克里斯·卡拉诺——设计实用因子模型（第七季第20集）

大家好，我是Corey Hofstein。如果你一直在收听《Flirting with Models》，就知道我专注于重新思考投资组合构建，我想亲自邀请你参加一个正是为此举办的盛会。10月8日，我们将在芝加哥Cboe Global Markets举办'回报叠加研讨会'，这是一场为期一天的线下深度探讨资本效率策略的活动，特邀演讲嘉宾包括达美航空首席投资官Jonathan Glidden、One River的Patrick Casley，以及加拿大养老金计划委员会系统策略组董事总经理Mark Horbul。

Hey, everyone. Corey Hofstein here. If you've been tuning in to Flirting with Models, you know I'm all about rethinking portfolio construction, and I wanna personally invite you to an event that's doing just that. On October 8, we're hosting the return stacking symposium at Cboe Global Markets in Chicago. It's a one day in person deep dive into capital efficient strategies, and we're featuring speakers like Jonathan Glidden, CIO of Delta Airlines, Patrick Casley from One River, and Mark Horbul, managing director of the systematic strategies group at Canada Pension Plan.

这是你直接听取机构配置者在可移植阿尔法和回报叠加领域前沿见解的机会。但席位有限，请立即访问returnstacked.com/symposium了解详情并注册。期待与你相见。三、二、一，让我们开始吧。

This is your chance to hear directly from the institutional allocators leading the charge on portable alpha and return stacking. But space is limited, so head over to returnstacked.com/symposium to learn more and register. Hope to see you there. Three, two, 1. Let's jam.

大家好，欢迎收听。我是Corey Hofstein，这里是《Flirting with Models》播客——一档揭开量化策略背后人性因素的节目。

Hello, and welcome, everyone. 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.

Corey Hofstein是Newfound Research联合创始人兼首席投资官。根据行业监管规定，他不会在播客中讨论任何Newfound Research旗下基金。所有播客参与者的观点仅代表其个人意见，不代表Newfound Research立场。本播客仅供信息参考，不应作为投资决策依据。Newfound Research客户可能持有本播客讨论的证券头寸。

Corey Hofstein is the cofounder and chief investment officer of Newfound Research. Due to industry regulations, he will not discuss any of Newfound Research's funds on this podcast. All opinions expressed by podcast participants are solely their own opinion and do not reflect the opinion of Newfound Research. This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions. 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.

本期节目中，我对话了Two Sigma旗下Venn战略研究副总裁Chris Carano。Chris在因子领域拥有罕见的多元视角，涵盖智能贝塔、多空对冲基金和风险建模。这些经历塑造了他对因子本质及实际应用的深刻见解。我们将探讨Venn的设计哲学：为何仅采用18个正交化因子？如何结合套索回归与最小二乘法减少过拟合？为何将可解释性置于复杂性之上？同时直面现实挑战——

In this episode, I speak with Chris Carano, vice president of strategic research at Venn by Two Sigma. Chris has had the rare vantage point in the world of factors, spanning smart beta, long short hedge funds, and risk modeling. And that experience has shaped the thoughtful view of what factors really are and how they can be practically used. We dive into the philosophy and design behind Venn, why it uses just 18 orthogonalized factors, how it blends lasso and OLS to reduce overfitting and why it prioritizes interpretability over complexity. We also tackle messy real world challenges.

如何用稀疏数据分析私募市场？如何信任合成收益流？在使用隐含组合结构变化的月度数据时如何划定边界？最后探讨因子结果的实际应用：无论是压力测试、残差解读还是组合诊断。请欣赏我与Chris Carano的对话。Chris，欢迎来到播客，感谢你的到来。

How to analyze private markets with sparse data, how to trust synthetic return streams and where to draw the line when using monthly snapshots that embed structural portfolio shifts. Finally, we explore what it means to make factor results actionable, whether through stress testing, residual interpretation or portfolio diagnostics. Please enjoy my conversation with Chris Carano. Chris, welcome to the podcast. Thank you for joining me.

非常期待今天能与你深入探讨因子世界的方方面面。

Excited to dive deep in all things the world of factors today with you.

很荣幸受邀参加，谢谢邀请。

Excited to be here. Thank you for having me. It's an honor.

让我们开始吧。通常我会先请嘉宾介绍背景，但我想调整这个问题。显然希望你分享如何走到今天的职业历程，但据我们讨论，你职业生涯中接触过因子的多个观察视角——从智能贝塔起步，经历多空对冲基金，如今从事风险建模，所有这些都贯穿着基于因子的观察框架。

So let's dive in. I usually start with each guest a little bit about their background, but I would love to tilt this question. Obviously, want you to share your background on how you got to where you are today. But a lot of your exposure through your career from what we discussed has been different vantage points on factors themselves. Starting somewhat in the smart beta side, long short hedge funds and now working in risk models, but everything including that factor based lens.

在这些角色中，你对影响因素的看法是如何演变的？

How has your view of factors evolved throughout those roles?

这是个很好的问题。我先快速读一段免责声明：在开始前，我想明确我在本播客分享的观点不代表Two Sigma的立场，也不构成投资建议。请参考Venn网站的重要免责声明。好了，现在我们可以开始讨论关于因子经验和视角的问题了。

So that's a great question. I am going to start it by reading a disclosure very quickly and just say that before we get started, I'd like make it clear that what I share on this podcast is not necessarily an endorsement by Two Sigma of the views I express, is not intended to be relied upon as investment advice. Please refer to Venn's website for important disclaimer and disclosure information. Okay. So now that we've got that out of the way, great question on my experience and vantage point on factors.

我想从我的职业轨迹说起。最初我在一家做智能贝塔的ETF公司工作——现在都不确定这个术语是否还像当年那样流行。虽然我已转向其他领域，但那时智能贝塔无处不在。那家公司主要做股息加权、盈利加权和多头股票敞口，这让我真正接触到因子投资，因为股息和盈利天然与质量和价值因子相关。

The way I'll start it is thinking of my career trajectory. So I started at an ETF shop that was smart beta, which I don't even know if that term is used as much as it was back in the day. I've moved on from that part of my career a little bit, but at the time, smart beta was everywhere. So this shop did dividends waiting, earnings waiting, long only equity exposures for the most part. And that's where I really got exposure to factors because dividends and earnings lend themselves to quality and value.

这就是我的起点。当时我们也做些多因子自下而上的策略等。对我来说，那时是把因子作为投资产品的增强工具。虽然有点跑题，但我特别珍惜那段与数据打交道的经历——比如亲自为5000只股票构建PE指标时，有些股票数据齐全，有些则缺失，我曾花一小时调试代码最后发现只是日期没对齐。这种经历让我深刻理解了因子产品的实际输入数据是怎么回事。

So that's where that started. Now we did some multi factor there, bottom up, for example, and other things of that nature. So for me, that was getting factors as an enhancement to an investment product, the vantage point there. Now I'll say this is a little bit off the topic, but something I really valued about that time was I was really working with the data at that point in my career. So I got to know what it really felt like to build a PE for 5,000 stocks.

这种实操经验确实让人对市场上各类因子产品的底层数据有了更立体的认知，这也是我特别感激那段经历的原因。

You have some of the data for some of them. You don't have the data for other ones. I spent like an hour debugging code just to find out that my dates were misaligned. I think that type of experience really adds perspective to what the actual inputs are into these kind of factor products that are out there. So that's just something I really appreciated about that experience.

之后我稍微远离了数据领域，转型为因子策略产品的战略规划师。这包括因子轮动产品、多空因子产品等。正是在这里，我深入参与了所谓的‘共性基本面因子讨论’——这个术语虽然拗口，但本质上就是当前金融顾问办公室里正在进行的那些典型对话：比如为什么在质量因子中杠杆作用更好或更差？

Then I moved a little bit away from the data. I moved into being a product strategist for a factor suite. So this included factor rotation products, long short factor products. And this is where I got into what I like to call common fundamental factor discussions, which is a mouthful. But it's basically the exact conversations that are probably happening in a financial adviser's office right now, which is why is leverage better or worse in a quality factor?

价值因子中无形资产与有形资产的区别是什么？股票风格风险溢价存在的各种原因有哪些？这些经历让我获得了大量相关认知。当时我负责的部分产品甚至以风险模型作为底层引擎，这使我开始接触‘因子作为分析框架’的理念，并认识到并非所有因子都追求收益。

What's intangible versus tangible for value? What are the different reasons why equity style risk premiums exist? So that's where I really got a lot of that exposure. And some of the products I was covering there actually had risk models as the underlying engine. So that's where I started to get exposure to this idea of factors as a framework and the idea that not all factors are return seeking.

外汇因子就纯粹是用于解释风险的。这彻底改变了我的观点——我意识到因子作为投资基金收益增强工具，在我看来几乎像是其真实应用场景的二次衍生品，其核心价值在于对多资产组合风险的全局性理解。正是通过这些风险模型等工具，我获得了相关经验，也自然过渡到如今在Venn的职位。Venn是一个风险分析平台。

The foreign currency factor is just trying to explain risk. And that really changed my whole view on realizing that factors as a return enhancement for an investment fund, it's almost like a second derivative of, in my opinion, what the real use case is, which is for holistic understanding of risk for a multi asset portfolio. So that's where I got that exposure, risk models, and things like that. So this was a natural segue to where I am now, which is Venn. Venn is a risk analysis platform.

需要说明的是，这个平台涵盖组合分析、业绩报告等全套功能。但其核心引擎是一个风险模型，为机构级多资产组合提供因子分析等服务。吸引我加入Venn的关键在于：此前接触的风险模型过于细化，包含数百个因子令人难以消化，而Venn反其道行之，力求极简。

So just to be clear, this is portfolio analysis, performance reporting. It's really the whole gamut of stuff you would expect. But there's a risk model that's really the engine gives factor analysis and all these things for institutional multi asset class portfolios. And I think what really drove me to Venn is the risk models I had been engaging with before were extremely granular, hundreds of factors, and really a lot to take in. And Venn really takes an opposite approach, which is it tries to be as simple as possible.

它通过提升可操作性来应用因子，避免陷入分析瘫痪的困境。这就是我当前的定位——仍然将因子视为分析框架，但致力于使其尽可能简单易懂。

It tries to approach factors through accessibility and making it so it doesn't have to be like an analysis paralysis type exercise. So I think that's where I'm at today. Still thinking about factors as a framework, but trying to make it as simple and accessible as possible.

我知道VEN的客户基础中有很多是机构配置者。或许我们可以先退一步，从最根本的问题开始：为什么机构配置者首先应该采用基于因子的视角？它能帮助他们回答哪些传统资产类别视角（沿用了几十年的方法）无法解答的问题？

I know a lot of VEN's client base is institutional allocators. Maybe we can take a big step back here and start with the fundamental question of why should institutional allocators even adopt a factor based lens in the first place? What kind of questions does it help them answer that a traditional asset class view, which has been used for decades, can't answer for them?

让我们从现状说起——目前大多数机构配置者都在使用资产类别。最简单的例子就是类似60/40股债配置的模式。当他们做资产配置决策时，会说‘我要缩短久期’，然后只会在固定收益板块里调整债券经理人。这就是当今主流的资产类别思维框架。需要明确的是，我并非否定资产类别的价值，但它们可能极具误导性——因为资产类别本质上只是个标签。

Let's start with what the reality is today, which is most institutional allocators are using asset classes. So what I mean by that is the most simple example is like a sixty forty or something like that. And when they go to make an asset allocation decision, they're saying, I'm gonna lower my duration, and they're gonna look at their bond managers, and they're gonna start playing around only in their fixed income sleeve. This is the asset class framework of thinking that is typically being used today. So the problem, I think, while asset classes can be helpful, just to be clear, this is not an attack on asset classes, They can be very misleading because at the end of the day, an asset class is just a label.

它并不能真正描述底层风险。我可以很肯定地说，当你考虑债券、房地产、固定收益等不同资产类别时，它们全都面临利率风险。利率才是连接这些资产的根本因子。因此即便你在资产类别上实现了分散，实际可能仍暴露于利率等少数根本因子的风险中。所以我们主张用‘如何管理久期’这类问题来思考——

It's not really a description of the underlying risk. So something I feel very confident in saying is that if you think about some different asset classes like bonds, real estate, fixed income, all of those have exposure to interest rates. So interest rates is the fundamental factor that is shared between them. So even though you may be diversified across asset classes, you may actually have risk exposure to a much smaller set of fundamental factors like interest rates. So the whole idea is let's think things like, what do we do about duration?

这是在风险维度而非资产类别维度思考。使用因子分析时，无论是评估某个经理人、配置板块还是整体组合，你都在用统一的风险语言和因子体系进行分析。这种一致性使得真正的全组合分析成为可能，而传统资产类别方法因其割裂性思维永远无法做到这一点。关于采用率我直言不讳——

That's thinking about that only now in a risk world rather than an asset class world. And when you're using factors, if you're analyzing a manager, a sleeve, a total portfolio, you're doing it using the same set of factors in the same language of risk. So you're applying that to all your different asset class managers. And I think that is what really makes true total portfolio analysis possible in a way that a traditional asset class approach just can't replicate because you're continuously thinking in that siloed way. So I'll just say quickly in terms of adoption, I won't lie.

我从事因子研究十余年，整个职业生涯都伴随着‘因子投资即将成为主流’的预言，但始终未能实现。不过颇具讽刺的是，我现在确实认为转折点已近在咫尺。

I've been in factors for more than a decade. Over my whole career, there's always been this feeling that factors are going to be mainstream tomorrow. It's like it's right around the corner. And it's never really materialized. But at the risk of being ironic, I do think it is right around the corner.

部分原因是权威机构Kaya刚刚发布了《全组合方法》白皮书——你们应该知道这家触达众多机构投资者的巨头——

And part of that is because there's an organization, Kaya. They just released a paper called The Total Portfolio Approach. You probably know Kaya. Huge organization. Touches many institutional investors.

该文件将因子分析定位为全组合方法的核心引擎，认为它能帮助理解整体组合表现。随着组合日益复杂（更多对冲基金、流动另类资产、私募资产），人们会越来越认识到因子的价值。因子分析正是为了揭示那些难以察觉的相关性和波动特征。

And that talks about factor analysis as the engine of a total portfolio approach. It's the thing that helps you think about total portfolio outcomes. So I do think people are gonna realize more and more the role that factors can play. And I think that paper is a response into how portfolios are getting more complex, stay more hedge funds, more liquid alts, more private assets. This is what factor analysis is really trying to help you understand those hidden correlations and volatilities that it can be hard to really digest otherwise.

这正是Venn的定位——我们提供因子思维与落地实践之间的桥梁。

I'll just quickly say that's where Venn fits in. So it's like, I'm thinking about factors and then Venn fits in to help bridge that gap to actually implement.

因子分析的关键在于选用哪些因子。Venn采用的（至少在我看来）是极其精简的方法——18个正交化因子形成层级体系。在多资产组合风险建模背景下，你如何从理论上论证这种‘少即是多’的哲学？据我所知Venn使用的因子数量远少于某些未具名的竞争对手。

A significant part of the factor analysis equation is ultimately what factors you're using to do that analysis. And Venn takes, at least in my opinion, a notably parsimonious approach. There's 18 factors, a hierarchy of factors, the factors are all orthogonalised to each other. What's your theoretical argument for sort of a less is more approach, particularly in the context of multi asset portfolio risk modeling? Again, at least in my experience, the number of factors that Venn uses is significantly lower than unnamed competitors.

说18个因子数量很少应该没人会反对。精简方法的本质在于建模时的选择性和纪律性——如果过度追求囊括所有风险信号，必然导致历史数据过拟合、样本外预测失效、模型可解释性下降等问题。

I don't think anyone's gonna bat an eye twice if you say 18 factors is a small number of factors. So I think you're okay there. So definitely a parsimonious approach for sure. So just to recap, parsimony is about really being selective and disciplined in what you model. So I think if you cast too wide a net and you try to fit every risk signal under the sun, there's clearly problems with that in terms of overfitting on historical data, so it won't be predictive out of sample, less interpretable.

你得到的是一个极其敏感且脆弱的模型。因此我们正试图朝相反方向构建——采用简约、简单、易于理解的视角。我们使用的因子类型是市场风险中最显著的那些。比如股票、利率、信用、大宗商品等宏观驱动因素，这些在任何多资产机构投资组合中都会突显并产生重大影响。此外我们还包含风格因子。

You get a really sensitive and fragile model. So we're trying to take that in the opposite direction, which is a parsimonious, simple, understand digestible lens. The type of factors we use are the greatest hits of market risk. So if you think about macro drivers like equity, interest rates, credit, commodities, these are things that are gonna jump out and slap you in the face for any multi asset institutional portfolio. And then we also have style factors.

这些就是风险溢价驱动因素。例如股票中的动量价值因子，作为宏观风格因子的趋势跟踪等。关键在于这些因子的选择和构建要能解释尽可能多的风险。采用简约方法时这点尤为重要。举个例子，我们的价值因子并非独创的高阿尔法因子——这不是我们构建价值因子的最佳方式。

So these are the risk premia drivers. So momentum value in equities, trend following as a macro style factor, for example. And I think the important thing here is that the factors are selected and constructed to explain as much risk as possible. And if you're taking a parsimonious approach, obviously, that's really important. So just to put that in perspective, our value factor is not some super unique high alpha, our best approach at a value factor.

因为如果那样做，广大机构资产所有者就无法与这个价值因子产生关联，我们就无法解释他们的任何风险。这并非我们的目标。当然我们希望它能代表学术上的回报溢价等特性，但核心在于通过因子选择和构建来解释最大化的风险。

Because if we did that, then the broad institutional landscape of asset owners are not gonna correlate to that value factor. So we won't explain any of their risk. So that's not the point. Now, of course, we want it to be representative of the academic return premium and all of these other things. But it's really about selecting and building them to explain as much risk as possible.

在简约性方面我们不止于此。我们还采用两步回归流程：OLS会拟合所有输入变量，而我们首先使用具有因子筛选功能的LASSO方法。实际在Venn平台进行分析时，我们经常连18个因子都不会用完。

Now, I'll say that we don't stop there in terms of parsimony. We also use a two step regression process. So the OLS tries to fit everything you throw at it. We first use a lasso, which is a little bit of a factor selector. So when people actually conduct this analysis on Venn, we're often not even using all 18 of our factors.

通常使用的因子更少。重申目标：保持样本外稳健性、透明化、提供真正可操作的分析。还有个额外好处是投资对话变得极其顺畅——无论是投资团队内部讨论，还是进行管理人尽调时涉及这18个核心风险因子，甚至向投资委员会或终端客户解释时，你完全可以说'不必担心模型问题'。

We're often using less than that. So again, the goal, be resilient out of sample, be transparent, give analysis that's really actionable. And I do think a little side benefit is that the investment dialogue is just so much easier. So whether it's an investment team talking to each other, trying to do manager due diligence and you're talking about 18 factors in these big fundamental risks. If you're talking to an investment committee or an end client, you don't have to be like, don't worry about the model.

因为模型本身足够易懂，可以直接讨论。这就是我们的设计理念。

Just you can actually talk about the model because it's it's digestible. So that's the idea.

你的回答中承认了因子选择在某种程度上具有主观性——不存在所有投资者都必然认同的完美柏拉图式因子集。对因子模型的常见批评是其存在过拟合风险，特别是在短期时间序列分析中。你提到Venn采用LASSO方法来应对，准确说是LASSO+OLS的两步流程。

In your answer there was the acknowledgement that to some degree the choice of factors is inherently subjective. There's no clean platonic set of factors that every single investor will necessarily agree on. A common critique of factor models is that they risk overfitting, especially when you're talking about shorter term time series. You mentioned that Venn applies this lasso based approach to try to combat this. Actually it's a two step approach is my understanding, lasso and then an OLS.

能否详细说明这个旨在提升拟合稳健性的两步流程？因为过度处理会导致系统不透明和难以解释。你们如何平衡稳健性与最终目标——可解释性？

Can you walk us through this two step process that you apply that tries to improve the robustness of fit without the problem when you start to do too much is it makes the system opaque and potentially non interpretable. Again, how do you fit that balance of robustness versus interpretability, which is the end goal here?

先暂时搁置LASSO。如果用全部18个因子做OLS，确实能得到最佳拟合，但过拟合风险和样本外预测性也最高。如何应对OLS全盘拟合的特性？这就是引入LASSO作为前置因子筛选器的意义所在。

Let's take the lasso out for a second. The OLS. If we did an OLS with all 18 of our factors, that's gonna be the best fit possible, but it's also gonna be the highest risk of overfitting and not being predictive out of sample. So how do we try to combat the fact that an OLS just fits everything you throw at it? That's where you introduce the lasso, which kind of acts as, a factor selector first.

我喜欢这样比喻（虽然这是音频节目）：想象回归方程中18个因子作为自变量，LASSO就像洗衣机上的旋钮——我称之为'因子收缩旋钮'。当你转动一格，所有18个因子贝塔系数就会向零收缩。

The way I like to think about it I know we're on a podcast here, but if you can picture it in your mind, you have the regression. You have all 18 factors as the independent variables. I like to think of the lasso as like this is dial, like you'd see on like a washer machine or something like that. I call it the factor shrinking dial. If you turn that one notch, what's gonna happen is is those 18 factor betas are gonna shrink towards zero.

而那些影响力较小或beta系数较低的因子将会归零。比如我们第一次调整因子旋钮时，可能从18个因子降到16个，其中两个因子的beta为零，相当于剔除了它们。再调整一次旋钮，可能又有四个因子归零。按这样计算，现在应该剩下12个因子了。

And the less influential or the smaller betas are gonna go to zero. So maybe we turn that factor dial once and it goes from 18 to 16 factors with two of them being at a zero beta. So we're basically dropping those. Now we turn the factor dial again and maybe four go to zero. So if I'm doing my math right, I think we're at 12 now.

这个过程可以持续进行，直到将所有无关变量从方程中剔除。但这个调节旋钮并非完美解决方案——完全不收缩时就是包含全部18个因子的全变量回归；若收缩到底则模型空无一物。因此关键在于如何平衡模型拟合优度与因子数量？这时就需要使用校正AIC评分，它正是为此设计的。

And you basically can continue to do that process until you're eliminating these variables from the equation. But this dial is not a perfect answer because if we don't shrink it at all, it's just an all s regression with all 18. If we shrink it all the way, then you got nothing in it. So how do you decide where's the right place where you can balance the goodness of fit with a number of factors? So this is where we use a score called corrected AIC, and it does exactly that.

该评分会同时计算拟合优度和因子数量，最佳得分（实际为最低分）对应的位置就是我们设定旋钮的基准点，决定有多少因子能进入OLS回归。我们使用的是针对小样本调整的校正AIC——比如分析组合只有36个数据点时，评分会倾向于更简化的模型。

It calculates goodness of fit, number of factors. Where the best score is, which is actually the lowest score, is where we set that dial in terms of the number of factors that we allow to pass through to the OLS. Now we use a corrected AIC, which is basically an adjusted for small sample size. So let's say the portfolio we're analyzing is only 36 data points. The score is gonna prefer an even simpler model.

这本质上是对数据量不足的惩罚机制：数据越少，模型复杂度越低。这是为了规避小样本过拟合风险。当数据稀缺时，可能仅保留1-3个置信度高且不易过拟合的因子。完成因子筛选并设定好旋钮后，假设最终保留10个因子进入OLS回归——这才是核心环节，用于估算beta系数。

So it's really penalizing the fact the less data, the less complex the model is. That's acknowledging the higher risk of overfitting with less data. So if you don't have a lot of data, then might only spit out one, two, three factors because that's what it's confident in terms of not introducing itself to that overfitting risk. So once you have that factor selection, you have the right dial setting, you've turned it, you've got maybe 10 factors that make it through, That's what you put into the OLS, and that's the workhorse. That's where you estimate the betas.

你会得到t统计量，模型会拟合所有输入因子。必须说明的是，这并非什么万能魔法。基于收益的因子分析仍有局限——比如分析股票多头策略时，可能混入固定收益套利因子，它通过了LASSO筛选进入OLS回归。

You get the t stats, and you're fitting everything you throw at it. So just to be clear, it's not as if this is some super magic bullet. It's returns based factor analysis. So there's still times I might analyze a long only Equity Active Manager, and there'll be some fixed income carry beta in there. It made it through the lasso into the OLS.

这时仍需保持清醒：这很可能是个伪相关，固定收益套利因子未必真有意义。所以绝非万能方案。但有趣的是，由于我们仅使用18个因子，LASSO的筛选结果非常透明——若最终保留12个因子，你立刻能看出价值因子明显被剔除了。

And I'd still have to have a good head on my shoulders. That's probably a spurious correlation. I don't know that fixed income carry is relevant here. So it's definitely not a magic bullet. But I will say something that's interesting is because we're taking this approach with only 18 factors, it's really easy to know what got filtered out by the lasso.

这种透明度让你清楚知道哪些因子被用于OLS回归来建模组合收益。

If you only have 18 factors and we give 12, you're like, okay, well, value is clearly not here. So it's really transparent in that way where you know what's being used in the OLS to basically model your portfolio returns.

你们因子库中相当部分属于风格因子——价值、动量、质量、防御性甚至趋势因子。根据我的经验，这些因子的具体构建方式会显著影响其表现。比如价值因子的不同定义会导致回归载荷差异巨大。更复杂的是，资产配置者对价值/动量/套利的定义可能与Venn因子库不同。你们如何平衡因子构建的精确性，与用户需要将Venn因子模型输出结果与其自身认知框架对齐的需求？

A meaningful portion of the factors that you have in your library are what I call style factors, the value, momentum, quality, defensive, even trend. And these are places where in my experience implementation details can significantly influence factor behavior. If you define value one way versus another, it can significantly change the loading that a factor regression can result in. What's even more difficult here is that one allocator's definition of value or momentum or carry might actually differ from Venn's library definition. How do you think about balancing this precision in factor construction with the need for users to align the outputs of Venn's factor model with their own mental models and definitions?

很好的问题。我们的股票风格因子包括价值、质量、动量、小市值、低风险和拥挤度，这些确实都有不同的衡量指标——且指标选择至关重要。这点我完全同意。不过我们采用的指标都是主流标准。

Fair question. We have equity styles in value, quality, momentum, low size, low risk, and crowding. Definitely, all of those have different metrics and the metrics matter. So a 100% agree there. I'll just say that we don't really get too cute in terms of what we use.

你不会看到冷门的质量指标，比如从没听过的古怪参数，我们用的是ROE这类通用指标。但这不意味着指标选择不重要，你说的问题确实存在。不过我想把讨论转向另一个常被忽视的维度——除了指标本身，这些指标的组合构建方式同样关键。无论是构建投资基金还是因子组合，最终如何呈现收益流才是核心。

So you're not gonna see a quality fundamental where you're like, never heard of that. It's gonna be ROE, things like that. That doesn't mean it doesn't matter and everything you said is true. But what I would maybe do is shift the question a little bit to something that I think is sometimes underrepresented, which is not necessarily the metrics, but also how important the portfolio construction is of those metrics. Whether you're creating an investment fund or a factor, how you ultimately represent that return stream matter.

例如，我们所有的股票风格都采用相同的投资组合构建方法。当然，指标不同，但构建方式相同。我认为其中最具影响力的部分就是市场中性或贝塔中性策略。它们采用多空策略，同时明确追求对市场的贝塔为零。这在投资者的心算、预期和直觉层面可能出现理解偏差，尤其是涉及低风险因子时。

For example, all of our equity styles have the same portfolio construction. Different metrics, of course, same portfolio construction. And one of the most influential parts of the portfolio construction, I think, is being market neutral or beta neutral. So they're long short, but also explicitly aim to have beta of zero to the market. Where I think this can break down in terms of the mental math or expectations and intuition of investors is especially with the low risk factor.

因为从本质上看，低风险因子有两种构建方式：美元中性策略——做多低风险股票，做空高风险股票，但保持多空头寸的美元价值相等；另一种是市场中性策略，但保持两边的贝塔值相等。美元中性策略实质上是做空股票风险溢价，因为多头篮子的贝塔较低，而空头篮子贝塔较高。所以你天然处于股票风险溢价的对立面。

Because if you think about it, there's two constructions you can kind of approach the low risk factor with. A dollar neutral, which is long low risk stocks, short high risk stocks, but an equal dollar value between those long and short exposures. And then the same market neutral, but equal beta value between those two. If you think about the dollar neutral, it's short the equity risk premium because the long basket has a lower beta, high basket probably higher beta. So you're naturally short the equity risk premium.

以新冠疫情崩盘为例（我们确实对此做了美元中性与市场中性策略的对比研究）。美元中性策略在疫情期间是正收益——显然做空股票风险溢价获得了顺风优势。而我们低风险因子的贝塔中性版本在疫情期间反而是负收益。

So let's imagine the COVID crash. And I say let's imagine, but we actually did research on this between the difference between the dollar and a market neutral during the COVID crash. The dollar neutral was positive during the COVID crash. Obviously, short the equity risk premium benefited from that tailwind. The beta neutral version of our low risk factor was actually negative during the COVID crash.

关键区别在于它缺少做空股票风险溢价的顺风效应。因此它更依赖高风险与低风险股票之间的价差——讽刺的是这需要经过风险调整。虽然理解起来有难度，但这样做的好处是与股票因子回归时保持正交性。举个实际例子：假设你持有低风险ETF，它在疫情期间跑赢标普500。

And the comment here is it just doesn't have the tailwinds from being short the equity risk premium. So now it's relying more on the spread between high risk and low risk stocks, essentially adjusted for risk ironically. And that can be hard to wrap your head around, but one of the benefits of this is orthogonal with the equity factor when you run a regression. But let's think about that practically. You're an investor.

在我们的市场中性模型中，这实际上暗示你应该降低股票贝塔暴露。若有预知能力，你本应直接减少股票贝塔。而引入低风险因子在当时反而不够审慎。这种解读更简洁明了——因为股票因子流动性更强、更具可操作性。正是这个因子驱动并解释了低风险ETF相对标普500的超额收益。

You have something simple like a low risk ETF, and it outperforms the S and P 500 during the COVID crash. With a market neutral version in our model, it would essentially just be telling you that you should have just had less equity beta. If you had perfect foresight, you should have just had less equity beta. And actually introducing low risk was not a prudent thing to do over that period. If you think about it, that's actually a much simpler takeaway because the equity factor is more liquid, it's more actionable.

采用市场中性或美元中性策略会得出截然不同的归因结论。另外值得一提的是'做空贝塔'概念——美元中性低风险因子其实持续在与股票风险溢价对抗。在我看来，这无异于无谓地损耗风险溢价。个人更倾向市场中性版本，不仅因其与风险模型的正交性，更因长期风险溢价获取的考量。

And it was just that that was driving and explaining outperformance of that low risk ETF versus the S and P 500. So incredibly different model outputs. Whether you use market neutral or dollar neutral for low risk, you would get a completely different answer in terms of what was driving outperformance over that period. Now something else, this is just a little different that I like to talk about. It's the betting against beta idea.

虽然有时不够直观，但风险模型的本职就是揭示收益的真实驱动因素。美元中性策略在牺牲风险溢价方面缺乏正当理由，而市场中性版本既能保持与风险模型的正交性，又能长期有效捕捉风险溢价。

But a dollar neutral low risk factor is also fighting the equity risk premium constantly. In my opinion, you're basically hurting your risk premium there for no reason. The market neutral version, I think, personally, that would be my preferred implementation, not only for orthogonality reasons with the risk model, but also just in terms of seeking a risk premia over a long period of time. So definitely it could be argued not the most intuitive at times, but that's what a risk model should do is really get at the true drivers of return.

我们已多次提及'正交性'这个关键设计原则。从数学角度，它确保因子间真正独立，优化拟合效果；正如你与Venn所述，正交化使因子提供更清晰的洞察——正如刚才讨论的案例。但有时这种清晰洞察可能与配置者观察到的市场回报直觉相悖，比如新兴市场上涨时，新兴市场因子可能持平甚至因残差化宏观风险而下跌。

We've used the word orthogonal probably at least a dozen times now. But it is a really critical design choice that you've made and I think it's important both from the mathematical side of making sure you have a good fit to these factors, that they are truly independent from each other, making the fit easier. But you've also written with Venn that the orthogonalization allows factors to provide clearer insights, which I think is what you just spoke about this. But sometimes those clearer insights can be at odds with an allocators intuition of observed market returns. For example, emerging markets may go up while the emerging market factor is flat or even negative due to residualized macro risk.

你如何平衡这些权衡——既要保证拟合优度和解释力，又要满足终端用户对因子可解释性的需求以实现可操作性？

How do you reconcile these trade offs of goodness of fit, explanatory power and again, the end user's need for interpretability to create actionability with these factors?

很好的问题。回顾一下：我们刚讨论通过多空组合构建实现股票风格与股票因子的正交化。但并非所有因子都适用这种方法，对其他因子我们采用'残差化'处理。

Great question. Just taking a step back. So we just talked about being orthogonal with our equity factor through long short portfolio construction for our equity styles. We're not able or we don't do that with all of our factors. So for other factors, we use something called residualization.

你提到了新兴市场，我就以新兴市场为背景来谈。我们的新兴市场因子，在未经任何残差化、正交化处理前，就是三个简单的指数：市值加权的新兴市场股票、市值加权的新兴市场债券，以及等权重新兴市场货币。这并非对新兴市场的革命性创新解读。我们认为这个新兴市场指数组合确实承载着某些宏观因子的风险敞口。

And you mentioned emerging markets, so I'll talk about in the context of EM. Our EM factor, before any residualization, orthogonalization, nothing fancy, is just three indexes. It's market cap weighted EM equities, market cap weighted debt, and then EM currencies, equal weighted. So it's really not a fancy game changing interpretation of what EM could be. Now that basket of EM indexes, we think, has some exposure to macro factors.

在座应该没人会否认，我刚才描述的组合存在全球股票敞口——毕竟其中33%是新兴市场股票。我们通过滚动三年指数加权回归来测算这个新兴市场组合对各宏观因子的敏感度。这个时长既能保证新兴市场组合与宏观因子之间的关系不会过于敏感和嘈杂，又能及时反映市场变化。操作上，我们做多这个新兴市场指数组合，同时根据滚动回归的估计值，按比例做空对应的宏观因子。

There's probably no one on this podcast that would argue that that basket I just described has some global equity exposure because 33% of it is EM equities. So what we do is we try to measure how much exposure to these macro factors this EM basket has. Now we do that using a rolling three year exponentially weighted regression. And we found that this is a long enough period where the relationship between that EM basket and those macro factors is not overly sensitive and noisy, but it's short enough where when markets change, it reflects that. So conceptually, what we do is we go long that EM basket of indexes, and then we short the macro factors with the magnitude of those shorts based on that estimated rolling regression.

这就是我们获取纯粹新兴市场敞口的方法论。至于如何与配置者的市场直觉相协调——我们的新兴市场因子长期呈现负收益。经过残差化处理的独立新兴市场因子年化约-150个基点。想象你是个机构配置者，当你构建投资组合时...

So that's conceptually how we basically try to get to that pure EM exposure. Now in terms of trading that off with an allocator's market intuition, our EM factor is negative over long periods of time. So the residualized EM factor, the independent one, it is about a 150 bps per year. It's negative. So imagine you're an institutional allocator and you load your portfolio in or you build it.

假设你配置了新兴市场板块和五位新兴市场基金经理，突然发现Venn模型显示新兴市场因子长期无超额收益。这会颠覆传统认知，因为人们预期新兴市场高增长高回报，而r因子却揭示真正驱动收益的是其中隐含的宏观因子。新兴市场上涨时（非绝对但多数情况），提高整体股票贝塔可能比单纯配置新兴市场更优——后者可能跑输全球市场涨势。

And you've got an EM sleeve and you've got five EM managers. And you're like, oh, wait a second. The EM factor in Venn's not even rewarded over time. So that definitely can challenge intuition because they expect EM growth potential, high risk but high reward, when r factor would suggest that it's actually just the macro factors doing the heavy lifting that are embedded in EM. So maybe there's an EM rally, not all the time, but most of the time, you probably would be better off just having a higher equity beta, not necessarily being in EM specifically because that may be lagging behind the EM the global rally.

这种认知冲突确实存在。但归根结底，风险模型的作用就是揭示独立可操作的真相。如果有人强烈反对，他们大可以寻找认可新兴市场因子的风险模型——只是必须接受这个现实。再抛个震撼弹：小盘股因子在我们的框架中也无超额收益，市场中性策略下小盘股长期负收益——这又是需要消化的反直觉案例。

That can be challenging sometimes. But at the end of the day, similar to before, like, that's what a risk model does. It tries to get at the independent actionable truths. And this is our framework. So if someone really disagrees with that, they might go find a risk model where EM is rewarded.

（为避免以炸弹式结论收尾补充道）不过必须承认这个现实。顺便再抛个震撼弹：小盘股因子在我们的视角下同样没有超额收益。全周期内小盘股的市场中性策略呈现负收益——这又是一个需要投资者调整认知的领域。

And they just have to kind of accept that. And not to end the question with another bomb drop, but actually small cap factor is not rewarded in our lens either. The market neutral implementation of small cap is negative over the full period. So that's another one where people sometimes have to wrap their head around it.

你职位的优势在于能接触大量机构投资组合的输入输出数据。我们在预沟通时谈到一个普遍现象：多数机构组合的实际分散度远低于表面配置，经分析会暴露出显著的因子集中度。能否请你深入谈谈，当BEN模型应用于机构整体组合时，最常见的意外发现有哪些？

One of the big benefits of sitting in your seat is you get to work with a lot of institutions and you get to see a lot of institutions put their portfolios into then and see what comes out on the other side. And one of the things we talked about in our pre call was that fairly consistent theme is that most institutional portfolios are maybe far less diversified than they appear when they go in. There's a lot more factor concentration that comes out. I was hoping you could maybe expand upon that and talk about some of the common surprises that emerge when BEN is applied to institutional total portfolios.

这其实呼应了我们之前讨论的要点。机构组合在资产类别层面看似极度分散——包含房地产、流动性ETF、共同基金、对冲基金和私募资产等，但经过模型分析后...

So this is gonna hit on some of the things we've kind of alluded to throughout. I think you and I both have done this. So when you think about institutional portfolio, they come in and they're ultra diversified from an asset class perspective. You've got real estate, liquid ETFs and mutual funds. You've got hedge funds, some private assets, and then they'll run it through then.

虽然不算绝对，但经常会出现股票和利率因子主导80-90%风险的情况。需要说明这可能是LASSO算法对36个数据点简化的结果——但根据我的经验，更多时候这反映了真实情况。之前提到的将宏观因子从其他因子中残差化并归集的概念...

And I'm not gonna say it's common, but it's not uncommon for that to spit back out an equity and interest rate factor as the main drivers of risk, sometimes upwards of 80 to 90%. Now, I'll just say quickly, that could be the lasso. Maybe they're coming to the table with 36 data points and the lasso is spitting out a simple model. But in my personal experience, that's not always the case and it's often not the case. So we had mentioned this residualization idea of taking out macro factors from other factors and consolidating that into the macro factors.

我们实际建立了金字塔式的分级体系：在Two Sigma因子框架中，股票和利率是顶层的宏观因子（可理解为货币对冲后的股票和债券）。这个体系旨在将尽可能多的风险向上归集到顶层因子。

Well, we actually have a tier system that does that. So imagine it like a pyramid. And the highest tier macro factors in the Two Sigma factor lens are equity and interest rates. So just think about this as currency hedge, equities, and bonds. That is the world with which we're trying to consolidate as much risk upwards as possible.

第二层级是信贷和大宗商品。第三层级则是外汇、新兴市场和本地通胀。同样地，残差化是主要工具，我们描述的股票风格为市场中性。但残差化工具会将风险向上传递至最高层级的宏观因子。因此这是一个关于将某一因子置于另一因子之上的层级决策选择。

Then tier two is credit and commodities. Tier three, foreign currency, emerging markets, and local inflation. And again, residualization is the tool for the most part, equity styles we describe, market neutral. But the residualization is the tool that passes that risk upwards to the highest tier macro factors. So that's a decision choice to put one factor over another in the tier.

我们做出这一决策的根本原因在于认为这些因子更具流动性和可操作性。当然，若您希望实施投资组合调整，这将是最佳切入点。回到最初的问题，令人惊讶的是您混合了各类资产及其相关要素，要么它们都具有高度相似的底层风险，要么您的投资组合混合方式使其仅能被这些高层级主因子所解释。但我确信这对许多人而言具有启发意义。

And the reason why we really make that decision is because we think they're more liquid and actionable. Of course, if you want to implement portfolio changes, that would be the best place to start. And going back to the the original question, the surprise is you have this mix of asset classes and all of this stuff. And either it all has very similar underlying risks or your portfolio mixes in such a way that it can just be described by these main high tier factors. But I definitely think that can be eye opening for a lot of people.

这样做的好处在于，您能更切实地理解真正驱动投资组合结果的因素。您为流程引入了一个工具，我认为它能以独特而有意义的方式定义多元化。您需要判断这个视角是否对资产配置流程具有价值。通常接下来的步骤是什么？这时您就会开始调整管理人的配置。

The benefit is, the payoff of that is you get a more actionable understanding of what's really driving the portfolio outcomes. You introduce a tool to the process that I think can define diversification in a different and meaningful way. And you have to accept or not accept that perspective as being valuable to your asset allocation process. And I'll just say, what is usually the next step if that happens? Well, now you start to play around with managers.

您开始思考如何引入更多Venn平台的其他因子贝塔。当您在Venn因子间实现分散配置时，会产生一种认知——理解这个风险框架后，才能真正感受到在此框架下的分散效果。因此这带来了明确的回报：发现意外之后，我该如何应对？

You start to see how can I introduce more betas to other factors that Venn has? And then when you are diversified across Venn factors, there's this feeling, you know, understanding this risk framework. Now I really feel diversified in this risk framework. So there's a clear payoff to it in terms of I have this surprise, now what do I do about it?

机构投资组合管理面临的最大挑战之一是不完全透明性，特别是配置的另类投资管理人。这里既包括可能存在收益数据问题的私募资产，也包括数据有限的对冲基金或新兴管理人。这些问题在讨论普通高净值客户配置高流动性共同基金和ETF时并不显著。Venn如何为机构配置者解决这个问题？

One of the biggest challenges when working with institutional portfolios is the incomplete transparency you may have, particularly with alternative managers that they allocate to. And here I'm thinking about both privates where maybe the return data has issues in itself, but also hedge funds or emerging managers with limited data. Problems that don't necessarily show up as much when you're talking about your average wealth client who's allocating to highly liquid mutual funds and ETFs. How does Venn approach this problem for institutional allocators?

我首先想回归整体组合策略的理念。因子引擎是实施整体策略的必要工具。暂且不论需要选择哪些因子建模，您还需管理和获取数据，确保各要素相互独立。

My first thought is to actually go back to this idea of the total portfolio approach. So the factor engine is this necessary tool to do the total portfolio approach. Now let's put aside for a second, you have to select which factors to use for your model. You have to manage and procure the data. You have to make sure everything's independent with each other.

抛开这些不谈。若采用持仓分析法，您还需要投资组合的持仓数据。对于难以获取持仓的对冲基金或私募资产，即使构建了因子透镜并完成所有前期工作，由于缺乏基础数据，实际上在开始整体组合分析之前就已举步维艰。这正是Venn采用收益分析法的根本原因——唯一需要的持仓数据是用于构建因子的基础持仓，而这由Venn完成。

All that aside. If you're using a holdings based approach, you also need the holdings for your portfolio. If you have a hedge funds or private assets where you're probably not gonna get those holdings, even if you did build a factor lens and do all this great stuff, in order to conduct that holistic total portfolio analysis, you're basically ending before you began because you just don't have the data to do it. So that's one of the fundamental reasons why Venn took a returns based approach. The only real holdings that are happening are the holdings used to build the factors, but Venn's doing that.

随后我们输出因子收益时间序列。作为用户，您要么获取标普500的收益数据，要么上传自身收益数据，之后完全采用基于统计回归的分析方法。对于无持仓数据的对冲基金，只要具备足够且可信的收益数据，就能进行统计分析。您可能发现他们频繁调仓但始终保持动量暴露。

And then we spit out the factor time series of return. So you as a user, you're either getting from then the return of the S and P 500 or you're uploading your own returns. And then it's purely a statistical regression based approach from there. For a hedge fund with no holdings, as long as you have enough return data and you believe the quality of that return data, you can do statistical analysis on it. You can see maybe they're turning over their portfolio a ton, but they always have momentum exposure.

或许我可以用ETF实现同样效果。又或者他们存在大量无法复制的正向残差——这显然是好事。私募资产则较为棘手，正如您提到的，其在时间加权收益方面存在各类问题，这远不止是数据可得性的挑战。

Maybe I could get that with an ETF. Or maybe they have tons of residual that can't be replicated and it's all been positive. So that's a great thing. Private assets get tricky because you mentioned they have all types of problems when it comes to time weighted returns for private assets. It's not just about having the data.

通常数据获取只是第一步。以私募资产指数（如私募股权指数）为例，其本身存在重大缺陷：收益必然平滑（几乎可确定），这将导致其滞后于公开市场——公开市场上涨时该指数可能三个季度后才跟进，且波动率被人为压低。

Often, that's just the first step. If you think about a private asset index, for example, like a private equity index, it's gonna have some major issues with it. So it's probably gonna be smooth. I say probably, almost certainly it's gonna be smooth, which is gonna lead to lagging public markets. So public markets rally this index may rally three quarters later or something like that, and artificially low volatility.

这两个组成部分，你可能听过Cliff Asness称之为'波动性洗白'，其核心理念是：与经历价格发现的公开市场不同，这些私募资产采用估值计价。这种估值计价方式往往是个黑箱，创造了人为制造平滑波动曲线的操作空间。这成为主要问题——试想这会如何影响你试图回归分析的流动性因子相关性。另一个问题是它们通常仅按季度更新。

And these two components, you may have heard Cliff Asness referred to as volatility laundering, which is basically the idea that unlike public markets, which are going through price discovery, these private assets are being marked evaluation. How they're being marked evaluation is often a black box and creates the opportunity to market in such a way that the smooth volatility profile emerges. So that's a major issue. Now you can imagine, there go your correlations with the liquid factors that you're trying to regress against. Another issue is they're usually only quarterly.

若你需进行每日全组合分析，就不得不将数据降频至季度级别以纳入私募资产。此时你捕捉到的只是组合中变动最缓慢部分的风险动态。且私募资产数据通常严重滞后，反映的可能是两三季度前的状态。因此我们思考如何利用收益增强功能，逼近私募资产按市价计量的可能形态。

So if you have total portfolio analysis that you're trying to do on a daily basis, well, now you have to downsample to basically quarterly because the private asset you're trying to include is part of that. And now you're only capturing the risk dynamics of the slowest moving parts of your portfolio. And then private assets typically are super out of date. They're as of, like, three quarters ago or two quarters ago. So the idea is how can we use return enhancing features to try and get us closer to what mark to market version of private asset might look like.

作为收益分析平台，Venn投入大量精力开发收益调整功能以优化时间序列。针对私募资产，我们采用三种原理相似的方法：平滑处理、插值法和外推法。三者都依赖优质公开市场代理指标作为参照基准。若用苹果股票作为私募信贷指数的代理指标，必然导致垃圾进垃圾出的结果。

And you can imagine as a returns based platform, Venn puts a lot of time and effort into return adjusting features to help you get better time series. So in the case of private assets, there's three things that we use that are similar in spirit. The smoothing, interpolation, and extrapolation. And all three of them rely on a good public proxy in terms of using that as a reference point. So if I try to use these features on a private credit index with Apple as my public proxy, it's gonna be garbage in, garbage out.

去平滑处理旨在将私募资产调整为市价计量，通常会提高其波动性。这是后台运算最复杂的环节，我们采用Gitmansky、Lowe和Makarov在2003年2月论文中提出的方法，本质上是平滑过程的逆向工程。

Desmoothing is what tries to mark the private asset to the market. That typically raises its volatility. It's definitely the most complicated in terms of what's going on in the back end. For that, we use a process from Gitmansky, Lowe, and Makarov from a paper in 02/2003. Basically reverse engineers the smoothing process.

插值法将私募资产数据从季度频率转为日频，同样以公开代理指标为参照；外推法则估算其最新估值。外推法基于回归分析，平滑处理也依赖回归，而外推更接近直接算术运算。最终目标是回答：若私募资产采用市价计量，其形态会如何？

Interpolation, we're changing the private asset from quarterly to daily, again, using public proxy as a reference point. And extrapolation estimates where it would be up to date. Extrapolation's regression based. Smoothings, regression based, extrapolations, more like straightforward arithmetic. But at the end of the day, the goal is to get a hypothetical what if my private assets were marked to the market, what might they look like?

这不仅优化因子分析的可解释性——通过增强与因子的相关性通常会降低残差，从而揭示更多风险来源——从工作流程看也至关重要。向投资委员会汇报时，我需要展示组合当前状态，而非半年前的数据。

And you can imagine not only does this enhance factor analysis and make it more interpretable, you tend to get lower residual because now you're correlating more with the factors and things like that. You're explaining more of the risk. But also just from a workflow perspective. I need to go to my investment committee and give them an idea of what my portfolio looks like today. I can't be doing that as of six months ago.

当组合中10%是私募资产而90%可每日计价时，我需要以日频视角分析整体组合。这既是为了学术严谨性，更是为实现真正有意义的全组合分析。

I have 10% in private assets. 90% of my portfolio is liquid daily. I wanna use my total portfolio in a daily lens. So it's really about just getting somewhere that is probably academically more correct, but at the same time unlocking total portfolio analysis in a meaningful way.

你们通过去平滑、插值和外推三项技术，将私募资产收益流转化为接近日频市价计量的时间序列以获得更可靠分析。但这些终究是合成收益流——请问其局限性何在？比如机构若有20家PE管理人，尚可讨论通用PE指数；若仅有一家则面临更多特质风险。请谈谈实际操作中的摩擦与困难，以及配置者应如何看待基于这些合成数据得出的因子结论？

So you have these three techniques, desmoothing, interpolation and extrapolation to ultimately transform what are private asset return streams into something closer to daily mark to market time series that you can get a more robust analysis on. But ultimately these are synthetic return streams, they're estimates and maybe you can talk a little bit about the limitations. I would presume that an institution that has 20 private equity managers, you can probably just talk about a generic private equity index there versus if they have a single private equity manager, there's a lot more idiosyncratic risk. So talk about maybe a little bit of the friction and the difficulties there and what that ultimately translates to. And my question is how confident should allocators be in the factor conclusions that are drawn from these synthetic return streams?

首先明确：我们确实以指数为基准开展工作，因为将IRR转化为时间加权收益需要考量资金流动。我们处理的是代表性指数敞口。关于合成收益流的可信度问题——我们本就始于同等不信任的起点。

Yeah. So just quickly to that first point you made, definitely we're working with indexes here because you do see a certain amount of capital inflows and outflows in order to transform those IRRs to time weighted returns. We're working with representative index exposures here. How do you trust those synthetic return streams? I'll get into that, but I just wanna make sure we know where we're starting from.

以广义私募地产指数为例：2004年2月以来的波动率仅10%，同期公开地产波动率达23%。你可以对私募资产优势各有见解...

How do you trust the smooth returns? You're starting from a place where you have the same level of mistrust. So if you look at something like a broad private real estate index, if you go back to 02/2004, that's got a vol of 10%. If you look at public real estate over the same period, it's 23%. Now you can say what you want about the opportunities of private assets.

我们确实认为私募资产蕴藏巨大机遇。但必须指出，若认为其10%的波动率比公开市场低13个百分点就高枕无忧，这种想法是不明智的。这是基本前提。那么问题来了——让我们以收益平滑为例，因为这可能是最独特的特性——你如何验证这种方法能更接近真实情况？

We certainly think there's tons of opportunity in private assets. But I do think it would be ill advised to think that that 10% vol, 13 percentage points less than public markets. So that's the starting point. So then the question is, let's use the smoothing as an example because I think that's probably the most unique of the features. How do you verify that this is getting you closer to a better answer?

每次想到这个我都觉得好笑，因为私募资产根本不存在市价计量的回报流。你建立这个平滑模型试图逼近的，其实是个不存在的市价计量标准。如何逼近不存在的东西？这想法本身就很滑稽。这时就需要像蒙特卡洛模拟这样的模型验证手段。比如我们会自行生成1000个季度的市价计量回报流。

I always think about this and giggle because there's no mark to market private asset return stream. So you're building this model to smooth something, to make it closer to a mark to market return that doesn't exist. So how do you get closer to something that doesn't exist, which is always this funny idea to me. This is where in order to do something like model validation, you can use Monte Carlo simulations. For example, we would build our own 1,000 quarterly mark to market return streams.

接着对它们进行平滑处理，再用去平滑化流程检验是否更接近真实回报。本质上，我们通过比较去平滑后回报与真实回报的平均差异是否小于平滑版本的差异来衡量效果。简而言之，就是验证我们是否比放任不管更接近真相。

Then we would smooth them. And then we would use our desmoothing process to see if it's actually getting closer to those true underlying returns. And basically, we do that by measuring if, on average, the desmoothed returns are closer to the true returns, then what the average difference in returns are for the smooth version with the true one. Basically, are we getting on average closer than if we did nothing? That's the idea.

所以平滑处理真能让你更接近真相吗？当然有些前提条件必须满足。比如与公开市场代理变量的相关性必须达到特定阈值。我们为此设定了若干推荐条件。

So is the smoothing getting you closer? Of course, there's certain conditions that have to be met. Like an example is correlation to the public proxy. There's a certain correlation that needs to be met there. So we have these certain conditions that we recommend.

在这些条件下，我们的去平滑模型90%的情况下能更接近真实经济回报（再次强调这本身是虚构的，基于蒙特卡洛模拟）。若条件不满足——比如公开代理变量相关性不足——我们就不推荐使用平滑功能。关键启示是：配置者可以相信这些经实证检验的方法能改善你对风险的认知，尽管私募资产本就是信息黑箱。

And in those conditions, 90% of the time, our desmoothing model gets you closer to what the true underlying economic returns are. Again, asterisk, that doesn't exist, but our Monte Carlo simulations. 90% of the time that happens. Now if the conditions aren't met, for example, public proxy is not correlated enough, that's an example where we won't recommend a smoothing in the app. I think the takeaway is for allocators, have confidence that these are empirically validated ways that are trying to improve your understanding of risk in what is a undoubtedly opaque no win scenario.

但对这些模型要保持谦逊态度。你只是在试图理解市价风险的可能形态。我个人认为这确实能让你更接近风险本质，尤其当波动率远低于公开市场时，这至少是个改进起点。但任何私募资产建模都必须保持适度谦卑，确保你将其视为辅助理解的工具。

But stay humble in terms of these models or simplifications. You're trying to get a better idea of what mark to market risk might look like. I personally think it you're certainly getting closer to a true underlying feeling of, like, what the risk is, especially when volatility is just so much lower than public markets. It's an easy place to start from in terms of trying to improve on that. But again, any type of private asset modeling, there's got to be a certain amount of humility involved and just making sure you're understanding it as a form of context.

特别是在整体组合分析中，这种方法能破除私募资产带来的障碍——比如季度更新延迟，让你能并行分析公开与私募资产。

And I think specifically in terms of total portfolio analysis, a way to break down the barriers that private assets introduce like quarterly and not being up to date and just allowing you to do public and private analysis side by side.

当我们无法获取完整持仓数据时，基于回报的分析确实是唯一选择，但这种方法也有其弊端。比如设想一个机构配置者，其资产配置比例随时间显著变化或频繁更换管理人。这意味着历史回报无法代表当前组合。这种情况下如何解读因子分析结果？如何界定可接受的管理人更换频率和配置调整幅度，才能避免垃圾进垃圾出？

The returns based analysis that we're talking about here is really the only option when a full holdings look through isn't available but it does come with some of its own problems. So as an example, I'm imagining an institutional allocator that has meaningful asset allocation shifts over time or has meaningful manager turnover. What that means is that old returns aren't necessarily representative of the portfolio as it currently stands. How do you think about the interpretability of factor results in that context and where do you again draw the line as to how much manager turnover is okay? How much allocation shift is okay that the general results can be trusted versus, hey, this is gonna be garbage in garbage out.

非常合理的问题。基于回报的分析虽能跨越数据障碍，但确有局限。举个大学捐赠基金的例子：假设他们长期保持60/40配置。

Totally fair question. Obviously, returns based analysis allows a lot of getting over data hurdles, but there's cons to that too. Let's talk about an example where maybe you have an endowment. And let's keep it simple. Let's just say they have a sixty forty allocation.

十年间维持该配置后，某天投资委员会突然决定改为80/20激进配置。但基于十年回报估算的贝塔值那天显示的仍是60/40的特征。

So they've had that allocation for ten years. And then one day, someone from the investment community wakes up and they say, you know what? We're gonna make it an eighty twenty. We're going for it. So that day, your beta is gonna look exactly like a sixty forty because that was estimated using those ten years of returns.

随着这个80/20组合的每日运作，它会逐渐倾斜得越来越明显，但如果你有十年的历史数据，这个过程将极其缓慢。基于收益的因子分析无法立即捕捉到这些资产配置变化。那么有什么解决方法呢？你可以缩短观察窗口。窗口期越短，80/20配置对贝塔值的贡献比例就越大，然后你可以进行滚动因子分析。

And as every day is more of that eighty twenty portfolio, it's gonna start to tilt more and more, but that's gonna take forever if you have ten years of history. Returns based factor analysis does not pick up on these asset allocation changes immediately. So what are the ways around that? You could shorten your window. The less you make that window, the more percentage of contribution, you could call it the eighty twenty allocation, is gonna have on the betas, then you can do rolling factor analysis.

所以不仅仅是简单地缩短时间范围。你可以观察三年或一年的滚动趋势。例如查看十年期的滚动一年组合，最近一年期就能捕捉到80/20配置的变化，股票贝塔值会更快上浮。但即便如此，这也不是万灵药。

So it's not just about literally shortening the range. You could look at trends and rolling three year, one year. So if you look at a rolling one year portfolio over ten years, the most recent one year period is gonna pick up that eighty twenty. The equity beta is gonna drift higher faster. But even then, that's not a magic bullet.

它仍然无法即时反映变化。因此最实际的方法是：如果你想知道当前组合的未来表现，而历史收益又不具代表性，你可以使用模拟组合。直接输入80/20配置，在Venn系统中构建，抛开实际历史收益数据，仅用过去三到五年的80/20配置进行建模。

It's not picking it up immediately. So the most realistic way, if you're trying to say, my portfolio today, how do I use this factor analysis to model and stress test what it would look like in the future if the past returns aren't representative, it's you use a pro form a portfolio essentially. You would just put in an eighty twenty. You would build that in Ven. You would basically throw out the actual portfolio returns that you've had, and you would just model using an eighty twenty over the last three years or five years.

这仍隐含假设历史收益对未来建模有参考价值，但你现在使用的是模拟的80/20组合，它应该能反映你给组合带来的新风险。这就是基本思路。不过要说明，这并非万能方案。基于收益的因子分析在战略资产配置方面更有效，这与多数机构组合的特性相符。

Now you're still implicitly assuming that those past returns will be informative for future modeling, but now you're using a pro form a eighty twenty, which should represent your risk that you just introduced to the portfolio. So that's the basic idea. With that being said, I think it's totally fair to say that's not a solution for everything. Returns based factor analysis is definitely more powerful for strategic asset allocation. That aligns with most institutional portfolios.

重申一下，简约方法、因子视角、基于收益的分析——这些都是为战略资产配置者设计的。它很适合分析对冲基金，但不一定适合交易股票债券的对冲基金经理。我不信任它的情况？就是当发生完全的制度变迁时，当你不再相信历史收益模式的时候。

Again, the parsimonious approach, the factor lens, returns based analysis, this is all geared towards the strategic asset allocator. It's great for analyzing hedge funds, but not necessarily the hedge fund manager who's trying to trade stocks and bonds and things like that. So where do I not trust it? It's really when you have complete regime shifts. It's when you don't trust in the past return patterns.

你无法构建模拟组合。因子间的关系可能偏离未来预期。面对这类制度变迁时，你必须清醒认识工具的局限性。这种情况下，高频持仓分析等补充手段可能更有帮助。总体而言，基于收益的分析只是拼图的一部分。

You can't build a pro form a portfolio. The relationships between the factors themselves may not be as expected in the future. Those types of regime shifts are where you have to look at yourself and say, like, I understand the limitations of the tool. This is an example where a higher frequency holdings based, something like that may be useful as a supplemental understanding. And in general, returns based analysis is one piece of a puzzle.

持仓收益分析等手段可以协同工作，拼凑更完整的图景。这些案例说明了工具的局限性所在，关键是要理解这些边界。

It's something holdings returns. They can all work together to paint a more full picture. So that's some of the examples where it can be challenging and it's just about understanding the limitations of the tool.

我想探讨这些因子模型的可解释性与可操作性。延续之前的问题，正交化可能产生反直觉但符合设计的结果。比如比较外汇对冲与未对冲股票组合时，会同时显现利率和股票风险暴露。在目标不仅是获取信息更要采取行动时，如何指导用户理解这些结果？

I want to talk about the interpretability and actionability of these factor based models. Along the same lines of an earlier question, orthogonalization can lead to somewhat counterintuitive but intentional outputs. For example, you can see interest rate and equity exposure showing up when you compare FX hedged equity versus FX unhedged equity portfolios. How do you think about educating users on interpreting those outcomes, particularly where the goal is not just information but action?

这个例子很棒，Corey。我们要深入探讨这个案例。外汇对冲与未对冲股票的比较有个前提——底层股票完全相同，唯一区别在于货币对冲。

I love that example. Corey, we're gonna go on a journey here. We're gonna try to get real detail oriented in this and go through this example. What I like about hedge versus unhedged equities, again, the assumption here is that the underlying equities are the same. The only difference is a currency hedge.

这是Venn系统展现残差化输出的绝佳案例。直观上，你会预期外汇对冲组合的收益差异完全由Venn的外汇因子解释。假设出现美元贬值与利率走低的情况，持有非美货币的股票会受益，未对冲组合将跑赢。

One of the unique opportunities where Venn clearly shows the output of residualization in the tier system, everything we talked about. The intuitive at face value, what you would expect when you look at the relative return stream between a hedge and unhedged is that Venn's foreign currency factor would explain everything. I think that's the first layer of thought intuition. So in a hypothetical world, let's say you had lower dollar, lower rates happening at the same time. Stocks held in non USD currencies would benefit from that, unhedged would outperform.

在这个利率和美元双双下跌的世界里，这种相关性风险本质上解释了为何对冲策略优于非对冲策略。但您该将这种相关性风险置于何处？是归因于外汇？还是归因于利率？这是一个维恩决策问题。

In this world where rates and the dollar are falling, that correlated risk essentially is going to explain the outperformance of hedge versus unhedged. But where do you put that correlated risk? Do you assign it to foreign currency? Do you assign it to interest rates? This is a Venn decision.

您可以选择任何方式，但基于我们提到的所有理由，我们认为利率更具流动性、更易操作、属于更高层级。因此我们选择将这种相关性风险纳入利率范畴。当您观察对冲与非对冲策略的相对收益流时，不仅能看到外汇因素，还能看到利率贝塔值——正是它在提取这种相关性风险。但这并不意味着不存在外汇风险，事实恰恰相反。

You could do whatever you want, but that correlated risk, for all the reasons we mentioned, we think interest rates are more liquid, more actionable, higher tier. So we choose to take that correlated risk and put it into interest rates. When you're looking at the relative return stream between hedge and unhedged, not only do you see foreign currency, but you see that interest rate beta too, which is pulling out that correlated risk. Now it doesn't mean that there's no foreign currency risk. It's actually the opposite.

外汇仍是两种收益流之间最大的相对贝塔值来源。它仅代表与利率无关的独特部分。需要明确的是，利率并非唯一因素，股票等其他宏观因素也与之存在残差关系。至此您已识别出这种相关性风险。

Foreign currency is still the largest relative beta between the two return streams. It's just that represents the unique uncorrelated part with interest rates. Just to be clear, interest rates is not the only one. Equity, there's other macro factors that it's residualized with. So now you've identified this correlated risk.

您选择剥离这部分风险，并将其置于更高层级的宏观因素——更具流动性和实际操作性，因为请记住，我们的利率因子其实就是债券。同理，由于这只是货币对冲债券，假设您是配置者，职业生涯中应该熟悉这类敞口。您知道哪些管理人涉足其中，对投资组合中的情况也心中有数。

You've chosen to strip out that piece and put it in a higher tier macro factor that is more liquid and actual because we're talking about, remember, our interest rates factor is just bonds. And on that same note, because it's just currency hedge bonds, imagine you're an allocator. You probably are familiar with that exposure throughout your career. You know which managers have some exposure there. You have a good idea in your portfolio.

比如，我可以拉动哪些杠杆来影响利率贝塔值的变化？这正是构建这些宏观因素成为广泛可操作指数的部分原因。现在您观察两者差异时，若想对冲部分风险，不必局限于使用货币远期等工具来影响外汇贝塔值，而是可以关注债券等利率敏感型工具。

Like, what are the levers I might pull to affect change in that interest rate beta? That's part of why those macro factors are built to just be broad actionable indexes. So now you're looking at the difference between the two and you wanna hedge some of that risk. You're not forcing yourself into only using currency forwards or something like that to affect the foreign currency beta. Now you're able to look at interest rate sensitive instruments like bonds or things of that nature.

内容确实很多。试想在维恩模型上运行分析时，您不会每次都进行这种脑力训练，也不会深究某个微小贝塔值的出现原因。虽然这个完美案例让我们明白这是残差化过程，但在多资产组合中，您未必能清晰感知某个因子如何被其他因子剥离的脉络。

That was a lot. Think about running analysis on Venn. You're not gonna do that mental exercise every single time. You're not gonna scrutinize why this little beta is popping out somewhere Because we had this perfect example, we know it's residualization. But when you have a multi asset portfolio, you're not gonna necessarily get that clean feeling of this one factor and then how it's being other factors are being stripped out of it.

因此我认为首要的是，使用风险模型时必须理解其运作机制。就维恩模型而言，关键在于层级体系和残差化过程——您是否认同这套逻辑？毕竟因子模型揭示的这些波动性和相关性细微差别，无法从新闻标题中直接获取，某种程度上需要预期意外情况的发生。

So I do think that first and foremost, it's important when you are using a risk model to understand the risk model you're working with. For Venn, in particular, it's really the tier system, the residualization process. Is this something that I believe in? Because at the end of the day, a factor model is surfacing these little volatility and correlation nuances that you can't just read that in a headline somewhere. So there's some level of expecting the unexpected with a factor model.

所以我认为关键在于接受风险框架。并非说要忽视分析——当出现警示信号时当然应该核查，保持怀疑态度是好的。但您需要先接受整个流程，不能对因子模型中每个反直觉的细节都提出质疑，至少这是我的观点。

So I do think it's about accepting the risk framework. And then I don't want to say ignore the analysis. Like if you have a red flag going up and you should absolutely do that, it's good to be skeptical. But I do think you need to start by accepting the process and you can't question every little unintuitive thing when you're working with a factor model, at least in my opinion.

我想探讨压力测试和情景分析。多数配置者会从资产类别和历史事件角度思考这些问题，这也是我常见的研究方式。但若转向因子领域会呈现怎样的图景？又能解锁哪些新的洞察？

I want to talk about stress testing and scenario analysis. Most allocators think about these in terms of asset classes and historical events. That's often how I see this type of analysis performed. What does it look like when you move into the factor space instead? And what sort of insights can that unlock?

我们开发了名为敏感性分析的工具。假设用户选择标普500等市场指数，并设定情景：若该指数未来30天下跌5%，我的多资产组合会如何反应？我们通过回归分析将指数冲击（标普下跌5%）转化为因子冲击。在这个简化案例中，可能表现为本因子视角下的股票风险溢价下降约5%。将指数冲击转化为因子冲击后，只要掌握组合过去三年的风险敞口（我们倾向以此作为代表性数据）...

So we have something called sensitivity analysis. Imagine a user goes in, they pick a market index like the S and P 500, and they say, if the S and P was down 5% over the next thirty days, what might my multi asset portfolio do? What we do is we use regressions and we translate that index shock, the S and P down 5%, into a factor shock. You can imagine in this simplified example, that probably looks a lot like the equity risk premium in Ben's factor lens going down around 5%, something like that. So now once you've translated that index shock into a factor shock, if you know your portfolio's exposures over we like to use the last three years as representative.

你可以将这种因子冲击转化为投资组合冲击，这就是核心理念。回到之前的例子，如果我选择80/20配置比例，就是在模拟一个预设的80/20组合的冲击。从指数冲击到因子冲击，再到组合收益的传导过程都是线性的，基于历史因子关系建立。

You can translate that factor shock into a portfolio shock. That's the idea. And just going back to our other example, if I was gonna do this and I just switched to an eighty twenty, I'd be shocking a pro form a eighty twenty portfolio. Now everything I described, index shock to factor shock, factor shock to portfolio returns, that's linear. And it's based on historical factor relationships.

因此这种方法最适合轻度到中度冲击。众所周知，因子在危机时期往往表现异常——例如协方差矩阵会显示因子间相关性发生变化。这时我们会启用高斯混合模型，专门应对极端用户冲击场景，比如标普500下跌20%而非5%的情况。

So it works best for mild to moderate shocks. Factors, as we know, are not always well behaved, especially in a crisis, for example, you can actually see the covariance matrix, the correlations between the factors change. So this is where we use something called a Gaussian mixture model. And this is essentially for extreme user shocks. So now we're talking the S and P down 20%, not 5%.

高斯混合模型的工作原理是：将所有两倍标准差范围内的因子收益归类到重叠的正态分布中。这是种机器学习技术，我们虽无法命名这些聚类，但从因子收益与Baltil分布特征可明显看出它们对应着不同经济状态。

So what a Gaussian mixture model does is it basically takes those all the returns of the two sigma factor lens, and it clusters them into overlapping normal distributions. Now it's a machine learning technique. We don't know what those clusters are. They're not named for us. But when you look at the factor returns and the profile of Baltic, it's clear they're kind of economic regimes.

比如存在危机经济状态聚类，此时股票因子下跌幅度可能达40%-50%（具体数值记不清），这显然表征着危机环境。我们采用的四大状态聚类分别是：危机期、稳态期、通胀期和如履薄冰期。

For example, there's a crisis economic regime or a cluster and the equity factor is down. I don't know the number off the top of my head. Maybe it's 50%, 40%, whatever the case is. It's clear that that's representing a world with crisis happening. So the four clusters and regimes that we use are crisis, steady state, inflation, and walking on ice.

当用户输入重大冲击时，系统会计算该冲击属于各状态的概率（总和100%），然后采用加权平均的因子协方差矩阵将因子冲击转化为组合影响。这就是我们模拟极端环境下异常因子关系的方法。

Basically, happens is when a user puts in a big shock, it will try to map what the probability is literally out of a 100% that it's a crisis. It's a steady state. It's an inflation regime that we're working in. And then there's basically a weighted average factor covariance matrix that goes in to translating that factor shock into a portfolio impact. So that's the way we try to model the abnormal factor relationships that can happen in extreme environments.

这时你会看到多元化失效，已知的系统性关系瓦解，最终影响被分析组合或资产。再举个灵敏度分析例子：假设当前因子暴露已知但仅有五年历史数据，若想模拟2008年2月表现，可利用该时点的因子收益数据推演组合在全球金融危机期间的可能表现。

That's when you get a breakdown of diversification, the systematic relationships that you know, and then ultimately what that means for the portfolio or asset that you're analyzing. That's just one example. This sensitivity analysis, like, can also I won't go in-depth, but quickly, I know my factor exposures today. Maybe I only have five years of history, but I wanna see what the portfolio would do in 02/2008. Well, you have factor returns back to 02/2008, so you can go see, you know, given my exposures today, what might my portfolio have done during the global financial crisis, stuff like that.

但再次强调，所有这些都建立在历史收益模式具有建模参考价值的前提之上。

But again, all of it is on the assumption that those past return patterns are indicative for modeling.

Venn还提供因子对风险收益及多元化贡献度的评估。我好奇你们如何帮助用户将这些诊断转化为组合操作？特别是传统组合构建方式通常无法直接按因子调仓——调整某个管理人或资产配置会同时影响多个因子。

One of the other outputs that Venn provides or produces is this estimate of factor contribution to risk return and diversification. And I'm curious how you help users translate these diagnostics into action in their portfolio, especially when portfolios as traditionally constructed don't usually allow for direct rebalancing along factor lines. Moving one manager or asset allocation choice will impact multiple factors simultaneously.

这个问题触及更高层次的议题：Venn在资产配置全流程中的定位。虽然因子投资接受度在提升，但据我观察机构客户尚未普遍采用因子调仓，当前主流仍是资产类别层面的再平衡思维。

This is a great question, and I think it gets like a higher order theme here. Where does Venn sit in the asset allocation process in total? I mentioned about factor adoption earlier, and we're close, but we're not there yet. Institutional clients are not typically factor rebalancing. That's not, in my experience, where we're at today.

因此这些因子输出（风险/收益/多元化贡献）的核心价值在于：赋能投资者更透明地理解组合，揭示潜藏洞见与风险——那些波动率和相关性不会主动显现的维度。因子模型让整个资产配置决策更精准、更有目的性。虽然目前没人完全基于因子分析制定投资计划，但他们会开始思考：高收益债券经理是否真该归属固收类别？这类根本性问题。

Asset class rebalancing and that form of thinking is still where people are starting from. So for me, all of the factor output you mentioned, contribution to risk, return, diversification, this is really just empowering investors to be more transparent about their understanding of their portfolio, to surface hidden insights and hidden risks that they otherwise wouldn't be able to access because those volatility and correlations are not always jumping out at you, and that's what the factor model brings to the table. And really just to take the entire asset allocation process and be a lot more intentional and sharper with a decision. I don't think anyone's out there doing an entire investment plan using returns based factor analysis and rebalancing in that way, but they're maybe asking themselves, does my high yield bond manager really belong in my fixed income sleeve? Does that make sense?

还是它们实际上更像股权？我的对冲基金经理真的配得上他们的费用吗？或者他们只是系统性风险的暴露？我所有的资产类别真的实现了分散化吗？我认为仅仅增加这一层洞察就能促进更好的讨论、更好的投资组合构建、更好的决策制定。

Or are they actually more equity like? Is my hedge fund manager really earning their fees? Or are they just exposure to systematic risk? Am I actually diversified with all of my asset classes? I think just adding this layer of insight promotes better discussion, better portfolio building, better decision making.

我认为这本身就是简化复杂性，从而提升可操作性。再者，我们处理实际分析的方式——你知道，这种简洁的方法真正是为资产所有者设计的——本质上就是让这个过程对他们来说变得简单。我不确定是否直接回答了你的问题，但这确实是在寻找基于回报的因子分析如何融入投资流程，使其更稳健、更透明，而不是盲目地按因子调整配置并全盘接受所有输出结果。

I think that in and of itself is cutting through complexity, which promotes actionability. And then again, the way we approach the actual analysis itself, you know, parsimonious approach being built really with asset asset owners in mind, it's really just making that process easy for them. I don't know if I answered your question directly, but it's really just finding where returns based factor analysis fits as a way to just make any investment process more robust, more transparent, not necessarily just blindly trying to rebalance by factors and accepting all of the output.

克里斯，你们团队对Venn的方法论有过大量著述。不仅在这个播客中，团队发布的文献里也始终保持着高度透明公开。如果人们想了解更多、深入探究实际流程，他们可以在哪里找到信息？

Chris, you guys have written quite a bit about Venn's approach. You've been very transparent and public not only on this podcast, but again in the literature that the team has written. If people want to learn more and dive deeper into the actual process, where can they find information?

我们有个网站叫'Vem by Two Sigma'，上面有包含见解和资源的博客，还有个详细解释计算方法的帮助中心。但我建议人们先从博客开始了解，那里遍布着可以联系我们（比如通过邮件或表单）的链接，如果他们想用因子视角或Venn的因子模型来检视自己的投资组合。所以我会推荐从网站入手。

So we have a website, Vem by Two Sigma is the name, and there we have a blog with insights and resources. We have a help center where we really get into how calculation's done or things like that. But I would definitely start with the blog as a place where people can come and learn more. And then there's tons of links all over that where someone can find an email or click on a form that would put them in contact with us if they really wanna see how their portfolio might look through a factor lens or Venn's factor lens or things like that. So I would start with the website.

作为独立第三方，我不得不说你们提供的透明度让我能亲自验证你们的方法论——在内部复现后得到了与Venn完全一致的结果。这非常难得，让我深刻理解了你们的工作流程，而一比一的复现效果简直令人惊叹。我太欣赏这种透明度了。好了，节目接近尾声，我要问你本季问所有嘉宾的同一个问题（这季拖得比预期长很多，但问题不变）：最近痴迷于什么？

I will just say as an independent source, you guys provide enough transparency that I was personally able to go through your methodology, replicate it in house, come up with the same results that you guys were actually producing in Ven, which was really nice because it gave me a lot of deep understanding of how your process works and then to see it translate one for one was really phenomenal. So I love that transparency. Alright, we're at the end of the episode, I want to ask you the same question I ask every guest this season. This season is dragging on far longer than I expected, so we'll continue with the same question though. What are you obsessed with today?

可以是一个想法、一本书、一部剧，或是你正在进行的某项活动。工作之外当前最着迷的事物是什么？

This could be an idea, it could be a book, it could be a show, it could be something you're doing physically. Just what is something outside of work that you're currently obsessed with?

哎呀。我知道最想说的答案——干脆直说吧：我收集宝可梦卡牌。好了科里，

Oh, boy. I mean, I know the exact answer that I wanna say. I'm just gonna go ahead and say it. So I collect Pokemon cards. Okay, Corey.

先说清楚这点。宝可梦卡牌最有趣的是它们自成市场体系——密封版与非密封版。每张卡都有独立的时间序列走势。我最近痴迷于思考：能否构建一个宝可梦卡牌指数作为市场基准？因为有个发现——

Let's just get that out of the way right now. What's really interesting about Pokemon cards is they have this entire market to them, Sealed versus unsealed. Each Pokemon card has its own time series trend. And I've been obsessed with thinking about, like, can you build a Pokemon card index that you could use as a benchmark for that market? Because I got news for you.

如果你买入所有密封产品各一份（虽然我还没构建这个指数），但我很确信其表现会远超标普500。所以我总在思考：该采用什么方法论？哪些卡牌具有代表性？

If you took all of the different sealed products and bought one of them, I'm pretty sure I haven't built the index yet, but I'm pretty sure it would outperform the S and P 500 by a lot. So I don't know. I think about that a lot. Like, what's the methodology I would use? Which cards would be representative?

该用密封藏品吗？等权重？还是像喷火龙卡占指数70%那样的市值加权？不知道这算好答案还是坏答案，但我确实时刻都在琢磨这个。

Would I use sealed collections? Would I do equal weight? Would I do market cap weighted where like Charizard the Charizard card would be 70% of the index or something like that? I don't know if that's a good answer or a bad answer, but I think about that all the time.

作为一个从小在Game Boy上玩第一代宝可梦长大的人，我完全支持你。但我对这个答案有点失望，因为你并没有为这些卡片建立一个因子模型。

As someone who grew up with gen one Pokemon on the original Game Boy, I am all in with you. But I'm a little disappointed in the answer and that it's not you weren't building a factor model for the cards.

是的，是的。嗯，这是个例子。因为是基于回报的分析，如果你构建了一个宝可梦卡片的时间加权回报序列，你可以把这个时间加权回报上传到Ven平台，看看它是否与经济增长或股票因子相关。因为人们会认为，当经济增长良好时，你会购买更多宝可梦卡片。

Yeah. Yeah. Well, this is an example. So because it's return based analysis, if you built a time weighted return series of Pokemon cards, you could upload that time weighted return into Ven and see if it correlates with economic growth or the equity factor. Because one would think when there's good economic growth, you would be buying more Pokemon cards.

而当经济增长不佳时，你会减少购买宝可梦卡片。所以可能存在这种情况，宝可梦的风险敞口很大程度上可以由股票因子来解释。等我构建这个指数后，我们就能看看是否如此。

And when economic growth is not doing well, you would be buying less Pokemon cards. So there could be that the Pokemon exposure is highly explained by the equity factor. We'll see when I build the index if that if that's the case.

我们得做一期后续节目。克里斯，这次对话太棒了。感谢你的时间，真的很感谢你能来参加。

We'll have to do a follow-up episode. Chris, this has been fantastic. Thank you for your time. I really appreciate you joining me.

好的。谢谢你，科里。这是我的荣幸，感谢邀请我。

Alright. Thank you, Corey. It was an honor and thanks for having me.