthier names in our core portfolio.

在价值股这边，那些可能……

On the value side, those maybe...

它们在成长性方面不怎么样，但是很划算。

They're not great on the growth side, but they're good bargains.

所以它们才会被选进来。

And so that's why they're there.

这其实就是把一个基本的经济学概念应用到个股层面。

It's taking a basic economic concept and applying it down at the stock level.

实际操作中，我们会评估一家公司的增长率相对于我们关注的整个市场的情况，然后根据它在那个区间里的位置，调整成长和价值因子的权重。

So in practice, what happens is we evaluate a company's growth rate relative to the universe that we're looking at, and then we adjust the weights on the growth and value factors based on where they fall along that spectrum.

所以每天在个股层面都在做这件事。

So that's happening every day at the stock level.

当我们把股票放进投资组合并汇总起来，你会在成长和价值因子之间得到一个很好的平衡，但这些平衡来自组合中不同类型的股票。

Now when we put the stocks in the portfolio and aggregate it up, you'll have a nice balance between your growth and your value factors, but they're coming from different types of stocks in your portfolio.

在过去几年里，我们经历了一些突发性的基本面冲击的例子。

In the last couple of years, we've had these examples of emergent fundamental shocks.

我们经历了新冠疫情，不确定是否该称之为迷因股狂热或基本面冲击，但最近更明显的是迷因股狂热，我觉得AI就是新兴基本面冲击的一个很好的例子。

We had COVID, not sure whether we'll call them meme stock mania, fundamental shock, but Meme Stock Mania more recently, I think AI is a good example of an emergent fundamental shock.

这些可以说是一种制度性变化，用一套标准的静态风险因素来捕捉它们，可能确实比较困难。

And these are kind of a regime change that's arguably difficult to capture with sort of a standard static set of risk factors.

在我们之前的通话中，

And in our pre call,

你谈到了如何让你的策略在面对市场冲击时更具韧性，找出市场中哪些部分最脆弱，并构建自定义因素来捕捉公司的创新性或前沿性，以及它们抵御不同类型市场冲击的能力。

you talked about trying to figure out how to make your strategy more resilient to when shocks hit the market, figuring out which parts of the market are most vulnerable and building custom factors that get at things like, well, how innovative or cutting edge a company is or how resilient they can be to these different sort of market shocks.

你能谈谈你是如何构建模型来实时评估新兴冲击的吗？

Can you speak a little bit about how you go into building models that can help assess emergent shocks in real time?

我们模型的目标是在不同的市场条件下尽可能产生稳定的阿尔法收益流。

The goal of our model is to produce as consistent of a stream of alpha as we can in different market conditions.

所以在构建模型时，我们真正关注的是韧性。

So when we're building the model, we're really focusing on resiliency.

也就是说，利用不同的阿尔法来源和不同类型的因素，这些因素在不同的市场条件下表现各异。

So using different sources of alpha and different types of factors that work different in different market conditions.

你可以通过使用多种数据源和不同类型的工具来评估我们之前讨论过的自适应权重方案。

And you can do this by using a variety of data sources, different types of tools that you can use to evaluate the adaptive weighting scheme that we talked about.

但关键在于从不同角度审视公司，并尝试在那些信息冲击真正反映到公司数据之前就理解它们。

But the key is looking at companies from different angles and trying to understand those information shocks before they really show up in the numbers for that company.

至于更大的市场冲击，我想从定义上讲，冲击本身就很难预测，因为它就是冲击。

As far as a larger shock to the market, I guess by definition, it's hard to predict a shock because it's a shock.

这不是任何人能预测的事情。

It's not something anybody can predict.

但我认为对管理人来说，重要的是在冲击发生时你如何应对。

But I think what's important for a manager is what you do during that shock.

拥有坚实的投资理念并坚持它很重要。

Having a solid investment philosophy and sticking with it is important.

我记得当新冠疫情来袭时，一切都天翻地覆，只有某些类型的股票表现良好。

I think about when COVID hit and everything was turned on its head and only certain types of stocks were doing well.

关键在于坚持你的投资理念，而不是把你的模型扔出窗外。

And the key is to stick to your investment philosophy, not to throw your model out the window.

你之前提到过经理的自主决策权。

You had mentioned before about manager discretion.

我觉得经理的自主决策权应该受到严格限制，因为这些模型都是我们一起构建的。

I think manager discretion needs to be pretty limited because we've all built these models together.

我们相信这些模型。

We believe in these models.

我们构建它们是为了长期稳健。

We built them to be resilient over time.

我们知道市场中有时候这些模型会失效，但我们也知道它们会恢复。

We know that there are times in the market when these models don't work, and we know that they will recover.

所以不要进去就乱来，把婴儿和洗澡水一起倒掉，或者因为个人偏见把事情搞砸。

So don't go in there and kinda throw the baby off with the bathwater or do something with your human biases to mess this up.

我们早就为这些情况做好了准备。

Like, we're prepared for these situations.

我们持有的是优质公司。

We own good companies.