杰克·福格尔——理论中的动量，实践中的动量（第一季第六集）

大家好。

Hey, everyone.

我是科里。

Corey here.

感谢收听《与模特约会》的又一期节目。

Thanks for tuning into another episode of flirting with models.

如果你喜欢这个节目，麻烦花点时间给个评分和评论，更重要的是，推荐给朋友。

If you're enjoying the show, I'd greatly appreciate it if you take a moment to rate, review, and most importantly, with a friend.

口碑传播才是这个播客成长的关键。

Word-of-mouth is how this podcast grows.

如果你想了解更多关于Newfound的收益叠加型共同基金、ETF和模型投资组合，欢迎访问returnstacks.com。

And if you'd like to learn more about Newfound's platform of return stacked mutual funds, ETFs, and model portfolios, head over to returnstacks.com.

好了，我们正式开始。

Now on with the show.

大家好，欢迎各位。

Hello, and welcome, everyone.

我是科里·霍夫斯坦，欢迎收听《与模型调情》，这档播客将揭开量化策略背后的真人故事。

I'm Corey Hofstein, 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 Hofstein is the cofounder and chief investment officer of Newfound Research.

由于行业规定，他在这档播客中不会讨论Newfound Research的任何基金。

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

节目中所有嘉宾的观点都仅代表他们个人，不代表Newfound Research的立场。

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的客户可能持有节目中提到的证券或资产。

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

如需了解更多，请访问thinknewfound.com。

For more information, visit thinknewfound.com.

今天，我采访的是精品ETF发行商Alpha Architect的联席首席投资官杰克·沃格尔。

Today, I am speaking with Jack Vogel, co CIO of boutique ETF issuer Alpha Architect.

我认识杰克很久了，这次特别邀请他来节目，主要有两个原因。

I've known Jack for some time now and was particularly excited to bring him on the show for two reasons.

第一个原因，你听一会儿就会明白——他对投资文献的了解几乎像百科全书一样全面。

The first, which you will quickly learn in the episode, is his near encyclopedic knowledge of investing literature.

我见过的投资人不多，能像他这样既广又深地记住学术研究和实战案例的。

I've met few investors who have both the breadth and depth of recall that he does for both academic and practitioner studies.

第二个原因，是他负责管理一种动量策略。

The second was because he helps manage a momentum strategy.

几乎每个投资者都曾翻过格雷厄姆的《聪明的投资者》，价值投资被大多数人视为像沃伦·巴菲特一边喝可乐一边吃苹果派一样纯正。

Almost every investor has at one time or another at least peruse the pages of Graham's Intelligent Investor and value investing is considered by most to be as wholesome wholesome as Warren Buffett drinking a Coca Cola while eating apple pie.

但动量策略却常被当成追涨杀跌的无稽之谈，被认为在真正的投资里没什么根基。

Momentum on the other hand is often disregarded as performance chasing nonsense with little foundation in the realm of real investing.

但正如你将在我们的对话中发现的，他们对理解这一现象本身，以及构建能有效捕捉它的投资组合，投入了大量深思熟虑。

Yet as you'll find in our conversation, deep care and thought goes into both understanding the anomaly itself and constructing a portfolio that can efficiently attempt to capture it.

希望你们喜欢我和杰克·沃格尔的这场对话。

I hope you enjoy my conversation with Jack Vogel.

杰克，谢谢你今天来参加节目。

Jack, thank you for joining me today.

对，科里。

Yeah, Corey.

谢谢你邀请我。

Thanks for having me on.

那我们先从最明显、最简单的问题开始吧。

So why don't we just start with the obvious and the easiest?

你能简单介绍一下你的背景吗？你是怎么对这个行业产生兴趣的，又是怎么来到阿尔法建筑师公司的？

Why don't you walk us a bit through your background and how you got interested in the industry and how you ended up at Alpha Architect?

是的，我进入金融行业的过程还挺曲折的。

Yes, so definitely a roundabout way into finance.

大学时我主修数学和数学教育。

As an undergrad, I was a math and math education major.

所以，严格来说我是个持证的宾夕法尼亚州高中数学老师。

So, technically a certified high school PA math teacher.

本科毕业后，我依然对数学充满热情。

And then coming out of undergrad, I was still super interested in mathematics.

于是我就申请并被德雷塞尔大学的数学博士项目录取了。

So, I applied and got into Drexel's PhD in Math program.

本来的打算就是拿个数学博士，之后要么去大学教书，要么看看接下来会发生什么。

And so, you know, the original intent there was get a PhD in Math, and then obviously potentially teach at college or just see what happens there.

但到了第二年，我对继续攻读数学博士的兴趣慢慢减退了。

And, you know, during my second year, I kind of became not as interested in getting a PhD in math.

我不想一辈子都做这个。

Didn't wanna do that for the rest of my life.

当时德雷塞尔有一位教授正在筹备设立金融数学专业，他自创了两门课：一门是固定收益，另一门是衍生证券。

And one of the professors at Drexel was actually attempting to start a financial mathematics major, and he made up two classes, one on fixed income, one on derivative securities.

就是通过这两门课，我对金融产生了浓厚的兴趣。

And I basically became super interested in finance from those classes.

之后我在德雷塞尔的数学系教了一年书，但那时候我就想，既然这么喜欢金融，不如干脆去读个金融硕士吧。

And then from there, I was teaching in the math department at Drexel for one year, but I was like, Hey, I'll just get a master's in finance because I really like this.

后来，德雷塞尔大学主动找我，问我有没有兴趣读金融博士。

And basically, Drexel kinda approached me and was like, Hey, would you be interested in getting a PhD in finance?

就这样，我走上了读博这条路。

And that's how I went down the PhD route.

读博士期间，通常都会跟一位教授合作。

And then from there, when you're a PhD student, you generally work with a professor.

到了我第二年，韦斯从芝加哥来到德雷塞尔大学。

And my second year, West, came to Drexel University from Chicago.

我就开始做韦斯的研究助理。

And I started working with Wes, basically as his research assistant.

随着时间推移，Alpha Architect——在它还没定名之前——慢慢发展起来，我就想，我更想做这个，而不是教书。

As things evolved over time, the Alpha Architect, under its numerous names before brand, kinda grew, and I was like, hey, I wanna do this as opposed to teaching.

于是我继续跟韦斯合作，等我一毕业，就全职加入他在Alpha Architect的团队。

So just kept working with Wes, and then when I graduated, I went full time working with Wes at Alpha Architect.

我想稍微聊聊你的博士项目。

So I wanna I wanna touch on your PhD program for a minute.

我自己的感受是，拿博士学位的人，大部分时间都花在专注的研究上，既要自己做研究，也要读大量文献。

My experience is that people who hold a PhD, typically a lot of their program involves a very dedicated research curriculum, both research that they have to perform themselves as well as reading a large number of studies.

我想听听你对现在所谓的‘可重复性危机’怎么看？

And I wanted to get your thoughts on the current, what's I guess being called the replication crisis, right?

很多科学研究、行为学研究，甚至量化金融领域的研究，现在都被质疑统计上不显著、设计有缺陷，导致整个行业对研究成果的信任度下降。

Where a large number of these scientific studies, behavioral studies, and arguably even a lot of the studies into quantitative finance are now being discredited as either not being statistically significant anymore, poorly designed studies, and it's causing this crisis of confidence across the industry.

回顾你过去读过的研究，再看看现在发表的论文，你是不是对今天的学术研究更持怀疑态度了？

I wanted to get your thoughts as you look back on a lot of the research you've read and as you read research today, do you take a more skeptical view towards a lot of the research published today?

我觉得你提到的，是那些反复验证异常现象的论文吧。

I think, so so you you are highlighting that a lot of, you know, there's replicating anomalies paper.

现在确实有不少论文在回头重新检验那些曾发表在顶级期刊里的策略，原本这些策略都被当成异常现象来推崇的。

There's actually numerous papers now kinda saying, looking back and trying to re replicate all of these strategies that were published in top tier journals, and therefore notified as anomalous, right?

我觉得学术界其实被冤枉了，有些外行人都说，这些学者不就是数据挖掘嘛。

And so I think in defense of the academic community here, I I think they're kinda getting a bad rap, because some people from the outside are like, Hey, these academics are just like data mining.

他们到底在搞什么名堂？

Like, what are they doing here?

我们真的该相信这些研究吗？

Should should we even trust them?

我不觉得他们就是这么做的。

I don't think that's necessarily what they're doing.

确实，有些情况下可能存在数据挖掘，然后用数据编个故事。

Clearly, there probably is, in some instances, data mining, and then you fit a story with some data.

但学者们真正想做的，是更好地解释过去股票收益的变化。

But really, academics are trying to better explain kinda what happened in the past in stock returns.

他们根本没说这些发现未来一定有效。

And they're not even trying to say, hey, out of sample, these should work.

对吧？

Right?

我给你举个例子，之前有个挺有名的异常现象。

So so let me give you, like, an example of something, you know, that's kinda was anomalous.

对吧？

Right?

但现在不是了。

And and now it's not.

对吧？

Right?

比如，行业内部上下游企业之间的关联就是一个很好的例子。

So so a good one's kinda like, you know, the industry customer supplier links.

对吧？

Right?

有一篇很有趣的论文指出，过去——大概在2000年之前，也就是九十年代及更早的时候——比如说，以卡莱尔和它的供应商为例，对吧？

And there's cool paper, you know, showing that basically, you know, in the past, this was like before, you know, the 2000 basically in the nineteen nineties and before, you know, if, let's say, I think the example was Callaway and its suppliers, right?

如果卡莱尔的股价表现很好，那你自然会预期它的供应商也应该表现不错，对吧？

And so, if Callaway clearly was having a good return, you should then be able to be like, Oh, okay, well Callaway's doing well, its suppliers should be doing well, right?

当时确实存在这种异常的多空关系。

There was this anomalous long short relationship there.

作者只是指出这种现象过去存在，但并没有说它在未来还会有效，你注意到了吗？

The authors just highlighted that this existed in the past, but they don't say out of sample it's gonna work, and what do you notice?

但事实上，对冲基金后来发现了这个现象，交易变得更容易，这个异常很快就消失了。

Well, out of sample, hedge funds came up with this, trading became easier, and all of a sudden that anomaly went away.

那他们在写这篇论文、指出过去证券价格中出现的有趣现象时，是存心不良吗？

So was it nefarious when they were writing that paper trying to highlight like a cool thing that happened in the past in the prices of securities?

当然不是。

No.

不过我想说的是，未来你得搞清楚，一个异常现象为什么会出现，以及它是不是很容易被套利。

What I would say though is that going forward, you know, you wanna make sure you understand why an anomaly exists and whether or not it's easily arbitraged.

如果真是这样，比如客户与供应商之间的关系，那很可能在样本外就失效了，因为现在有太多高频对冲基金在盯着这种机会。

And if it is, such as that, like, customer supplier relationship, you know, it's probably not gonna work too well out of sample because there's a lot of, you know, high frequency hedge fund type shops that are gonna take advantage of that.

那我们不妨花点时间聊聊为什么——因为量化金融领域很大程度上建立在这样一个理念上：我们尽可能以数据为依据，回溯历史收益，寻找稳健的异常模式。

So maybe we can talk about that why for a minute because so much of the quantitative finance space is based on this idea that we are evidence based to the best of our ability that we're looking back at historical returns and we're trying to identify robust anomalies.

但别忘了，这本质上是一门观察性科学，我们没法重复做实验，只能尽力在不同地区、不同时间周期和不同资产类别中寻找证据，来支持我们的假设。

But again, pointing out the fact that this is an observational science, we can't run repeated trials and to the best of our efforts, we can look for evidence in different geographies, time horizons and asset classes to support our hypothesis.

归根结底，我们都在同一组数据里挖宝，所以数据挖掘的风险是实实在在存在的。

At the end of the day, we are all scouring the same data and so there are the risks of data mining.

所以很多时候，大家会说，这些条件虽然必要，但还不够，你必须搞清楚背后的原因。

And so very often the proposition is, well, that's all necessary but not sufficient, that you do need a why.

对我来说，这个原因往往只是种推测，并不一定是数据能证实的。

And to me often that why an anomaly exists is somewhat hypothetical.

我们其实是在基于猜测做推断，而不是有确凿的数据支持。

It's not necessarily proven out in the data that we're speculating.

这其中多多少少带点信仰成分——我们相信这个解释能持续下去，相信它未来还会有效。

And it does to a certain degree contain a faith based element that it'll continue going forward, that we have this explanation we believe in and we believe it'll continue going forward.

我想听听你对这个问题的看法。

I was wondering if you could touch upon that a bit.

你怎么看待数据和定性原因之间的平衡？当一个异象出现时，是什么让你决定它背后的原因足够可靠，值得拿来用？

How do you think about the balance of data versus the qualitative why as to how an anomaly exists and what changes your mind as to whether the why is sufficient for you to use an anomaly going forward?

是的。

Yeah.

我觉得我对这个问题的看法，差不多已经转了一圈回来了。

So I think, you know, I've kinda come almost full circle on this.

对吧？

Right?

我说的全盘回归，意思是，你在金融领域刚开始的时候，尤其是在博士项目里，最早学到的一件事就是：市场是有效的。

And full circle meaning, you know, when you start anything in finance, one of the first especially, you know, PhD program, one of first things you learn is, hey, markets are efficient.

对吧？

Right?

那这句话是什么意思呢？

And what does that mean?

意思就是，人们并不是傻子。

You know, that means that people aren't idiots.

一般来说，如果你投资某种证券，期望比其他证券获得更高回报，那你实际上就是在承担额外的风险。

On average, you know, you're gonna see that if you invest in a particular type of securities, and you expect or hope to earn a higher return than another set of securities, you should be implicitly taking on some sort of an additional risk.

对吧？

Right?

不过，要说清楚这种风险到底是什么，其实挺难的。

Now, I think one of the things that's hard to say is, hey, what is that risk?

这个从来就没有明确定义过。

That's never really defined.

有效市场假说只是说，如果你获得了更高的回报，那一定是因为你投资了风险更高的资产。

It just says, in the efficient market hypothesis is that if you get higher returns, you must have invested in a more risky investment.

对吧？

Right?

而正如文献中展示的，价值、规模这些异象确实存在，对吧？

And so then, obviously, as shown in the literature, there's value, size, anomalies that appear, right?

法玛和弗伦奇会说，这些其实只是风险更高的投资。

And Fondren French would say, Hey, these are just riskier investments.

但另一方面，行为金融学也提供了很合理的解释，比如人们对价值股的新闻反应过度，对动量股的新闻反应不足。

But then, there's some very valid behavioral reasons that potentially people are overreacting to news for value, under reacting to news for momentum, right?

所以你会想，也许这其实是行为因素导致的。

And so you're like, Hey, well maybe it's behavioral.

也许人们就是不够理性，这些现象未来也会一直有效。

And maybe people are just idiots, these things will work forever in the future.

我觉得这两者都有。

And I think it's a combination of both.

很可能人们在某些方面存在系统性的行为偏差。

There probably should be some behavioral error that people are, for some reason, systematically making.

但另一方面，要想某种策略在未来持续有效，它必须伴随着额外的风险。

But, the additional aspect is that, for something really to work in the future, it has to have some sort of an additional risk.

所以，我对这个问题的看法又回到了原点。

So, again, I've kinda come full circle on that.

我觉得市场是部分有效的，但不是完全有效。

I think markets are somewhat efficient, not perfectly efficient.

大多数人应该都会同意这一点。

I think most people would agree with that.

比如价值投资，人们可能对新闻反应过度，导致股价被压低，但价值股本身也可能确实风险更高。

So, value is an example where potentially people are overreacting to news, causing prices to go lower, but also value stocks may just literally be inherently more risky.

对吧？

Right?

所以我们现在用的是企业倍数，针对的是可选消费板块。

So we use enterprise multiple, right now that's consumer discretionary.

亚马逊确实有可能把所有这些可选消费公司都挤出市场，这不是空穴来风。

There's like a legit risk that Amazon's gonna put every one of those consumer discretionary companies out of business.

所以我觉得，未来如果你要做因子投资，就得搞清楚背后的原因——这个原因至少得是：要么回报更高一点，因为风险更大；要么就是现在投资者在系统性地犯某种错误，而且你预计他们以后还会继续犯。

So, what I'd say is, I think going forward, if you're trying to do any sort of factor investing, you kinda wanna know why, and the why should be at some level, it should be a little bit riskier, or there should be some systematic behavioral error that people are making now, and that you expect them to continue to make in the future.

今天我们花大量时间来讨论动量策略。

So we're gonna spend a lot of time talking about momentum today.

动量是那种常被归因于行为偏差的异象之一。

And momentum is one of those anomalies that very often the reason that gets ascribed to its existence is a behavioral error.

比如投资者存在锚定效应，导致初期反应不足；也可能是因为投资者理性地忽视了信息；等动量真正形成后，又会出现恐慌性追涨和过度反应。

That there is investor anchoring that causes initial under reaction, potentially an argument about rational inattention of investors, and then eventually once momentum takes hold, that there's a hurting and an overreaction effect.

但我很少听到有人从风险角度解释动量。

But I very rarely hear a risk based argument for momentum.

几乎所有人都把它归结为行为问题。

It's almost always a behavioral argument.

你刚才提到，对于未来这些市场异象，应该存在一个基于风险的解释。那么在动量这个现象上，你觉得风险因素到底体现在哪里？

With you coming somewhat full circle back to the idea that there should be a risk based argument for an anomaly going forward, where do you think the risk based argument lies for momentum?

所以，回到这个循环，我觉得无非就是风险或者行为，这两者之一。

Well, so again, full circle, I think it's gotta be one of the two, risk or behavior, right?

你没错，通常大家讨论的都是行为层面的反应不足。

So obviously, you're correct in that the behavior generally is talked about like an under reaction, right?

那从风险角度来说，为什么动量股票会更危险呢？

Now as far as risk, right, like why would momentum securities be riskier?

我们不妨回想一下学术界对动量的发现到底是什么。

Well, I mean, let's remember actually what the academic momentum finding is.

学术研究发现，高动量股票和低动量股票之间的收益差，在不控制这个因子的情况下，具有统计显著性。

The academic finding is that high momentum firms minus low momentum firms have a statistically significant alpha, if you don't control for that factor.

对吧？

Right?

而一旦把这个因子加进去，就能更好地解释股票收益的横截面差异。

And so when you add that in, you basically better explain the cross section of stock terms.

而且，就像你今天提到的那项研究一样，发现动量策略的空头部分对多空组合的收益贡献往往更大。

And also, which is found, similar to that research piece you pointed out today, is that the short book for momentum tends to actually generate probably a bigger portion of the long short portfolio returns.

所以，如果你认为动量是一个多空组合，而空头部分——也就是那些表现最差的股票——实际上带来了大部分超额收益，那很可能这些公司股价跌幅很大，比如平均来看，表现最差的股票组合下跌了70%。

So, in theory, if you're saying that, you know, momentum is a long short portfolio, and the short book, which are the extreme losers, are generating a lot of the excess returns to that long short portfolio, it's probably the case that companies that are down a high percentage, let's just say your average loser portfolio's down 70%.

我这只是随便举个例子。

I'm just making that up.

但假设真是这样，这些股票确实风险更高。

But let's say that's the case, they're probably riskier.

我同意这个看法。

I would agree with that.

所以，做空它们，本质上就是把风险嵌入到了这个多空异常现象里。

So trying to short them, basically, is risk embedded in that long short anomaly.

既然我们已经聊到这儿了，不如干脆彻底深入探讨一下动量策略？

So since we've started going down the rabbit hole, why don't we just dive all in on momentum here?

你们是市场上为数不多的几个在做动量策略的团队之一，就是你们的量化动量指数。

You guys run one of the very few momentum strategies in the marketplace, your quantitative momentum index.

我干脆问个最直接的问题吧：为什么投资者能接触到的动量策略这么少？

And I maybe I'll just ask the obvious question, why aren't there more momentum strategies available to investors?

我觉得很大一部分原因，是人们长期以来习惯把策略硬塞进不同的分类里。

I mean, probably a lot of it's just driven by the way people have historically categorized strategies into different boxes or buckets of a portfolio.

具体为什么变成这样，我也说不准。

Not sure exactly why that happened.

如果你去看晨星的风格箱，或者先锋、iShares这些平台，它们通常都只展示两个维度。

So, if you go on Morningstar, Stylebox, or Vanguard, or iShares, all those websites are gonna show you generally two dimensions.

一个是市值大小，从小盘到大盘；另一个是价值到成长，就这两个方向。

They're gonna show you size, so small to large, and then value to growth, along those two dimensions.

所以很多人在选基金时，只要不是简单买市场指数，就会在这两个维度上做选择。

And so a lot of people will try to pick to the extent you're not just picking the market.

他们希望自己的配置能落在这两个维度的某个位置上。

They wanna allocate somewhere along those two dimensions.

对一些人来说，动量策略可能就被归到成长型策略那一类了。

So momentum to some people, I think, is thought of as a growth strategy.

其实和成长型策略的重叠并没有那么大，但我觉得，因为分类方式就是这样，这是我个人的经验。

There is not a massive, actually, overlap with growth, but I think for some people, since the way buckets have been created, this is just my experience.

人们通常把投资风格简单归为价值或成长，除非你愿意增加第三个维度，否则大家就得琢磨怎么在三维风格矩阵里定位你，我想这大概就是原因。

Think people kinda seem to bucket people into value or growth, and unless you wanna add a third dimension, and now all of a sudden people are trying to figure out where you fit on the three-dimensional style box, I think that's probably the reason.

就是当初人们评估基金时，只沿着价值到成长这个单一维度来看。

Just the original way people were assessing funds along the value growth spectrum.

那我们继续聊聊动量和成长之间的区别。

So let's stay along this this vein of thinking of momentum versus growth.

我们那边奥肖内西资产管理公司最近发布了一篇叫《从零构建因子》的文章。

Our friends over at O'Shaughnessy Asset Management recently published a piece called Factors from Scratch.

我知道你读过。

I know you've read it.

这篇文章分析了不同因子策略的收益，拆解成基本面增长和估值变化这两部分。

And it it looks at breaking down the returns of different factor strategies into the components of fundamental growth and revaluation that occurs.

他们发现，动量因子的收益中，很大一部分其实来自投资组合里个股盈利增速的加快。

And what they end up finding for the momentum factor is significant portion of the total return can actually be explained by accelerating earnings growth in the underlying securities that are held by the portfolio.

他们甚至直接把动量策略称为‘正确执行的增长策略’。

And they go so far as to call momentum growth done right.

所以在我看来，似乎有理由认为动量其实是一种增长型策略。

So to me, it seems like there is an argument that momentum might actually be a growth strategy.

不过他们也指出，大约一年后，投资组合中的增长率就会放缓，回归到市场平均水平，这里可能有个市场误判的异常现象——投资者错误地把这些证券的高增长趋势线性外推了。

Now they also point out that after about a year that growth rate tapers off in the portfolio and there's a, there's a reversion back to the market rate and there might be an anomaly explanation here where the markets extrapolating in error at the growth rate of these securities going forward.

但在我看来，动量和增长之间确实可能存在一定的重叠。

But it does seem to me like there is the potential for some overlap of momentum versus growth.

可听你的意思，你并不认为这两者有显著的重叠。

And it sounds like you're arguing that you actually don't think there is significant overlap.

所以我想请你帮我理清一下这两者之间的区别。

So I was wondering if you could draw apart the distinction for me.

是的。

Yeah.

我的看法是，我几乎不可能把投资组合的某一部分纯粹配置给一个只做增长型股票的策略。

I mean, so what I would say about that is that clearly, I mean, would never just allocate a portion of my portfolio probably to a pure growth strategy.

所以，如果他们说，动量策略其实是更好地接触增长型投资的一种方式，这倒是有道理的。

So to the extent they're saying, hey, momentum's a better way to gain access towards a growth type strategy, that makes sense.

再说一遍，这其实是个定义上的细微差别。

And again, I think just nuance data definition.

而且，奥肖内西那帮人写的那篇文章确实很棒。

And again, that is actually a great piece by the O'Shaughnessy guys.

建议大家都去读一读。

Recommend everyone read that.

但他们里面提到的，尤其是关于动量的部分，其实早在罗伯特·诺维·马克斯的一篇论文里就提过了。

But so, what they find in there, especially on momentum, right, is actually something that was in one of Robert Novi Marx's paper.

本质上，动量其实就是基本面动量。

Fundamentally, momentum is fundamental momentum.

对吧？

Right?

诺维·马克斯和肖内西团队都发现，从某个层面来看，动量收益之所以存在，是因为它其实反映了基本面的动量，比如盈利增长的持续性。

And so, what Novi Marks finds, as well as the Shaughnessy guys find, is that at some level, the fact that momentum returns can be explained by fundamental momentum, meaning an earnings type of momentum.

所以，如果你把成长定义为盈利的增长，那确实说得通。

And so, at some level, if you're defining growth as the growth of earnings, well, yeah, I mean, makes sense.

因为从现有文献来看，我们早就知道，增长率和盈利与价格动量策略高度相关。

Because momentum, we already know, just from the literature, it's been shown that growth rates and earnings are highly related to price momentum strategies, right?

罗伯特·诺维·马克斯的论文会探讨这一点，他在论文中提出，盈利动量比价格动量更有效。

So, Robert Novie Marks' paper will look at that, and basically now in his paper, he'll say, Hey, earnings momentum is better than price momentum.

我们在书里也讨论过这个，但你知道，我们为什么还是用价格动量。

And we discuss that in our book, but you know, why we still use price momentum.

但从宏观角度看，这其实让我们更有信心，动量策略并不是什么凭空捏造的概念。

But, at a high level, it actually gives you a little bit more confidence that momentum is not just this hole in the wall idea.

你不是单纯在挑那些价格涨得高的股票。

You're just picking stocks with high price returns.

我们确实就是在挑价格涨得高的股票，这没错。

It's actually, you're picking stocks with high price returns, that's what we do.

但从根本上说，这些价格表现优异的股票之所以好，是因为它们有强劲的盈利动量。

But, fundamentally behind that, a reason that these winner stocks on a price basis are doing well is because they have good earnings momentum.

这些价格表现差的股票之所以动量低，是因为它们的盈利在下滑。

And one of the reasons that these loser stocks on a price basis have low momentum is because their earnings are going down.

所以，如果我们从盈利的角度来看增长，那么动量确实是一种不错的途径，可以接触到这种增长型策略。

So, to the extent we're talking about growth from an earnings perspective, yeah, momentum's a decent way to gain access to a growth type strategy.

你提到了诺维·马克斯的那篇论文，它全面探讨了基本面动量，甚至认为价格动量已经被基本面动量所涵盖。

So you bring up the Novi Marx paper that talks all about fundamental momentum, and actually argues that price momentum is subsumed by fundamental momentum.

最近，Q因子模型开始流行起来，它认为动量其实已经被它的两个因子——净资产收益率和盈利能力——所包含，我记得是这样。

More recently, you've had the Q factor model start to gain some popularity that argues that momentum is actually subsumed by their factors, return on equity and and profitability, I believe.

面对这些相互矛盾的理论，你是怎么依然有信心继续在投资中使用价格动量的呢？

How do you look at establishing your confidence in continuing to use something like price momentum in your investment process in the face of contradictory pressures?

我们先来多聊聊诺维·马克斯的那篇论文吧。

Well, let's first just maybe talk about the Novie Marks paper a little bit more.

在诺维·马克斯的论文里，他具体测试的是价格动量策略，我想这个大家都能理解。

So so on the Novie Marks paper, what he tests specifically is he looks at, you know, a price momentum strategy, which I think everyone understands.

如果还不清楚，其实也很简单。

If not, it's pretty simple.

你只需要根据股票过去一年的回报率来筛选，剔除上个月的数据，然后买入表现最好的一组，做空表现最差的一组。

You just look at stocks based on their past one year return, exclude last month, and you go long a winner bucket, you go short a loser bucket.

简单来说，这就是动量因子。

High level, that's the momentum factor.

而且，这始终是在多空组合的框架下进行的。

And again, it's always in the context of a long short portfolio.

然后，他把这个和所谓的基本面动量做对比。

And then, compares that to what he calls fundamental momentum.

对吧？

Right?

他从两个角度来衡量基本面动量。

And, he measures fundamental momentum two ways.

一个是看财报发布前后股价的反应，分析累计异常回报。

One is through looking at earnings announcement returns around earnings statements, and you look at the cumulative abnormal return.

再把股票按累计异常回报的高低进行排序。

You sort stocks into those with the highest cumulative abnormal returns.

这就是你的赢家组合。

That's your winner bucket.

你做空那些表现最差的。

You short the ones with the lowest.

他同样用标准化的意外收益来做类似分析，这是会计文献里一个较老的指标。

Similarly, does the same with standardized unexpected earnings, which is an older measure in the accounting literature.

他发现，当你分析这些多空组合时，确实如此，基本面动量策略已经涵盖了价格动量。

What he finds is when you examine these long short portfolios, you're right, fundamental momentum strategy subsumes price momentum.

所以你会想，好吧，杰克，论文里明明就是这么写的。

So, you're like, okay, Jack, well, clearly, this is what it says in the paper.

我不会说他错了。

I'm not gonna say he's wrong.

那为什么你还是用价格动量呢？

So, why do you still use price momentum?

原因有两个。

Well, there's there's two reasons.

一方面，他在论文里这么做，这在学术论文里其实挺常见的。

So one is the fact that in his paper, what he does is and this is kinda standard in academic paper.

他 basically 说，嘿。

He basically says, hey.

我会把这些策略做波动率调整，让它们的波动水平一致。

I'm gonna vol adjust these strategies to make them the same.

对吧？

Right?

所谓波动率调整，简单说就是让投资组合的收益标准差保持一致。

And so what, you know, vol adjust, let's just say, you wanna have the same standard deviation of the portfolio returns.

但实际上，价格动量策略的波动率大概在19%左右，也就是多空组合的收益标准差。

Well, what happens, actually, is price momentum happens to have a volatility around, let's say, 19%, standard deviation of returns, the long short portfolio.

而你的收益动量组合，别忘了，是基于盈利这种基本面数据的。

Your earnings momentum portfolios, which again, remember, are looking at earnings, which are fundamental.

一般来说，经济上行或下行时，高盈利和低盈利公司的表现还是会同步变动，但价格却可能大幅波动。

So, in general, if you can imagine, as the economy goes up or goes down, your high and low should still gonna go up and down, whereas price can be all over the place.

这些价格型投资组合的标准差实际上只有6%。

Now, the standard deviation on these price portfolios is literally 6%.

所以，你要么得把价格动量的多空组合削减到三分之一，要么就得把基本面组合的多头和空头部分都加三倍杠杆。

And so, what that means is you either have to take the price momentum long short portfolio and cut it in a third, or you need to 3x lever the long book of the fundamental portfolio and 3x lever, the short book of the fundamental portfolio.

你知道的，杠杆是一把双刃剑。

And as you know, leverage is a double edged sword.

如果你选择三倍杠杆，没错，它确实能覆盖价格动量，但你也可能直接亏到破产，对吧？

So, if you do the 3x one, yes, it still it will subsume price momentum, but you also will go bankrupt, Right?

如果你选择只做三分之一，那倒也没问题，但这样一来，你投资组合里有三分之二的资金就相当于躺在现金里了。

And if you do a third, well, that's fine, but now you got two thirds of your bucket that you're assuming is in cash.

所以，这说明了解价格动量其实是由基本面驱动的很重要，但同时，我觉得在多空策略的框架下来看，如果现在你问我，杰克，给我做一个多空动量组合，我反而更可能用基本面动量来做。

So, that's one reason that it's good to know that price momentum is kind of driven by fundamentals, but at the same time, I think that's examined in a long short context, and if you ask me right now, say, Jack, I want you to run me a long short momentum portfolio, I would actually probably do it with fundamental momentum.

诺维·马克斯的研究成果确实不错，我对他说的每一点都没有异议。

So, Novi Marx's findings in there are good, and I wouldn't disagree with anything that he says in there.

我只是想说，在构建投资产品时，不一定非得照着他的方法来，因为那并不是我组合的目标。

I would just say when building an investment product, don't necessarily have to follow that because that's not what the goal of my portfolio.

那我们不妨先退一步，聊聊量化动量指数吧。你能给我们讲讲，从整体上你是怎么构建这个指数的？选股和再平衡的过程又是怎样的？

So let's maybe take a step back and actually talk about the quantitative momentum index for a moment, and maybe you can give us a 30,000 foot view of how you think of the actual construction of the index and the process you employ to perform your security selection and your rebalancing.

是的，不如这样，

Yeah, so how about this?

我先把整个流程走一遍，然后咱们再逐个细节深入讨论。

I'll just go through the process and then we can dig into each of the steps.

第一步，先确定我们的可投资范围。

So first step is kind of just assess what's our investable universe.

可投资范围主要是美国和国际市场的中大型股票。

So, for investable universe, it's mainly mid large caps in The US or international securities.

第二步，正如我刚才说的，我们更看重价格动量，而不是基本面动量。

The second step is, as I just mentioned, we are fans of price momentum, not fundamental.

基本面本身没什么问题。

There's nothing wrong with fundamental.

但我们选择用价格动量。

We use price momentum.

我们选择这个方法，回到那篇没有署名的论文，是因为价格动量策略与价值策略的相关性，比基本面动量策略更低。

One of the reasons we use this, just getting back to that no remarks paper, is actually because a price momentum strategy has a lower correlation to a value strategy than a fundamental momentum strategy.

所以我们用价格动量来筛选出表现最好的前10%的证券。

So, we use price momentum to get down to the top 10% top decile of securities.

我们用过去一年的价格回报，也就是总回报，但剔除上个月的数据。

We use past year returns, price basis, or total return, excluding last month.

第一步，我们先做个简单的计算，从一千只证券开始。

So, in step one, let's just do simple math, we start with a thousand securities.

第二步，筛选到一百只。

Step two, we go to a 100.

第三步，我们用一个叫‘温水煮青蛙’的指标。

Step three, what we do is, we use what's called our frog in the pan measure.

这个指标和低波动率策略有点像，我们会从中选出大约五十只股票。

This measure is similar to a low vol measure, and we take the top 50 or so stocks on this measure.

从第一步的一千只，到第二步的一百只，再到第三步的五十只。

We go from 1,000 in step one, to 100 in step two, to 50 in step three.

第四步，我们会根据动量异象中的某些季节性效应来安排再平衡的时间。

And then in step four, what we do is we try to time our rebalances around some what are called seasonal effects in the momentum anomaly.

所以我们总是在季度末月份之前进行再平衡。

And so we rebalance right before quarter ending months.

也就是说，我们的投资组合每个季度调整一次。

So we rebalance our portfolios every quarter.

最后一步，我们会对这些证券进行等权重配置。

And then the last step is, we equal weight across the securities.

这就是全部的五个步骤。

And so those are the five steps.

最终你会得到一个由50只股票组成的投资组合，每季度调整一次。

You end up with a 50 stock portfolio that gets rebalanced every quarter.

那我先提一个可能看起来挺明显的问题，很多人批评动量策略说，这种异象在现实中根本撑不住交易成本，更别提税了。

So let me start with maybe a somewhat obvious question, which is a very common critique of momentum is that the anomaly just cannot survive real world trading costs, particularly after taxes.

那你们是怎么做到在扣除交易费用和税费之后，还能捕捉到一部分潜在的超额收益呢？

So what makes you think that you can run this strategy such that after transaction costs and after taxes, you still have the opportunity to capture some of the potential available Alpha?

是的。

Yes.

这是个很好的问题。

That's a great question.

任何想做因子投资的人都应该清楚这一点。

And it's one that anyone who wants to do factor investing should probably understand.

我觉得这里其实涉及两个问题。

And so what I'd say is there's two questions here.

一个是税务，另一个是交易成本。

One is taxes, one are trading costs.

先说税务，我们更倾向于使用ETF结构而不是共同基金，主要是因为ETF通常税务效率更高。

So first on taxes, one of the main reasons we preferred the ETF wrapper relative to a mutual fund wrapper is the fact that it is generally more tax efficient than a mutual fund.

比如，我们可以看看其他人的动量ETF，它们的表现一直很稳健。

So for example, you can just, we'll talk about other people's momentum ETFs to stay high and to the right.

像MTUM或者PDP这样的动量ETF，都是市场上其他公司推出的，不是我们自己的。

So you examine like MTUM or PDP, other momentum ETFs that are out there that are not ours.

如果你留意的话，这些基金每季度或每半年再平衡一次，在整体上涨的市场里，它们的资本利得分配却为零。

And if you notice, they have rebalance every quarter, every six months, in a general upward market, and you see that they have zero capital gain distribution.

所以在税务方面，动量策略的结构确实更有优势，这也是我们选择它的原因。

So, on the tax front, the momentum wrapper definitely helps, and that's why we did that.

至于交易成本，确实交易越频繁，成本就越高，这意味着因子策略必须跑赢市场更多，才能覆盖这些成本。

Now, getting into the transaction cost analysis, definitely true that the more you trade, the higher the transaction cost will be, which means the higher the hurdle, the original alpha or expected return needs to be above the market.

关于这一点，有几篇论文做过研究。

There's a couple papers on this.

我觉得主要有两类文献在探讨因子投资——包括动量策略——是否能超越交易成本。

I would say there's two strands of literature that attempt to examine whether or not factor investing, including momentum, exceeds trading costs.

一类是市场微观结构方面的研究，比如早些年的Saka论文、Novy-Marx论文。

One is the microstructure literature, and this would be going back in time, the Saka paper, Novie Marx paper.

还有几篇，比如Lesman和Schilzow的研究，对吧？

So there's a couple papers, Lesman, Schilzow, right?

这些论文通常基于学术界提供的TAC数据，来估算交易成本。

And so these papers basically make assumptions on using what's called the TAC data from academic sources, on how much it costs to trade.

学术界的大致结论是，这些策略在扣除交易成本后就撑不住了。

And basically their high level takeaway from the academics is, hey, these things don't survive after trading costs.

另外还有两篇论文。

And then you have two other papers.

一篇是AQR做的，另一篇是贝莱德做的。

One is by AQR, one is by BlackRock.

那些特别怀疑的人会说，这些机构显然有利益冲突，对吧？

So people who are highly skeptical will say, hey, these are clearly conflicted people, right?

但我认为，从宏观角度看，这两家公司都不可能拿自己的声誉冒险，发布错误的信息。

And I'd argue, Hey, at a high level, neither of those firms would ever wanna put their reputation at stake and give out bad information.

但他们发现，用他们自己的交易数据——也就是实际机构投资者或基金公司真实的交易方式——这些策略是有效的。

But what they find is they say, Hey, using our data, our trading data, which is how an actual institutional investor or fund company would trade, we find that they work.

所以很明显，学术界和实务界在这个问题上出现了分歧：学者说没用，从业者说有用。

So clearly you get this divergence in the microstructure literature that academics say it doesn't work, practitioners say it does.

我觉得一个很有趣的发现是，AQR那篇论文里提到，我们不妨直接估算一下跟踪标普500指数的交易成本。

And I think a cool finding really is that if you examine, and this is in the AQR paper, they say, Hey, let's just say how much it costs to S and P 500.

他们用的是学术界的数据显示，按照他们的模型，交易标普500指数的成本至少要60个基点。

And they use the academic data, the way that their model works, and according to the academics, it would cost at least 60 basis points to trade the S and P 500.

也就是说，根据学术界的说法，先锋基金买入标普500成分股的成本是60个基点。

That means literally for Vanguard to buy S and P 500 stocks, according to the academics, it's 60 bips.

但实际操作的机构认为，成本大概只有6个基点左右。

Whereas the practitioners have it as at like six bips.

对吧？

Right?

你去问先锋和iShares，他们说实际成本大概在8到10个基点之间。

And then you ask Vanguard and iShares, they're like, yeah, it's like eight to 10 bips.

对吧？

Right?

很明显，学术模型的估算比实际操作高出了十倍左右。

So clearly the academics model looks to be misspecified on a multiple of 10 times what the actual practitioners are doing.

所以我写了一篇很长的文章来讨论这个问题。

So, I argue, I wrote a long piece on it.

我觉得大家最好读一读，这样才能全面理解双方的观点，但根据市场微观结构的研究，确实存在明显分歧。

I think people should read it to make sure you understand both sides of the story, but according to microstructure literature, definitely a divergence.

同样地，有人从另一个角度去分析，就是试图估算交易成本。

Then similarly, people have examined it from another way, is try to infer trading costs.

我觉得这些论文再次明确表明，这种方法行不通。

And I would say, those papers clearly once again say it doesn't work.

我写过一篇很有说服力的反驳，到现在还没人能指出我哪里错了，说它其实行得通。

I have a pretty good rebuttal that I haven't seen anyone be able to actually say I'm wrong, that it doesn't work.

因为这类估算交易成本的研究里，藏着一些过于理想化的假设。

Because there's some massive assumptions that are being made in this inferred trading cost literature.

它们默认每个人都在完美地跟踪因子组合，可现实中根本没人这么干。

Basically that everyone's running a perfect factor portfolio, which literally no one's doing.

一旦你加入现实中的情况，比如有些基金其实是在搞隐形指数化，或者过去经理人会换策略，那些结论就站不住脚了。

When you make some real world assumptions, like there's some closet indexing, or in the past, potentially managers switch strategies, that finding goes away.

我觉得因子异常现象在扣掉交易成本后应该还是有效的，但我也认为，它的套利空间肯定有上限。

I would say that the factor anomalies should work after trading costs, but what I would say is that there's clearly a size limit.

对吧？

Right?

如果你让沃伦·巴菲特这样身家上千亿的人去玩动量策略，根本行不通。

If if you gave us, like Warren Buffett with multiple 100 billions can't run a momentum strategy.

这确实是事实。

That is a true fact.

所以我建议大家都去了解一下，我尽量把正反两方面的观点都讲清楚了。

So I just recommend everyone kinda read about it, you know, I tried to outline both sides of it.

不过总的来说，我们还是认为因子策略是有效的。

We still think it works, though, is high level takeaway.

我想聊聊这个规模限制的问题，因为很明显，任何策略都有一个临界点，一旦资金太大，就会冲击市场，削弱自身的超额收益。

So I wanna touch on that size limit component, because it is somewhat obvious that every strategy has to have some size limit at which point it'll start to impact the market and erode its own potential alpha.

但你也提到，其实根本没人真的在做纯粹的因子策略。

But you also mentioned the fact that nobody really runs pure factor strategies.

策略。

Strategies.

如果你看看市场上现有的那些极少的动量投资产品，会发现有些指数用的是多种动量指标的组合，有些会结合基本面动量评分，有些则考虑了空头兴趣、盈利意外这些因素，还有些用的是残差动量。

If you look at the marketplace of the very limited momentum offerings out there, you have some indices that are using a blend of different momentum measures, you have some that are taking into account fundamental momentum scores, you have some that are taking into account things like short interest, earning surprise, some that are using residual momentum.

你们用的是很标准的学术型12减1动量，但同时还加入了‘温水煮青蛙’这类方法——这个待会儿我们会讲，以及在再平衡时加入了一些季节性因素。

You guys use the very standard academic 12 minus one, but then employ things like frog in the pan, which we'll we'll get to, as well as some seasonality in your rebalancing.

所以综合来看，你觉得动量策略在市场中能有多大容量？它还能持续产生超额收益吗？

So with that in mind, what do you think the limits are for momentum in the marketplace for it to continue to be and potentially offer alpha?

肯定是有上限的，这点毫无疑问。

So, I mean, there there's definitely a limit, 100%.

不过我觉得大家经常忽略一点，就是总喜欢孤立地看问题，对吧？

Now I think one thing that people always forget is that they tend to examine and try to come up with an answer in a silo, right?

比如你问杰克：动量策略的上限到底是多少？

So you're like, hey Jack, what's the limit for momentum?

然后你就去查数据，发现业内普遍认为是500亿左右。

It's like, okay, well, as of right now, I can kind of examine and if you look at the practitioner data, they say it's like 50.

于是你就觉得：嗯，这听起来挺合理。

And so you'd be like, okay, well that makes sense.

但隐含的是，还有其他策略可能在做非动量的操作，对吧？

But implicitly, there's also other strategies that are potentially doing non momentum strategies, right?

所以如果其他人恰好在对冲这种效应，那确实会产生影响。

And so to the extent other people are kinda canceling it out, that does have an effect.

现在我要说，取决于你如何构建投资组合，每个方案的答案都不一样。

Now, what I'd say is, right now it's probably, depending on the way you form the portfolio, each one will have a different answer.

另外，你的交易成本可能会因此上升，从而产生影响。

Before, your trading cost will then potentially get higher and have an effect.

可惜的是，这个问题没有完美的答案，因为它会随着时间不断变化。

Unfortunately, there's no great perfect answer to that because it's gonna adjust also over time.

比如五年后，如果市场翻了一倍，我现在的回答就会变成：你得把规模也翻倍，对吧？

Like in five years, if the markets double, my answer right now would be, you'd have to double that, right?

但如果因为某种原因，大家不自觉地开始反向操作，避开动量策略，那你的容量或许还能再大一点。

Where if people are, for some reason, implicitly decided to move against the momentum strategy, maybe you have a little bit more capacity.

很遗憾，我没法给你一个精确的数字或完美的答案。

Unfortunately, don't if I can give you a perfect number, perfect answer.

不过我觉得，具体容量有多大，得看你是信实践者还是学术界的说法，两者差距会非常大。

What I'd say though is that it's probably in, depending on who you believe, practitioners or academics, you get massive variance in what the capacity is.

如果我们看贝莱德的那份报告，他们 basically 说，做动量策略的话，可以分五天完成交易，他们的容量直接达到了三千二百四十亿美元。

If we look at the BlackRock paper, if they're basically saying, Hey, we can do momentum, but we can trade over five days, their capacity is literally 324,000,000,000.

这规模可不小。

So that's pretty big.

我只想说，你刚才提到的这些论文，光是整理这期节目的备注，就够我头疼的了。

I just wanna say that all the paper mentions that you've had so far is gonna make compiling the show notes for this episode a very painful experience for me.

我会记下来，全都发给你。

I will remember them and pass them all along to you.

谢谢你。

I appreciate that.

我想回到你之前随口提过的一件事，就是你使用价格动量的原因。

So I want to return back to something you mentioned really off the cuff earlier, but it was about your use of price momentum.

你提到，你之所以喜欢价格动量，是因为它和价值策略的相关性比较低。

And you mentioned that one of the reasons you like price momentum is that it offers a lower correlation to a strategy like value.

这其实和我过去跟你还有你的联席首席投资官韦斯讨论过的话题是一致的，就是关于动量策略中的行业限制问题。

And this is actually in line with the conversation I've had with you in the past as well as with your co CIO Wes, in talking about sector constraints within a momentum strategy.

过去十年里，关于在因子组合中施加不同限制的作用，出现了大量新研究。

And so there's a lot of new literature in the last decade about the role of applying different constraints within a factor portfolio.

现有证据表明，至少到目前为止，像动量这样的策略，如果不设行业限制，通常能最大化你的年化复合增长率。

And the evidence seems to suggest, at least as of today, that when you look at something like momentum, taking an unconstrained approach tends to maximize your compound annualized growth rate.

但如果你把组合调整为与整体市场行业中性，反而更有利于提升信息比率。

But if you constrain yourself to be sector neutral to the overall market, you tend to maximize your information ratio.

我最近刚读到哈特福德的一篇论文，提到如果采用行业限制，虽然会损失大约5%的总收益潜力，但波动率能降低约20%，跟踪误差也会明显下降。

And so there was a paper from Hartford that I just recently read that suggested that you give up about 5% of your total return opportunity, but decrease your volatility by about 20%, your tracking error when you go sector constrained.

不过我之前听你讲过很多，绕了这么大一圈，其实就想引出我的问题。

But I've heard you argue in the past is a long winded way of getting to my question.

你以前说过，当把这种优势放在整个投资组合的背景下来看，尤其是在有效前沿上，如果你的动量策略不设行业限制，同时考虑组合里其他资产，很多所谓的收益差距其实会被抵消，甚至反而成了有益的分散化。

I've heard you argue in the past that that benefit really isn't as large when you consider it in the context of a portfolio, sort of on the efficient frontier that if you go sector unconstrained and consider an unconstrained strategy in the context of other things in the portfolio, a lot of that tends to cancel out and or is actually beneficial diversification.

所以我想请你再详细说说这个观点。

So I was wondering if you could expand upon that idea a bit for me.