Practical Lessons from Michael Mauboussin - Multiples Base Rates and Expectations Investing 20240923

 

邁克爾·莫布森的實用啟示——乘數基準率與預期投資 20240923

So that value growth distinction, I feel, I mean, Buffets talked about this, I feel that distinction doesn't make any sense. There are two ways to lose money. One's the old fashioned way, which is costs are greater than your revenues, and that's bad, right? So we don't want that. The second way is exactly what you describe Jack, which is that your investments are showing up on your income statement. The point I make over and over is that multiples are not valuation. Let me just stop there.
我認為那種價值與成長的區分毫無意義——巴菲特也討論過這點。虧損有兩種途徑：一是傳統方式，即成本高於收入，這顯然很糟；二是你剛才描述的狀況，傑克，也就是投資直接反映在損益表上。我反覆強調的關鍵在於：乘數並非估值本身。讓我在此打住。


Multiple are not valuation. They are a shorthand for the valuation process, and one should never confuse those two things. So you know, you can audit some of these, you mentioned all these guys doing these forecasts. You can audit what they've said at the past and see how they've done, and it's not very pretty, right? And you have to find and appeal to the base rate, which may not be your fingertips and often it's not. So you have to go out and make a little effort to find it. But once you do, I think it reshapes how you think a lot about the world. Welcome to two quans and a financial planner, where we bridge the world of investing in financial planning to help investors achieve the long-term goals. Join that Ziggler Jack forehand in me, Justin Carbno, as we cover wide range of investing in planning topics that impact all of us and discuss how we can apply them in the real world to achieve the best outcomes in our financial life. Jack forehand is a principal at the Lydia Capital Management. Matt Ziggler is managing director at some point investments. The opinions expressed in this podcast do not necessarily reflect the opinions of the Lydia Capital or some point investments. No information on this podcast should be construed as investment advice. Securities discussed in the podcast may be holding some clients of the Lydia Capital for some point investments. So if you just clip on a YouTube cover, you might be surprised to know that I am not Michael Mobison and Matt, you are not like a Mobison either. Well, let me just check my ID confirmed. But I think one of the things we want to do is we interviewed Michael Mobison a few years back in 2021. If one of our most watched excess returns episodes of all time, and one of the things we want to do is whenever we interview people like this, we wanted to still down the most important lessons someone can learn from them. And so what we're going to do is we're going to go back to that interview.
倍數並非估值。它們只是估值過程的簡化表達，千萬別將這兩者混為一談。要知道，你可以審核某些預測——你提到那些做預測的人們。你可以查核他們過去的言論，看看他們的表現如何，結果通常不太理想，對吧？你必須尋找並參考基礎比率，這可能不是唾手可得的資訊，而且往往確實如此。所以你得花點功夫去尋找。但一旦找到，我認為它會徹底重塑你對世界的許多看法。歡迎收聽《兩位量化專家與理財規劃師》，我們連結投資與財務規劃的世界，協助投資者實現長期目標。請跟隨主持人傑克·佛爾漢德與我——賈斯汀·卡爾諾——一起探討廣泛的投資與規劃主題，這些主題影響著我們所有人，並討論如何將它們應用於現實世界，以在財務生活中達成最佳成果。傑克·佛爾漢德是莉迪亞資本管理公司的負責人。麥特·齊格勒則是某點投資公司的董事總經理。本播客所表達的觀點不一定代表莉迪亞資本或某點投資的意見。 本播客中的任何資訊皆不應被視為投資建議。播客中討論的證券可能在某些時間點為 Lydia Capital 的部分客戶所持有。所以，如果你只是點開 YouTube 封面，可能會驚訝地發現我並非 Michael Mauboussin，而 Matt，你也不是 Mauboussin。嗯，讓我確認一下我的身分證——確認無誤。但我想我們要做的事情之一，是我們在 2021 年曾訪問過 Michael Mauboussin，那是我們有史以來觀看次數最高的《超額回報》節目之一。我們想要做的是，每當我們訪問像他這樣的人物時，我們都希望能提煉出人們能從他們身上學到的最重要教訓。因此，我們接下來要做的是，回顧那次訪問。

If you watched this interview, you're going to see a ton of Michael Mobison because we're going to play this in his words, we're going to play his clips. And then we're going to come in and we're going to talk about what we think long term investors can learn for him because they're so much to learn. And one of the things that surprised me, and I think you said the same thing when you listen to it is, like this is still very, very relevant today. And we did this interview in the end of 2021 and outside of maybe a minute or two where we talked about like what might happen in 2022 or something, almost all of it is still relevant today. Yeah, which will catch you off guard.
如果你觀看了這場訪談，你將會看到大量麥可·莫布森的內容，因為我們將以他的原話呈現，播放他的片段。接著我們會加入討論，探討長期投資者能從他身上學到什麼，因為可學習之處實在太多了。其中讓我驚訝的一點是，當你聆聽時，我想你也會有同感：這些內容至今依然非常、非常具有參考價值。我們在 2021 年底進行了這場訪談，除了可能有一兩分鐘談到 2022 年可能發生的事情之外，幾乎所有內容至今仍然適用。是的，這會讓你感到措手不及。

If you go back and listen to the original one, it'll be like, well, we think we know what will happen in 2021 or whatever, you're like, wait, wait, hold on. But Mobison's just one of those guys, too. I don't know about you, but I mean, I still go back and read those old like Mason posts. He's one of those people who's great for the way he thinks about markets, complex adaptive systems, valuations, all this stuff. He is a default evergreen thinker, his knowledge is forever compounding. So of course, when you have an interview with them, he's going to go into all these wonderful details and yeah, cannot recommend listening to the full one enough if this clip showed doesn't get you excited enough.
如果你回頭去聽原始的那一集，你會發現，嗯，我們以為自己知道 2021 年或什麼時候會發生什麼事，然後你就會想，等等，等一下。但莫布森也是那種人之一。我不知道你怎麼想，但我的意思是，我還是會回頭去讀那些舊的梅森貼文。他是那種思考市場、複雜適應系統、估值等所有這些事情的方式非常出色的人。他是一個典型的常青思想家，他的知識永遠在複利增長。所以當然，當你採訪他時，他會深入探討所有這些精彩的細節，是的，如果這個片段還不足以讓你興奮，我強烈推薦你去聽完整版。

And he's still put the stuff out today and like that's one of the challenges of interviewing them. And you can see we covered a lot of topics, which we're going to cover a lot today, because there's just so much stuff you want to talk to him about when you talk to him. Like you could talk to him for four hours, you know, and he's put out so much stuff, since he did this, hopefully we'll have him on again. But yeah, there's just so many lessons. And so we're going to go through 10 of them today. We've got a bunch of clips of Michael, you know, making the lesson, and then we're going to explain maybe what we think about it.
而他至今仍在持續產出內容，這正是採訪他時面臨的挑戰之一。你可以看到我們涵蓋了許多主題，今天也會探討很多內容，因為當你和他對話時，總有無數話題想深入交流。就像你可以和他暢談四小時那樣，自從這次訪談後，他又發表了許多新見解，希望未來能再次邀請他上節目。確實，其中蘊含太多值得學習的智慧。今天我們將重點探討其中十個要點。我們準備了麥可講解這些課程的多段影片片段，之後也會分享我們對這些內容的見解。

And the first one I think is really, really relevant based on where we are, especially in the wake of GameStop, because I don't know that too many people understand this definition. So here's Michael talking about the difference between investing and speculating. So yeah, to me, let's let's go back to basics, Jack, and I like the way you say this. Like when you, you know, we all get caught up in this, like how do we keep ourselves focused on the North Star? The key is to think about investing versus speculating, right? So investing is buying a partial stake in a business with an understanding that you are, you're going to get the benefits of that value growth over time. And speculating is buying something hoping that it'll go up, and is always like to say, most investors combine too. I don't want to say everyone, anybody's pure and not any of this stuff. But the reality is that you should separate those two things as you're participating in markets, right? So clearly, if you're buying or selling a meme stock, you're not doing it because you think there's fun, at least I presume that they, by the way, the original of GameStop, there was fundamental value. So I think the initial buying of that stock back a long time ago was a fundamental case. But as it starts to make these sort of extraordinary runs, it stops to be a stop being a fundamental case.
而我認為第一個觀點真的非常貼切，基於我們目前的處境，特別是在 GameStop 事件之後，因為我不確定有多少人真正理解這個定義。所以，以下是麥可談論投資與投機的區別。對我來說，讓我們回歸基礎吧，傑克，我很喜歡你這樣說的方式。就像當我們都陷入這種情況時，該如何讓自己專注於北極星？關鍵在於思考投資與投機的差異，對吧？投資是購買一家企業的部分股權，並理解你將隨著時間獲得價值成長的好處。而投機則是購買某樣東西，希望它會上漲，而且我常說，大多數投資者其實兩者兼具。我不想說每個人都是純粹的，沒有任何混合成分。但現實是，當你參與市場時，應該將這兩件事分開，對吧？所以很明顯，如果你買賣迷因股，你不是因為覺得有趣才這麼做，至少我推測他們——順帶一提，GameStop 最初是有基本面價值的。 所以我認為很久以前最初買入那支股票是基於基本面分析。但當它開始出現這種異常飆升時，就不再是基本面投資了。

So again, what are you doing? And by the way, there's no, I'm not making a moral judgment, speculations perfectly fine. In fact, speculation is actually a very, can some ways can be a healthy thing for markets in terms of liquidity and so forth. So let's be clear. That's okay, but just demarcate what you're doing, just to be clear about it. So I think that's the thing I would just remind people, what are you doing, you investing or speculating? So this one's a really good lesson for me, because I think a lot of people confuse these things and this came up in our interview with Aswell, the motor in as well.
所以再次強調，你究竟在做什麼？順帶一提，我並非在做道德批判——投機行為完全沒問題。事實上，投機在某些層面甚至能為市場帶來流動性等健康效應。讓我們把話說清楚：這沒什麼不對，但請明確劃分你的行為本質。因此我想提醒大家：你究竟是在投資還是投機？這對我來說是個寶貴的教訓，因為許多人常混淆這兩者，我們採訪艾斯威爾時也討論過這個問題。

This idea of am I really investing? Am I buying this thing because I've done a fundamental analysis of it and I think it's going to go up over a long period of time or am I buying this because I think the price is going to go up and that balance, there can be, as Michael talked about in the clip, there can be components of both of those to a successful investment strategy. But I think in terms of, especially in terms of thinking about your timeframe, it's really important to delineate when you start, which one of these am I doing? This is also a useful one to step away from just the investment mentality on it. Work with a lot of business owners in the day to day life, and we talk a lot about when you're working in the business versus when you're working on the business.
這個想法是：我真的是在投資嗎？我買這個東西是因為我對它做了基本面分析，並認為它會在長期內上漲，還是因為我認為價格會上漲？正如麥可在片段中談到的，一個成功的投資策略可能同時包含這兩種成分。但我認為，尤其是在考慮你的時間框架時，區分你開始時是在做哪一種，真的非常重要。這也是一個有用的方法，可以暫時脫離純粹的投資心態。在日常生活中，我與許多企業主合作，我們經常討論何時是在「經營業務」，何時是在「發展業務」。

And it's kind of like you could invest in a business or you can work in or for a business, work on the business itself, separating those two things out is really important because when I invest in a company, I'm actually starting to think about the profits in the future, the net present value of these different things, how it's all going to play out where funding is going to come from for an idea versus if I'm just betting on a stock and where it's going to go, those are two very different mindsets with very different ranges of outcomes and very different ranges of how you want to treat the position once it's, once it's under your control. So this one, I think, not just in your investing, it applies on much larger scales for what you're working in versus what you're investing or betting on.
這有點像是你可以投資一家企業，或者你可以在一家企業工作或為其工作，甚至經營企業本身。將這兩者區分開來非常重要，因為當我投資一家公司時，我實際上開始思考未來的利潤、這些不同事物的淨現值，以及一切將如何發展——比如一個點子的資金將從何而來。相較之下，如果我只是在賭一支股票及其走勢，這兩種心態截然不同，結果的範圍也大相逕庭，而且一旦持倉在你的掌控之下，你對待這個部位的方式也會有非常不同的考量範圍。因此，我認為這一點不僅適用於你的投資，在更大的範圍內也適用於你所從事的工作與你所投資或賭注的事物之間的區別。

Yeah, the other thing I think that was important that he said is neither one is necessarily good or bad. I think there's this belief by a lot of people, including me, that investing is this great thing and speculating is this bad thing. And that's, I think in a lot of cases that he is true. I mean, if you're buying game-stop call options and you don't have an edge in game-stop call options, then speculating is very bad. But I think in general, some people we know, we've had people in the podcast who you would probably describe their strategy is not rooted in fundamentals or buying a company over the long term.
是的，我認為他提到的另一點很重要：兩者未必有絕對的好壞之分。我想很多人（包括我自己）都抱持一種觀念，認為投資是件好事，而投機則是壞事。在許多情況下，這種看法確實成立——比方說，如果你買進 GameStop 的買權，卻對這類期權沒有掌握優勢，那麼投機行為確實非常糟糕。 但總體而言，我們認識的一些人——包括曾上過我們節目的來賓——他們的策略或許無法被歸類為根基於基本面分析或長期持有公司股份。

They are technically speculating, but they have an edge in that speculation. And so speculating cannot, it's not good or bad. It's just, it's probably important for you to know which one you're doing because, for instance, if I'm a value investor and I want to buy something because it's cheap or something like that. And then a month later, I'm thinking about selling it, well, I was speculating the whole time I was not looking myself as an investor and I've screwed myself up because I would need to know if I'm investing my time frame is a lot longer than a month. And so I think like bringing your time frame together with what you're doing is one of the important areas of delineating between investing and speculating.
從技術上講，他們是在投機，但他們在這種投機中具有優勢。因此，投機本身並沒有好壞之分。重要的是，你需要清楚自己正在做的是哪一種，因為舉例來說，如果我是一名價值投資者，我想買入某樣東西是因為它便宜之類的。然後一個月後，我卻考慮賣掉它，那麼，其實我從頭到尾都是在投機，我並沒有把自己視為投資者，這會讓我陷入困境，因為我需要知道，如果我在投資，我的時間框架應該遠超過一個月。所以我認為，將你的時間框架與你所做的事情結合起來，是區分投資與投機的重要環節之一。

Absolutely. And I think that right there, just, and again, it's like if I'm investing in, I have this long-haul mentality of what I am there for, I'm there for this long term, how outcome. If I'm speculating on, I'm thinking about, okay, if this should come to past that I expect to see this profit and I'm just treating it as an entirely, entirely different vehicle. So the time frames, the vehicle, the perspective, if you know what you're doing, Grant Williams talks a lot about this too, if you know what you're doing, it's really, really useful in teeing up your mindsets here in the right frame of reference to make better decisions and know if you have an edge or not. So this next one is probably the biggest lesson I've learned from Michael Mobson and that's a very, very high bar because there are so many lessons I've learned from Michael Mobson, but here he introduces the concept of expectation investing, which is a great, by the way, a great book he wrote together with Al Rappafort, that'll get into more detail, but here he explains it in more simple terms. So expectations of esting has three steps. The first step is to go backwards and say the only thing we know for sure in this whole equation is the price. So let's go back and reverse engineer using a discounted cash flow model, which is an appropriate way to think about economic both theoretically and I think practically, think about what has to happen for this current stock price to make sense, right?
確實如此。我認為關鍵就在於，如果我是以投資的心態進場，抱持著長期持有的想法，追求的是長遠的成果；而當我進行投機時，我思考的是：如果預期的事情發生，我就能獲利，這完全是兩種截然不同的操作方式。因此，時間框架、操作工具與視角都不同。如果你清楚自己在做什麼——格蘭特·威廉斯也經常談論這點——這對於建立正確的心態框架非常有幫助，能讓你做出更好的決策，並了解自己是否具備優勢。接下來這一點，可能是我從麥可·莫布森那裡學到最重要的一課，這個標準非常高，因為我從他那裡學到的實在太多了。他在這裡介紹了「期望投資」的概念，順帶一提，這是他與艾爾·拉帕波特合著的一本非常棒的書，書中會有更詳細的闡述，但他在此以更簡單的方式解釋。期望投資包含三個步驟。 第一步是回過頭來思考，我們在這整個方程式中唯一能確定的只有價格。所以讓我們回過頭來，用折現現金流模型進行逆向工程——無論從理論上還是實際應用上，這都是思考經濟問題的恰當方式——去推敲要讓當前股價顯得合理，必須發生哪些事情，對吧？

So that's going to be typically articulated in things like drivers of value, which would be sales, growth rate, margins, capital intensity, those kinds of things, right? And so the key to step one is to try to be sort of agnostic, right? Like you don't have a view of the world necessarily, you just want to say what has to happen or what does one need to believe for today's stock price to make sense, right? So if you want to metaphor for that, it would be, where is the bar been set for the bar a high jumper? We don't know how high the high jumper could jump yet, but we know the bar set of two feet, five feet, ten feet, whatever it is. Step two is an introducing historical analysis, but more importantly strategic and financial analysis to judge whether that company is going to meet, exceed, or come short of those expectations, right? So that's really where the rubber meets the road analytically. And again, history can be a really good guide for that, but it's also, and by the way, the thing that comes out that is really important is, you know, typically lower multiples or associated with lower expectations, a higher multiple high expectations, but you might notice that that whole discussion goes out the window, right? It's not really the difference, just low multiples. It's really how will the company perform vis-a-vis what's priced in, right? So that's step two.
因此，這通常會體現在價值驅動因素上，比如銷售額、增長率、利潤率、資本密集度等這類指標，對吧？所以第一步的關鍵在於保持中立，也就是說，你不必對市場有特定的看法，只需思考要讓當前的股價合理，需要發生什麼情況，或者需要相信什麼前提。如果用比喻來說，就像是跳高選手面前的橫桿設在哪個高度？我們還不知道這位選手能跳多高，但我們知道橫桿設定在兩英尺、五英尺還是十英尺。第二步則是引入歷史分析，更重要的是透過策略與財務分析，來判斷這家公司是否會達到、超越或低於這些預期。這才是真正考驗分析功力的地方。 再次，歷史可以是一個非常好的指引，但同時，順帶一提，從中得出的重要觀點是：通常較低的倍數與較低的預期相關，較高的倍數則與較高的預期相關。但你可能會注意到，這整個討論其實並不那麼重要，對吧？關鍵並不僅僅在於倍數高低，真正的重點是公司的表現將如何對比市場已反映的預期，對吧？這就是第二步。

And of course, the end, and I should say two that that is a very probabilistic exercise. We argue that coming out of step two, what you should have is a number of scenarios for potential outcomes, and you should attack probabilities to those. So we're really going to think about the world in an expected value terms rather than, you know, here's the answer, and then step three is, of course, by seller hold as appropriate based on steps one and two. But I love this idea, because first of all, we all know that, you know, the companies can be expensive. Companies can be cheap, but there's always expectations embedded in that price. And so anybody who thinks, well, I'm just going to buy this company because it's cheap, and I'm going to make a lot of money. By, if I'd buy coming to expensive, it's not going to do as well. Like, it doesn't work out that way, because the market on a regular basis is doing its best to try to figure out what these things are actually worth. And there's an expectation embedded in that stock price. And if I want to be, if I want to make money, I've got to be smart in that. I've got to figure out what is different in terms of the world that's going to play out, compare to what isn't embedded in that stock price. And I think just always thinking that way is such a good way to think and investing, because it's how you figure out if you truly have an edge, or if you're doing something that, you know, because you're just doing it, and you really have no advantage. Because if I'm buying something because that a P you've 12, and that's the only analysis I do, what, what edge do I have over all those people that have embedded all those expectations in that stock price? I came, I came into this book originally. I think was this book published late 90s first, sometime in the 90s I want to say. I'm not even positive. My feet. Okay. I remember because when he came on the interview, they had just done the revised edition.
當然，最後一點——我應該說，這是一項極具概率性的練習。我們認為，完成第二步後，你應該對潛在結果有多種情境設想，並為這些情境分配概率。因此，我們將真正以期望值的角度來思考這個世界，而非單純追求一個確切答案。而第三步，自然是根據前兩步的分析，適時進行買入、持有或賣出決策。 但我非常欣賞這個理念，首先，我們都知道公司股價可能偏高，也可能偏低，但其中總是蘊含著市場預期。因此，任何人若認為「我只因這家公司股價便宜就買入，就能賺大錢」，或是「若買入價格過高，就不會有太好表現」——事情並非如此運作。因為市場持續在盡力評估這些資產的真實價值，而股價中早已隱含了某種預期。若我想從中獲利，就必須在這方面展現智慧。 我必須弄清楚，相較於股價中尚未反映的部分，這個世界未來會有哪些不同。我認為，始終以這種方式思考是投資的絕佳途徑，因為這能幫助你判斷自己是否真的擁有優勢，或者你只是在做某件事，卻沒有任何真正的優勢。因為如果我買進某檔股票，僅僅是因為它的本益比是 12 倍，而這是我唯一的分析依據，那麼，相較於那些已經將所有預期都反映在股價中的人，我又有什麼優勢呢？我最初接觸這本書時，我想這本書是在 90 年代末首次出版的，大概是 90 年代的某個時候。我甚至不太確定。我的腳。好吧。 我記得，因為當他接受採訪時，他們剛剛完成了修訂版。

So I came to this book well after it was published, and I came after it after first discovering mobs and work when it was a lot more behavioral finance focused, still investment focused, but in focused on like, the errors we make, this is kind of like before I think fast and slow thinking fast and slow, condiments book came out. And what was really cool about this is you start with like all these mistakes you make in the real time, in the present, in understanding the future. When I went back and read expectations investing for the first time, the thing that really cemented in my brain about this is a lot of times when we're learning about investing, or we talked to people about investing, we go at this point in time and we play it forward. What expectations taught me visually, and this is not in the book, this is, you know, my knucklehead brain making sense of this, is never like you're not at zero going forward for a month or a hundred years. You actually start in the middle, and that's a really powerful thing to start in the middle, because when you start in the middle, all of a sudden you can look both backwards and forward. You can look in the rear view mirror, you can look out the windshield. And when you're doing that, when you're looking both backwards and forward, you can stop saying just only looking forward, how do I think I'm going to get to this potential price in the future? I'm also saying, how did I get to the potential price of where I am today?
所以我是在這本書出版很久之後才接觸到它，而且是在先發現了莫布辛及其著作之後才讀的——當時他的作品更偏向行為金融學，雖然仍以投資為核心，但重點在於我們所犯的錯誤，這大概是在《快思慢想》那本經典著作出版之前的事。這本書最酷的地方在於，它從我們在當下、在現實中理解未來時所犯的各種錯誤開始談起。當我回頭第一次閱讀《期望投資》時，真正讓我印象深刻的是：很多時候我們學習投資，或與人談論投資時，總是從當下這個時間點出發，然後向前推演。而「期望」這個概念讓我從視覺上理解到——這點書中沒寫，是我這顆笨腦袋自己領悟的——你從來不是從零開始，向前推演一個月或一百年。實際上，你是從中間開始的。從中間開始真是件非常有力的事，因為一旦從中間出發，你突然就能同時回顧過去與展望未來。 你可以回顧後視鏡，也可以眺望擋風玻璃前方。當你同時回顧過去與展望未來時，就能不再只專注於「我該如何達到未來的潛在價格？」而是同時思考：「我是如何抵達今日的潛在價格點？」

When you start to put that to the assumptions, what do we know now, and then what can we project into the future? It really helps me at least think of that broader range of outcomes that we're marching towards. More things can happen than will happen, one thing will happen, the past one thing happened. So how did I get here, and then how am I going to go there unpacking the expectations? I can't even begin to say how enormously useful that all was to me. And the other thing I was mentioned briefly before I moved to the next one is this idea about scenarios and probabilities is so important that you mentioned at the end. You don't end up with this process saying, well, the stock is trading at $25, the expectations are that, and I think it's worth $32, and that's the only thing that could possibly happen. That's not the way it works. You look at a bunch of different scenarios for the business and what it could be worth, and you kind of like wait them based on probability, and you come to something that you think is your best guess or your educated guess as to what might happen. So I think that's just another important part of it. So moving on to this next one, this is something that's been people have been talking about a ton, and it's interesting because we're still talking about it today, three years later, which is this idea that fundamentals don't matter. So here we ask Michael about that, and here was his take.
當你開始將這些套用到假設上時，我們現在知道什麼，然後我們能預測未來什麼？這至少幫助我思考我們正邁向的更廣泛結果範圍。可能發生的事情比實際會發生的更多，一件事將會發生，過去一件事已經發生。那麼我是如何走到這裡的，然後我將如何透過拆解預期來走向那裡？我甚至無法形容這一切對我來說有多麼巨大的幫助。在我轉到下一個話題之前，我簡短提到的另一件事是關於情境和機率的想法，這非常重要，你在最後也提到了。你不會以這樣的過程結束：嗯，股票交易價格是 25 美元，預期是那樣，我認為它值 32 美元，而這可能是唯一會發生的事情。事情不是這樣運作的。你會查看業務的多種不同情境及其可能的價值，然後根據機率對它們進行加權，最終得出你認為的最佳猜測或基於知識的猜測，關於可能發生的事情。所以我認為這是另一個重要的部分。 那麼接下來談談這個話題，這件事已經被大家熱烈討論許久，有趣的是三年後的今天我們仍在討論——也就是「基本面無關緊要」這個觀點。我們就此詢問了麥可的看法，以下是他提出的見解。

And then there are a whole other parts of the market that I think we're over here. That same exact fundamentals don't matter narrative. And that is when there's a new market developing or a new industry developing. One good example today is probably electric vehicles. I think we'd all agree that 15 or 20 or 30 years from now, electric vehicles would be more prominent on roads than they are today. But we don't know exactly how this is going to shake out, which companies are going to win, how the whole infrastructure is going to change and so on and so forth. So the typical way that markets, and this is again, these are patterns that have played out over centuries, this is not new, is you just throw a lot, you try out a lot of stuff.
而市場中還有其他領域，我認為我們正處於這種狀態——同樣是「基本面無關緊要」的論調。這通常發生在新興市場或新興產業發展的階段。當今一個很好的例子可能是電動車產業。我想我們都會同意，未來 15、20 或 30 年後，道路上的電動車會比現在更加普及。但我們無法確知具體將如何演變——哪些公司會勝出、整體基礎設施將如何改變等等。市場典型的運作模式（這種模式已持續數百年，並非新鮮事）就是大量嘗試、廣泛試錯。

You try out a lot of new companies, a lot of capital flows into it. It's essentially an evolutionary process. Huge jobs when you're a number of competitors, and then there's a weeding out process. The market is Darwinian and figures out what's going to go. So in those cases, in retrospect, it feels like the market does a really good job. But as you're living through it, it feels like there's a detachment between the prospects for particular companies and the money and the valuations and so and so forth. So so that was a little bit of what I was that was where I was trying to go with all this. And you know, the just a broader comment is it's very tricky because right now we have relatively low discount rates, which themselves imply very muted returns going forward across different asset classes. And in the case where companies have been able to grow, they're getting double benefits, which is obviously the value of the growth and especially the long duration growth and low discount rates. So that's why we start to see some valuations. And below, if you'd asked me, you know, I've been doing this since the mid 1980s, if you'd asked me, if we'd ever see, you know, 140 earthy, well, it was even below 1% 10 year, you know, back in the 1980s, I would have thought you were completely bonkers and I would have been a lot of money against that happening. But here we are, right, this is the world that we live in.
你嘗試了許多新公司，大量資金湧入其中。這本質上是一個演化過程。當競爭者眾多時會創造大量就業機會，接著便會經歷淘汰過程。市場遵循達爾文法則，會篩選出最終能存活的企業。因此回顧這些案例時，市場似乎運作得相當出色。但當你身處其中時，卻會感覺特定公司的前景與資金流動、估值表現之間存在脫節現象。這正是我試圖探討的核心觀點。更廣泛來說，當前情況相當微妙——我們正處於相對較低的折現率環境，這本身就預示著各類資產未來的回報將相當有限。而那些能夠持續成長的企業則獲得雙重優勢：不僅享有成長本身的價值（特別是長期成長性），更受益於低折現率的環境。這正是我們開始看到某些估值現象的原因。 而且，如果你問我，你知道，我從 1980 年代中期就開始做這行了，如果你問我，我們是否會看到，你知道，140 個基點，嗯，甚至在 1980 年代，10 年期國債收益率還低於 1%，你知道，我當時會覺得你完全瘋了，而且我會押上大筆錢賭這種事不會發生。 但我們現在就在這裡，對吧，這就是我們生活的世界。

So yeah, you know, again, I guess I'm expressing some faith in market efficiency, but of course, if you're an active investor, you have to believe in inefficiency and inefficiency at the same time, right, inefficiency means you create opportunities and I think that's always been true, but efficiency means eventually, if you're right about how you think about the value of the business, that price to value gap gets closed to something. This is something I'm guilty of. Is a lot of times this can be an excuse for people. This idea that, well, this stock had this huge return, you know, Tesla had this massive return. So fundamentals didn't matter and Nvidia is a good one today.
所以，是的，你知道，再次，我想我是在表達對市場效率的一些信心，但當然，如果你是一個主動型投資者，你必須同時相信市場無效率和效率，對吧，無效率意味著你創造了機會，我認為這一直是對的，但效率意味著最終，如果你對企業價值的看法是正確的，那麼價格與價值之間的差距會縮小到某種程度。這是我常犯的錯誤。很多時候，這可以成為人們的藉口。這種想法是，嗯，這支股票有這麼大的回報，你知道，特斯拉有這麼巨大的回報。所以基本面並不重要，而今天輝達就是一個很好的例子。

Like Nvidia is up so much like it's trading at this valuation fundamentals don't matter and most of the time, and he talked about the idea of electric vehicles and how it's very, very hard to price something like that early in the early stages, but most of the time, the market get these, get these things pretty right. And if you're sitting here looking at Nvidia going up as much as it has and said fundamentals don't matter, like go look at a chart of its sales or its free cash flow. Um, a lot of that tracks pretty closely to what the stock has done. So I think this has been somewhat of an excuse for people who like to buy cheaper companies in recent years to say fundamentals don't matter and I think there are reasons where fundamentals may matter less, but I think there's still a huge part of what goes on.
就像 NVIDIA 股價漲了這麼多，以至於它現在的估值讓人覺得基本面根本不重要，大多數時候都是如此。他談到了電動車的概念，以及在這類產業的早期階段，要為其定價是多麼、多麼困難，但大多數時候，市場對這些事物的定價還是相當準確的。如果你坐在這裡看著 NVIDIA 股價一路飆升，然後說基本面不重要，那不妨去看看它的銷售額或自由現金流圖表。嗯，這些數據與股價走勢其實相當吻合。所以我認為，這在某種程度上成了近年來喜歡買便宜公司的人的一種藉口，用來聲稱基本面不重要。我認為在某些情況下，基本面可能確實沒那麼重要，但我相信它仍然是影響市場運作的一個重要部分。

Everything. And this is something you and I talk about all the time when we talk with Ben Hunt and people like that too, everything that matters has a reason for mattering, predicting like what's going to matter what that reason's going to be, be it a catalyst or something else is one of the hardest things, one of the hardest things in life, let alone in investing. I just came across a story, I don't know if you know this, uh, Peter Atwater actually said this to me, the, uh, do you know the song the first cut is the deepest, are you familiar with the song? Okay. Do you know who wrote the first cut is the deepest?
一切。這正是你我經常談論的話題，當我們與班·亨特以及類似人士交流時也是如此——所有重要事物之所以重要都有其原因，而要預測什麼將會重要、那個原因會是什麼，無論是催化劑還是其他因素，這可謂最困難的事情之一，堪稱人生中最艱鉅的挑戰，更遑論在投資領域了。我剛好看到一個故事，不確定你是否知道，呃，彼得·阿特沃特其實跟我提過這件事——你知道〈First Cut Is the Deepest〉這首歌嗎？你熟悉這首歌嗎？好的。那你知道〈First Cut Is the Deepest〉是誰創作的嗎？

This is one of them. Is that a Cheryl Croson? Can I write? So great guy, but she, she performed it. She did. It was a massive hit for her, but this, this ties into the fundamentals matter, like it's a great song. It was also a hit for, uh, ride Stewart is a hit for a whole bunch of other people. It's actually been a hit song at least six times, I think it was written by, uh, Kat Stevens. And he wrote the song when he was 17 years old in like the 60s, he sold it for 30 quid. Did you ever perform it? Sure. He did. He did. It was a hit for him. I think second, I want to say it was after a P.P. Arnold had it as a hit, which is a totally different arrangement version of the song, but really cool, but this idea that you have a song that's fundamentally great, but nobody knows who Kat Stevens is. There's no reason for that song to matter when it was written.
這是其中之一。那是雪兒·克羅森嗎？我能寫嗎？真是個了不起的人，但她，她表演了它。她確實做到了。這對她來說是個巨大的成功，但這，這與基本面有關，就像它是一首很棒的歌。它也是，呃，羅德·史都華的成功之作，對其他許多人來說也是成功之作。實際上，這首歌至少成功過六次，我想它是由，呃，凱特·史蒂文斯創作的。他在 60 年代 17 歲時寫了這首歌，以 30 英鎊賣掉了它。你曾經表演過它嗎？當然。他表演過。他表演過。這對他來說是個成功。我想是第二次，我想說是在 P.P.阿諾德把它作為一首成功歌曲之後，那是這首歌一個完全不同的編曲版本，但真的很酷，但這個想法是，你有一首本質上很棒的歌，但沒有人知道凱特·史蒂文斯是誰。這首歌在創作時沒有理由重要。

So he sold it at like a bargain basement price, and then it's gone on to have all these successive versions that have been massive. And yeah, that, like the Cheryl Croson version was the most recent one that was freaking inescapable. So fundamentals matter, but they have to have a reason for mattering. And sometimes that means something just climbing and not going away for years. You don't have the greatest thing, but if the reason isn't applied to it, why other people are going to care, you can just sleep on it for almost forever, if not for forever itself. Fundamentals matter, but they have to have a reason to matter, and that's just really hard to know.
所以他以近乎清倉價賣掉了它，然後這東西卻接連推出了多個大獲成功的版本。沒錯，就像最近雪柔·克羅森版本那樣，簡直紅到無處不在。所以基本面確實重要，但它們必須有重要的理由。有時候這意味著某樣東西就是能持續攀升、多年不退燒。你手上的東西或許不是最棒的，但如果缺乏讓人關注的理由，你可能幾乎永遠——甚至真的永遠——都只能把它擱在一邊。基本面確實重要，但它們必須具備重要的理由，而這點真的很難預先判斷。

And this gets into our next one here, which is a lot of value investors, like me, didn't really see Amazon and Google in a lot of these big tech companies for what they were worth. And so we asked Michael, what do we know of that? That value growth distinction, I feel, I mean, Buffett's talked about this. I feel that distinction doesn't make any sense, and I'll just underscore that. But just a level set, and you're asking such a profound question, and it really is permeates the whole industry is, you know, going back to 1970s, tangible investments were double those of intangible investments, tangible, these are things you can touch and feel, so like factories, machines, inventory, right, touch and feel. Intangible assets by definition are non-physical, right?
這就引出了我們接下來要談的一點，那就是許多價值投資者，包括我自己，當初並沒有真正看清亞馬遜和谷歌這些大型科技公司的價值所在。因此我們請教了麥可，對於這一點我們該如何理解？我認為價值與成長的區分——巴菲特也曾談論過這個話題——在我看來這種區分其實毫無意義，這一點我想特別強調。但先讓我們建立一個基本概念，你提出的這個問題非常深刻，而且確實貫穿了整個產業：回顧 1970 年代，有形投資的規模是無形投資的兩倍。有形資產指的是那些你能觸摸和感受到的東西，比如工廠、機器、庫存，對吧？就是能實際觸碰的。而無形資產根據定義則是沒有實體的，對嗎？

So these are things like software, code, or instruction, or training, or branding, those kinds of things. And so that was two to one tangible intangible, and now that relationship is flipped. So twice as much intangible than intangible, intangible, so that's the basic observation. I think most people agree that makes, that makes common sense. But here's why it becomes tricky from, from an investing point of view is, tangible investments are historically have always been on the balance sheet, right? So you capitalize and make and depreciate them over time by contrast, intangible investments by convention are expense, and so as a consequence, they show up as an expense and hence they, they hurt earnings, right?
這些包括軟體、程式碼、指令、訓練或品牌等類型的東西。因此，過去有形與無形資產的比例是二比一，而現在這個關係已經翻轉。所以無形資產的數量是有形資產的兩倍，這就是基本的觀察。我認為大多數人都同意這符合常識。但從投資的角度來看，這之所以變得棘手，是因為有形投資歷來總是出現在資產負債表上，對吧？所以你可以將其資本化並隨著時間折舊；相比之下，無形投資按照慣例則被視為費用。因此，它們會以費用的形式出現，從而影響收益，對吧？

You have less earnings. And so the key question is of the spending, let's say, SGNA spending, what percent of that SGNA is necessary to maintain the business, and what percent is discretionary in pursuit of evaluating growth. And I think this when you talk about these sort of large companies, what I think the market may have missed is that they were making enormous investments that were showing up their income statement, which made their earnings look very modest, relative to their actual economic propositions. And you know, today we have very specific examples of things like, you know, think of software as a service, you know, it may, it may be very expensive for me to acquire you as a customer, right? So my customer acquisition cost may be relatively high, but once I've got you as a customer, I know that there's going to be a stream of cash flows going down the road, right? So from an accounting point of view, however, and pursuing that that's an NPV calculation, right, that it's good for me to have you as a customer, the faster I grow, the more I'm going to lose money, right, which is horrible in some ways, right? So I think that's the distortion we have to get past. So, you know, the report we wrote about this is called one job, and what we, what we argued for is it as an investor, you need to go down to the basic unit of analysis, right, which is understand how that company makes money, and then separately focus on the cash flows in order to understand the prospects, right? So if you do those two things, and again, you know, you're jack, you've sort of said this long-term thing, you want to have a north star, right? In all these cases, you want to have a north star that guides how you think about these things, and that's a good example of the accounting is just not kept up with the actual underlying economics. Yeah. So this is an interesting topic because he's 100% right about this, I mean, basically companies today are investing for their future in a different way than companies in the past and investors for their future.
你的收益減少了。所以關鍵問題在於支出，比如說 SGNA 支出，其中有多少比例是維持業務所必需的，又有多少比例是為了追求增長而可自由支配的。我認為當談到這類大型公司時，市場可能忽略了一點：它們正在進行巨額投資，這些投資體現在損益表上，使得它們的收益相對於實際的經濟價值顯得非常有限。你知道，今天我們有非常具體的例子，比如軟體即服務（SaaS），獲取你作為客戶的成本可能非常高，對吧？所以我的客戶獲取成本可能相對較高，但一旦我讓你成為客戶，我知道未來將有一系列現金流產生，對吧？然而，從會計角度來看，追求這一點其實是一個淨現值（NPV）計算，對吧？擁有你作為客戶對我是有利的，但我成長得越快，虧損就越多，這在某種程度上是很糟糕的，對吧？所以我認為這是我們必須克服的扭曲現象。 所以，你知道嗎？我們撰寫的這份報告名為《一項工作》，我們主張的是：身為投資者，你必須回歸到最基本的分析單位，也就是理解那家公司如何賺錢，然後再獨立關注現金流，以便掌握其前景，對吧？如果你能做到這兩點，而且，你知道的，就像傑克你長期以來所說的，你需要一個指引方向的北極星，對吧？在所有情況下，你都需要一個北極星來引導你思考這些事情，而這正是會計未能跟上實際底層經濟狀況的一個好例子。是的。所以這是個有趣的話題，因為他在這點上百分之百正確，我的意思是，基本上，如今的公司正在以不同於過去的方式為未來投資，投資者也是如此。

You're seeing a lot more R&D, you're seeing a lot more SGNA, you're seeing, you know, less of the hard capex you saw, and so one of those things, you know, R&D is expense, SGNA, and more advertising expense are expense, and so those companies are going to look less profitable on their financial statements than somebody who's, you know, on old school company. So it definitely right, but I think it also, it's also very interesting in that it, it's a very tricky thing to do, and this, this is true of the capex and the R&D and everything together, which is, it doesn't mean that we necessarily would have seen Google and Amazon if we had known this because pets.com was doing a shitload of R&D as well, like, and it didn't go that well for pets.com, the R&D.
你看到的是更多的研發支出，更多的銷售管理及行政費用，而過去常見的硬性資本支出則相對減少。其中關鍵在於，研發屬於費用，銷售管理及行政費用以及更多的廣告支出也都是費用，因此這類公司在財務報表上，看起來會比那些傳統老牌公司的盈利能力更低。這點確實沒錯，但我認為這也相當有趣，因為這其實是件非常棘手的事——無論是資本支出、研發支出，還是所有這些加總起來，都不代表我們若早知如此就必然能識別出 Google 和 Amazon。畢竟 pets.com 當年也投入了大量研發，但結果對他們來說並不理想。

So we had every company, what they're going to get out of that R&D in real time and the present is very hard to figure out, but he's 100% right that that's what we missed is, you know, we look at these companies, and we didn't necessarily see what was going on, which is things we're getting expense to make them look less profitable, but those things were the things that we're going to lead to them being huge companies in the future. Back to this matters, like, what matters is what matters, like over time, and that's going to change over time. If you were just starting at the beginning and you're looking at, you know, in an Amazon versus a Walmart or whatever, whatever the scenario you want to come up with, if you're just starting out at the beginning and looking forward and not thinking about what is what got us here, if you're thinking about what got us here, you can start to say, well, this stuff that's happened in the period leading up to the price today, I actually see this change in expectations. If you totally ignore that and just go, I look at the past as a perfect regression of this thing and I'm just using price to book and I'm ignoring everything else. Well, you ignored all the changes in the accounting standards, you ignored all the changes in raising capital and all the things that have gone on, and those all have frustratingly small sample sizes and whatever else, frustratingly smaller than the empirical sample sizes of the data you're trying to pull from to do that regression analysis and find your factor. So this is another reminder where like when you start in the middle and you look backwards, you're not just looking backwards statistically, you're looking backwards at what's mattered, how's it evolved over time and then using that to map forward? That doesn't mean you're going to pick and hold on to Amazon in 1999, but it does mean that you have some baseline awareness that, hey, maybe tangible versus intangibles is kind of an trend of changing a lot and even if it doesn't mean revert all the way back to where it was in the future, certainly some of the stuff is going to upset the old apple cart. I'm going to respond to that and I also want to use an example, but first I want to play this next clip because we also asked him about this idea, you know, if you look at the Russell 2000 today, there are way more money losing companies than there have been for a long period of time. And we asked him whether that means anything because he in his answer, he brought up the idea of Walmart, you brought up. So I think that's something important to talk about, but I want to play the clip first. There are two ways to lose money, one's the old fashion, which is costs are greater than your revenues and that's bad, right? So we don't want that. The second way is exactly what you describe Jack, which is that your investments are showing up on your income statement, those are attractive and pv positive investments and as a consequence, they don't really have, they don't really tell you about the value of the company. And so that to me is the distinction that you want to make sure you're making. You know, one of the things I always like to point out is that, you know, Walmart for the first 15 years, it was public had negative free cash full every year. So it was profitable, but it invested more than a turnings and hence free cash flow was negative. Walmart was spectacular, right? I mean, he had great returns on capital to stock, perform three times the benchmark of the S&P 500 over that period. So it was a great stock. But again, that's only a message of the accounting, right? So let's say the modern-day version of Walmart would have been expensing all those same investments. It would have looked like it was losing money. The free cash will be a bit exactly the same and would have had, you know, would have had similar type of economic performance. So so let's not get confused by the accounting. Let's focus the focus on that trail of what is the underlying economic proposition. And again, that's why we call one job. Let's keep our, let's focus, you know, like they say in basketball when you're playing defense, watch the hips, right? Because everything's going to follow the hips. And that, this is a little bit of following the hips kind of idea, which is, let's let's focus on the basic unit of analysis and really understanding that. So to your point, look, it's not, eventually all companies have to, you know, you want, you want to make sure that your economic proposition is good. But yeah, losing money in and of itself does it really tell you whether a company is attractive business. Yeah. So a couple of things on this. First of all, this idea, you know, Walmart and Amazon are not that different. Now, what was different is the money Amazon was spending for its future growth was treated differently from an expense standpoint. Like Amazon was, was a better than Walmart on a free cash flow basis. Walmart was better than Amazon on an accounting basis. But they both were doing the same thing. They both were investing in very high profitability things with a long-term focus, knowing that eventually this was going to make them a lot of money. And so we want to look at both of them differently. And obviously, they're different businesses. They have different margins. They have all that stuff. But they, they both were doing sort of similar, that similar ideas behind what they were doing. Just one was being treated completely differently on the accounting statements than the other was. This basic idea that that, that Mobison will talk about over and over again is just building a model of a company to understand where's where's top line revenue come from, then what do you do with that revenue? And I think it gets to this fundamental case in these money losing companies when we talk about them. It's just asking the question, is this thing bootstrapped? Is the money that it's being reinvested in everything else? Is it entirely coming from the company? And from revenue from stuff, it's selling and then how are they, how are they accounting for that? Or is it venture backed? Or is it just, is it backed by something else? Are they taking loans from a bank to do this? Are they taking loans from, are they issuing bonds to do this? Are they issuing stock to do this? And when you start to do this, you start to see both the shape of the different business models, the shape of the decisions being made. And then the shape of the different ways you can start to think about what is not just the business practices, the business model itself, the, the everything store online versus the everything store in the purest retail sense. But how do they arrive at growing that enterprise? How do they arrive at that when you play it out three or five years, 10 years, however many years into the future, if this is something you are going to invest in or speculate on, you might say, okay, if this money is getting raised by shareholders and these things are getting plowed in, well, they have a structural advantage over somebody who's only bootstrapping this stuff to get to the same point. If this works out for them, they could be way ahead on the curve with the consumer in the future. You can start to see and now in the rear view mirror how that took place and how Amazon was able to go into all sorts of other markets in front of, in front of Walmart. It's really, really fascinating thing to look at pairs like this that had similar looking businesses, but fundamentally totally different business models. Let me give an example because it relates to like the business on and because this is how it really like I understood this concept. So we're in the subscription business. Let's just say to use round numbers, we charge $250 a year for validity. Now let's say it costs us, basically, let's say it costs us $300 to acquire a subscriber in our average subscriber stays of less for two years. So my value of that subscriber is $500 because they stay for two years. I'm acquiring that subscriber for $300. That is a profitable long-term transaction, but now let's go back to the accounting statement to let's say every time I acquire a subscriber, I'm taking their annual fee they pay to us. I'm investing it back to acquire more and more subscribers. What do my accounting statement look like? They look like a disaster. I am losing money every single year because but I'm going to get big payouts in the future and it and if you think of about a business like Salesforce, that sort of explains what they did. They were spending a bunch of money to acquire a customer, but Salesforce is a much better business than like a subscription investment business because a subscription investment business, you know, you might have a couple years as like your average life of a subscriber for a Salesforce. It's a really, really long time which is similar like the asset management business which both of us are in. So you can pay a lot of money to acquire these people. It can look forable in the present, especially if you're investing that back, that money back to acquire even more and more people, but long-term it ends up being an incredibly profitable transaction. So I think that's a simple way to think about like a business like Salesforce and what people were getting wrong about it. And then who you're trying to appease with that decision? If you have investors in the business who are committed to that long-term vision and aren't looking for a quick turn, people who aren't speculating on what you're going to do next, it's a lot easier to appeal people if you have them in that long-term vision if you, you know, are promising that your car company's going to go to Mars or something. The longer-term that thing is, the more people who are invested in can stay true to that story because they can say I need all this time for it to play out. The more you have pressures, whether it's from a bank because you have a loan or it's from shareholders who want to get paid out that dividend, the more pressure you have on you to maybe not make that decision. Not look at that. I'm two years to profitability and here's my churn rate on my customer versus my lifetime value and all those very important things that you have to bake into a business model. Where are the money comes from? How you're getting there and how you tell that story? Kind of everything. That's how you define to the people who are giving you that money, how it matters. So there's next one of the most eye-opening one of what he said because you'll see people drop multiples all the time like I do at all the time. You probably do it. It's like, oh, this company traded 15 times earnings. But how many people who say that can actually explain what that means.
所以我們對每家公司的研發成果及其即時與當下的價值都難以準確評估，但他百分之百正確地指出了我們所忽略的關鍵：當我們審視這些公司時，未必能洞察到正在發生的變化——那些看似降低盈利能力的支出，實際上正是未來促使它們成為巨頭的關鍵因素。回歸到核心問題：真正重要的是那些隨時間推移而持續重要的事物，而這些事物本身也會隨時間演變。 假設你從零開始觀察，無論是亞馬遜對比沃爾瑪，或是任何你想像的情境，若你僅著眼於未來發展而不思考「是什麼造就了現狀」，那麼你將錯失關鍵。但若你深入探究「造就現狀的原因」，便能開始理解：在當前股價形成前的時期裡發生的種種變化，其實反映了市場預期的轉變。倘若你完全忽略這點，僅將過去視為完美的回歸模型，只依賴市淨率而漠視其他所有因素——這無疑是片面的。 好吧，你忽略了會計準則的所有變動，忽略了籌資方式的所有變化，以及所有正在發生的事情。這些變動的樣本數都少得令人沮喪，無論如何都比你試圖從中提取數據進行迴歸分析、尋找影響因素的實證樣本規模要小得多。這再次提醒我們：當你從中間開始向後看時，你不僅是在統計上回顧過去，更是在審視哪些因素曾經重要、它們如何隨時間演變，並以此推演未來？這不代表你會選擇並死守 1999 年的亞馬遜，但確實意味著你該有個基本認知：嘿，有形資產與無形資產的消長可能是個劇烈變動的趨勢，即使未來不會完全回歸到過去的狀態，某些變化肯定會顛覆舊有的格局。 我接下來會回應這點，同時也想舉個例子說明，但首先我想播放下一段影片片段，因為我們也問了他關於這個觀點的看法——你知道，如果你觀察當今的羅素 2000 指數，會發現虧損公司的數量比過去很長一段時間都多得多。我們詢問他這是否具有任何意義，因為他在回答時提到了沃爾瑪的例子，這正是你剛才提及的。所以我認為這點值得深入探討，但我想先播放這段影片片段。 虧損有兩種形式：一種是傳統模式，即成本高於營收，這顯然不是好事，對吧？所以我們要避免這種情況。第二種則完全如你所述，傑克——那些投資項目呈現在損益表上，其實是具有吸引力且現值為正的投資，正因如此，這些數字並不能真正反映公司的價值。對我而言，這正是需要明確區分的關鍵點。我常喜歡舉例指出：沃爾瑪上市後的頭 15 年裡，每年自由現金流都是負值。 所以它是盈利的，但投資額超過了營業額，因此自由現金流為負。沃爾瑪表現驚人，對吧？我的意思是，它的資本回報率很高，股價表現是同期標普 500 指數基準的三倍。這確實是一支很棒的股票。但同樣地，這只是會計上的表象，對吧？假設現代版的沃爾瑪將所有這些投資都列為費用，那麼它看起來就像在虧錢。自由現金流其實完全相同，而且會有相似的經濟表現。所以我們不要被會計數字搞混了。讓我們專注於根本的經濟命題。這也是為什麼我們稱之為一項工作。就像籃球防守時常說的：盯住對方的臀部，對吧？因為所有動作都會跟著臀部移動。這有點像「盯住臀部」的概念，也就是專注於分析的基本單位，並真正理解它。 所以回到你的觀點，你看，這並不是說，最終所有公司都必須，你知道，你希望確保你的經濟主張是好的。但沒錯，虧損本身是否真的能告訴你一家公司是否是有吸引力的業務。是的。關於這點有幾件事要說。首先，這個概念，你知道，沃爾瑪和亞馬遜其實沒有那麼不同。現在，不同的是亞馬遜為了未來增長所花費的資金，在費用角度上被以不同方式對待。就像亞馬遜在自由現金流基礎上比沃爾瑪更好。沃爾瑪在會計基礎上比亞馬遜更好。但它們兩者都在做同樣的事情。它們都在投資於具有長期關注的高盈利能力項目，知道這最終將為它們帶來大量資金。因此我們想以不同的方式看待它們兩者。顯然，它們是不同的業務。它們有不同的利潤率。它們有所有那些東西。但它們，它們兩者所做的背後都有某種相似，那種相似的理念。只是一個在會計報表上被以完全不同的方式對待，而另一個則不是。 莫比森反覆強調的基本概念，就是建立公司模型來理解其營收來源，以及如何運用這些營收。我認為這觸及了我們討論虧損公司時的核心問題：這家公司是否自給自足？其再投資的資金是否完全來自公司自身、來自銷售商品的收入？他們又如何處理這些帳務？或者這家公司是創投支持的？還是有其他資金來源？他們是否向銀行貸款？是否發行債券？或是發行股票來籌資？當你開始這樣分析，就會逐漸看清不同商業模式的輪廓、決策背後的形態，以及各種思考方式——不僅是商業實踐或商業模式本身，更包括「線上萬物商店」與純粹零售意義上的「實體萬物商店」之間的差異。 但他們是如何實現企業成長的呢？當你將時間軸拉長到三、五年甚至十年後——若這是你要投資或投機的標的——你可能會思考：如果這些資金是透過股東籌集並持續投入，那麼相較於僅靠自有資金逐步發展至相同規模的競爭者，他們確實擁有結構性優勢。若策略成功，他們將能大幅領先市場曲線，搶佔未來消費者心智。如今回顧歷史，我們能清晰看見亞馬遜如何以此模式在各類市場中搶佔先機，甚至領先沃爾瑪。觀察這類看似業務相似、商業模式卻根本迥異的企業對比，實在引人入勝。讓我舉個切身例子來說明，因為這正是我理解此概念的契機：我們身處訂閱制產業。假設以整數為例，我們每年收取 250 美元會員費。 假設我們獲取一位訂閱者的成本大約是 300 美元，而平均每位訂閱者會停留兩年。那麼，這位訂閱者對我的價值就是 500 美元，因為他們會停留兩年。我以 300 美元的成本獲取這位訂閱者，這是一筆長期來看有利可圖的交易。但現在，讓我們回到會計報表上：假設每次我獲取一位訂閱者，我都會將他們支付的年費重新投資，用來獲取更多訂閱者。那麼，我的會計報表會是什麼樣子？它們看起來會像一場災難——我每年都在虧錢，但未來我將獲得巨大的回報。如果你想想像 Salesforce 這樣的企業，這在某種程度上解釋了他們的策略：他們投入大量資金來獲取客戶，但 Salesforce 的業務模式比訂閱制投資業務要好得多，因為在訂閱制投資業務中，訂閱者的平均停留時間可能只有幾年，而 Salesforce 的客戶停留時間非常長，這類似於我們兩人都從事的資產管理業務。 因此，你可以花費大量資金來收購這些人。這在當下看起來可能很划算，尤其是如果你將這些資金再投資，用來收購越來越多的人，但長期來看，這最終會成為一筆極其有利可圖的交易。所以，我認為這是理解像 Salesforce 這樣的企業以及人們對它的誤解的一種簡單方式。然後，你試圖用那個決定來取悅誰？如果你有投資者，他們致力於那個長期願景，而不是尋求快速回報，不是投機於你下一步要做什麼的人，那麼如果你讓他們參與那個長期願景，比如承諾你的汽車公司將要前往火星之類的，就更容易吸引他們。那個願景越長遠，投資其中的人就越能忠於那個故事，因為他們可以說我需要所有這些時間來實現它。你面臨的壓力越大，無論是來自銀行因為你有貸款，還是來自股東希望獲得股息分紅，你就越有可能不會做出那個決定。不會去考慮那個。 我距離盈利還有兩年時間，這是我的客戶流失率與終身價值的對比，以及所有那些必須融入商業模式的重要事項。資金從何而來？你如何達成目標？又該如何講述這個故事？基本上涵蓋一切。這就是你向提供資金的人們闡明其重要性的方式。接下來是他所說最令人大開眼界的一點——因為你會看到人們總是隨口拋出倍數數據，就像我經常做的那樣。你可能也這麼做過。比如說：「這家公司以 15 倍本益比交易。」但有多少說這話的人能真正解釋這意味著什麼？

So he talks about earning the right to use multiples. The point I make over and over is that multiples are not valuation. Let me just stop there. Multiple are not valuation. They are a short hand for the valuation process and once you're never confused those two things. Right. So the valuation process is the present value future cash flows. Multiple are a short hand. Now what's good about multiples? What's good about short hands in general? Right. They save you time. Right. And by the way, I should just be clear. I use multiples. If you were an hour having a commerce at casual conversation, we might I would maybe drop multiples about a particular business. Whatever. That's fine. But the key is that you understand the economic implications of the multiples that you're using. So you're saying, I think this should be a 15 times EBITDA or the 30 times earnings. So what is that? What do I have to believe for those multiples to make sense? And so as you know, we spend a lot of time writing about we wrote a piece called What does a PE multiple mean? We wrote a piece called What does an EVD? But down multiple mean, essentially creating a bridge between those multiples as people tend to use them. And the underlying economic assumptions that you need to make in order for those to justify those multiples. And just to be really explicit about those things. And you know, as what the motor in at New York University sort of the dean evaluation, he's talked a lot about this. He surveyed investor reports and he's found or analyst reports, pardon me, he's found that nine out of ten rely predominantly on multiples. So this is how people tend to talk to one another. So again, as I tell my students, the end of the sort of end of our evaluation module, you have to sort of earn the rights to you multiple. You can use them, but earn the right. And the way you earn the right is to demonstrate that you understand the underlying economic assumptions that are embedded, right? So the last thing I'll say, and it goes back to expectations, investing broadly speaking, which is the assumptions about future value creation, investment needs, all the, that's implicit in a multiple. It's implicit. It's not that it's not there. It's implicit. And a DCF model, it is explicit, right? So people go, oh, well, you just change your assumption a little bit of value. Absolutely, but that's explicit. So the question is, would you rather have something implicit and buried, and then we don't really know what's actually what we're doing, or explicit, and overt, and then we could be bait, right? And then that, that, to me, of course, the latter is a vastly more attractive proposition than the former. So economic policy might be a little bit strong, but, but that's, that's the basic idea. And then a related idea I'll just mentioned quickly is there's a presumption often that growth in and of itself is a good thing. And what we, and we demonstrate this in a simple appendix in chapter one, I think it is actually, that growth in and of itself is not value, it needn't be value creating. So the key concept is growth adds value when a company's earning above the cost of capital, right? So qualifying growth, the fact the way you should think about it is return on capital, cost of capital spread is first and foremost, and then growth amplifies, right? Makes a good thing better. And if you're spread as negative, it makes a bad thing even. This one is great. And we won't go through the, how this relates to a discounted cash flow analysis, but it does. And you probably shouldn't be using multiples. If you're someone who is looking at individual businesses and valuing those businesses, you probably shouldn't be using multiples and buying something because it trades at 12 times earnings, unless you know what the fact that it trades at 12 times earnings actually means. Now, I think it's a little bit different. In fact, we're investing and we'll get that in a second, but I think this is so important is anything you do, like you have to earn the right to do it by understanding what underlies what you're doing and what it actually needs. So I'm just real quick calculating the multiple and excess returns, but it keeps coming up with a negative number, my dearest friend. This is one of those things. There's that great Michael Mowes and paper on this, the, the, the, the, the right to use the multiple paper, highly highly recommend if you haven't read this, but this just says it's okay to use the multiples, as he said, you just have to earn the right to do it. If you learned to build the financial model, you have to stub your toe on some stuff. I am as guilty as this is anybody. You get wrapped up in the excitement of learning about quantitative models and how to take stuff and how to apply it. But then you have to understand, wait, why is greenblatt in the magic formula excluding banks and financials? Why are these things happening? Oh, what else do I have to do? Are there different multiples for different industries or sub-sectors or whatever else that help me more articulate, better articulate the, you know, the way to think about these things relative to each other and on an absolute basis? That little subtle adjustment of saying, do you actually understand the inputs and how they influence this numerator and denominator? Goes a long, long, long, long, long, long, long long way. Yeah, I mean, just my last point, and I think this is a little less applicable to someone like me who's a factor investor. Because as a factor investor, I'm not looking at individual company and saying, because it's 15 times earnings, I think it's cheap. I'm looking at these system-wide things and saying, because this basket of companies is riskier than the market, I expect to get paid for that, or because people across a wide number of companies are systematically misprasing them and overestimating bad news, I get paid for that. So it's less important than like a factor investor. They're not going to go in for every company and be like, I need to understand and earn the right to use the multiples, but it is important, and this gets back to the idea, you want to earn the right to get paid in investing. So you want to understand why you're getting paid. And in Michael's world, that means you have to earn the right to use the multiple. And in the factor world, it means you have to understand why I'm getting this return by investing in these companies with these common characteristics. So I think it does carry across. So as a fundamental or quantitative value factor investor, that's how we just fire appearances on access returns. That's what you're saying. That is pretty much what it is. Okay. We also really paid the right to use the multiple for this length. So there's next one is also one that until I heard him originally say this, I didn't get this. So I had been one of these people who had said, all right, you know, one of the great things about the rise of passive investing is there's less active investors out there. So those of us that are active investors, we got a much better chance. Michael will explain why I'm completely wrong about that. By the way, Peter Lynch, I think very recently made a comment about why active is good in this context. It's very intuitive to think to yourself, well, gee, if everybody's just going for average, it's easier to become above average. But it actually is, I think the actual, the actual answer is completely the opposite, which is interesting, right? So the first thing just to state, well, I built Bill Sharp wrote a very famous paper on the arithmetic of active management.
所以他談到要贏得使用倍數的權利。我反覆強調的一點是，倍數並非估值。讓我在此打住。倍數不是估值。它們是估值過程的簡化表達，一旦你從不混淆這兩者。對。所以估值過程是未來現金流的現值。倍數是一種簡化表達。那麼倍數有什麼好處？簡化表達一般而言有什麼好處？對。它們節省你的時間。對。順便說一下，我應該說清楚。我使用倍數。如果你在一個小時的隨意交談中，我可能會對某個特定業務丟出倍數。隨便。那沒關係。但關鍵在於，你要理解你所使用的倍數背後的經濟含義。所以你在說，我認為這應該是 15 倍的 EBITDA 或 30 倍的收益。那是什麼意思？我必須相信什麼，這些倍數才說得通？因此，如你所知，我們花了很多時間寫作，我們寫了一篇名為《PE 倍數意味著什麼？》的文章。我們寫了一篇名為《EVD？》的文章。 但降低倍數均值，本質上是在這些人們慣用的倍數之間建立一座橋樑。以及為了使這些倍數合理化，你必須做出的潛在經濟假設。並且要真正明確地闡述這些事項。你知道，正如紐約大學的院長評估中所提到的，他對此談論甚多。他調查了投資者報告——抱歉，是分析師報告——他發現十份中有九份主要依賴倍數分析。這就是人們通常相互溝通的方式。所以，正如我告訴我的學生，在我們評估模塊的尾聲，你必須可以說是「贏得使用倍數的權利」。你可以使用它們，但要贏得權利。而你贏得權利的方式，就是證明你理解其中所蘊含的潛在經濟假設，對吧？最後我要說的是，這又回到了預期投資這個大概念，也就是關於未來價值創造、投資需求等所有假設——這些都隱含在一個倍數中。它是隱含的。並非不存在，而是隱含的。而在現金流折現模型中，這些是明確的，對吧？ 所以人們會說，哦，你只是稍微改變了對價值的假設。沒錯，但這是明確的。問題在於，你寧願要一個隱含且埋藏的東西，然後我們其實不知道自己在做什麼，還是要一個明確且公開的東西，這樣我們才能進行辯論，對吧？對我來說，後者當然比前者更具吸引力得多。所以經濟政策這個詞可能有點強烈，但基本概念就是如此。接著我想快速提到一個相關概念：人們常常預設成長本身是件好事。而我們在第一章的簡單附錄中證明了，成長本身並非價值，它不一定是價值創造的。關鍵概念在於：當公司的獲利高於資本成本時，成長才會增加價值，對吧？所以應該這樣思考：資本回報率與資本成本之間的利差是第一要務，然後成長會放大這個利差，讓好變得更好。如果你的利差是負的，成長只會讓壞變得更糟。這一點說得非常棒。 我們不會深入探討這與折現現金流分析的關聯，但確實存在關聯。而且你可能不應該使用倍數估值法。如果你是那種研究個別企業並為其估值的人，除非你真正理解「以 12 倍本益比交易」這件事實際意味著什麼，否則你很可能不該僅因某檔股票以 12 倍本益比交易就使用倍數法買進。現在我認為情況略有不同——事實上我們正在投資（稍後會談到），但我認為至關重要的是：無論你做什麼，都必須透過理解行為背後的基礎與實際需求，來「贏得使用它的資格」。所以我只是快速計算倍數與超額報酬，但親愛的朋友，它不斷得出負數呢。這正是那種經典情況。Michael Mauboussin 有篇關於此主題的精彩論文《使用倍數的資格》，若你還沒讀過，我強烈推薦。文中闡明：使用倍數並無不可，但正如他所言，你必須先「贏得使用它的資格」。就像學習建立財務模型時，總得經歷些磕磕碰碰的過程。 我和任何人一樣有這個毛病。你會沉浸在學習量化模型的興奮中，想著如何運用這些工具並實際應用。但接著你必須理解，等等，為什麼格林布拉特的魔法公式要排除銀行和金融股？為什麼會這樣？哦，我還需要做什麼？不同產業或子行業是否有不同的估值倍數，能幫助我更清晰、更準確地理解這些事物之間的相對關係和絕對基準？只要稍微調整思考方式，問自己：你真的理解這些輸入值如何影響分子和分母嗎？這會帶來極其深遠的影響。是的，我的最後一點是，這對像我這樣的因子投資者可能較不適用。因為作為因子投資者，我不會單看某家公司就說「它本益比 15 倍，所以很便宜」。 我觀察這些系統性的事物並認為，由於這組公司的風險高於市場，我預期能因此獲得回報；或者，由於許多公司的投資者普遍錯誤定價並高估壞消息，我也能從中獲利。因此，這對因子投資者來說相對沒那麼重要。他們不會針對每家公司深入研究，認為自己必須理解並「贏得使用倍數的權利」，但這確實很重要，這回歸到一個觀念：在投資中，你希望贏得獲得回報的權利。所以你必須理解自己為何能獲得回報。在麥可的世界裡，這意味著你必須贏得使用倍數的權利；而在因子投資的世界裡，則代表你必須理解為何投資這些具有共同特徵的公司能帶來回報。我認為這兩者是相通的。因此，作為基本面或量化價值因子投資者，我們就是這樣透過表象來獲取超額回報的。這就是你所說的，大致上就是如此。好的，我們也確實為這段期間使用倍數的權利付出了代價。 所以接下來這個也是，直到我最初聽他這麼說，我才真正理解。我曾經是那種會說「好吧，你知道嗎，被動投資興起的一大好處就是主動投資者變少了，所以我們這些主動投資者機會就大多了」的人之一。麥可將會解釋為什麼我這個想法完全錯了。順帶一提，彼得·林奇最近好像也針對這個情境下為什麼主動投資有好處發表了評論。直覺上很容易會想：哎呀，如果大家都只追求平均，那要變得比平均更好就容易多了。但實際上，我認為真正的答案恰恰相反，這很有趣，對吧？首先要說明的是，比爾·夏普寫過一篇非常著名的論文，談論主動管理的算術。

And he basically said, if you think about it, this has to be roughly right. And it's not perfectly right, but it's roughly right, right? So there's the return. Let's just say the markets, yes, and P500 is obviously much bigger. Let's just say the S and P500 to constrain our discussion. You're going to have some percent of his indexed, right? And they're going to earn the rate of return to the market minus their small fees. And then the other part is active, right? And and the active managers in the aggregate are also going to earn the market rate of turn, right? Because the two pieces have to equal to total, right? So by definition, they're going to market better turn. Now the key is there are fees are higher than the indexers, right? So by definition, indexers are going to outperform the accurate active managers in the aggregate. That's been true for a long time, nothing new about that. So now let's zoom in a little bit on the active managers, right? What we have is a distribution of returns, right? So for you to have positive alpha, and even before fees, forget about fees, if you have positive alpha, someone has to have negative alpha, right? And the exact same dollar amount, right? But mathematically, that has to be true. Alpha is a y-intercept on a regression, right? So it's a y-intercept, it has to be zero net. So the question is, who's on the other side of my trade, right? And so what I argue and I'm not sure this is completely true. All the ethic directionally people would buy in large agree with it, is the money has flowed out of the weaker hands in terms of the active community. And that could be weaker mutual funds, weaker individuals, they've mostly gone to indexing. And as a consequence, those who remain are actually smarter, right? And so just in the metaphor I would use is something like a poker table, right? So let's say you have a poker game Friday night, you invite your buddies over. And of course, your buddies are going to have some distribution of skill, right? Some will be better players and others, and the smarter players will take money from the weaker players over time, right? For sure, not any particular type of over time. But if all of a sudden, the weaker players decide they don't want to play anymore, or they just come and drink your beer, which they might do, and not play, right? Which is in the sense, by the way, the active community, by the way, just to be super clear, the past of community, the indexing community, is free riding off the active community, right? So there's free riding here, just to be super clear about this. And that's why drinking your beer is a free ride. But if those weak players leave, the people around your poker table Friday night are only the best players. And as you imagine, if it's only the good players, it becomes more difficult to win. So I think that poker metaphor is actually a pretty good way to think about this, is that if you accept the premise, and I think we can mostly show this. But if you accept the premise that those who have fled the active market are the weaker players, those who are left over are the stronger ones and it becomes harder. Yeah, so this is great. I mean, this idea of obviously, as, you know, I just hadn't thought when I thought this through the first time that as active managers are getting hit here by the fact that they're underperforming and people are, you know, passive investing is rising, like what's left is the good people? And, you know, that makes it harder. So it's completely counterintuitive to think that less active managers lead to a more difficult asset management business, but that is what it is, I think. So we used to play in these, these like Sunday pick-up soccer games, and I was a kid in town. And, and at first, it was just like some some dads from the team, whatever, and the sons, and a few other, like just random people who'd like be around. But then what ends up, so this is, this is my comparison, like, you know, there were probably little active markets and little like pick-up games, like all scattered about. But nobody really had that many people. For whatever reason, you know, all of a sudden my friend Adam has some older cousins, and they come, and they join the league, and then they bring some friends from some other places. Now, all of a sudden, the pickup game turns into like this bigger thing. But, it turned into this bigger thing where all of a sudden we had some college kids who were like D1 athletes. All of a sudden, that attracts like a couple of the, basically, like the immigrant families. Like, we had this, uh, this, like, this Italian, like, 60-year-old, and it's like grandkids would come. And you brought all these incredibly talented people into the pool all of a sudden. So it's kind of like, it's just a pickup Sunday league game, but you had these freaking ringers who were showing up and defecting from other things and coming to ours. So what ends up happening is, like, the level of skill just kept going higher and higher and higher until it was this crucible of where you're just like, I'm just going to get my butt kicked, like, all the way out there. With the whole passive active thing, it's like, yeah, the weekends get flushed out as you keep raising that bar. And a lot of, I think, what we're seeing now, and we see it when we hear people like Mike Green or, uh, Ironhorn, or people like that just talk about this, it's, it's actually, it still matters, and it is crazy competitive, because the people who have stuck it out are really, really smart, really, really good. And they're competing over a much more limited amount of, like, when are we going to be right? When's this thing going to matter? How's this going to work? And they all know that that's where they're competing against. That, that really changes the dynamic, uh, not unlike my Sunday league pickup soccer against my skin. So this next tip gets into something that's related to this in many ways. So Michael here is talking about the idea of the paradox of skill. It's one of these things. It's not at first blush intuitive, but it's, I think it's right. So when you think about skill, you can think about it on two levels. The first is absolute skill. And I think we'd all agree, and all the listeners would agree, that if you look around the world, not just investing, but business and sports or whatever it is, that the absolute level skill has never been higher. Right? And part of that is because we have better training, better techniques, better investing. We have, you know, computing power and access to information and so on and so on. So I think that's, there's not a lot of doubt about that. The second issue, though, is the one that's more important for our discussion, which is relative skill, right, which is the difference between the very best in the average participants in each field. Now I learned about this idea. It was not my idea at all. I learned about it from Stephen J. Gould, who wrote a book called Full House back in the mid 1990s, and he was ruminating on why no one has hit over 400 immediately baseball since Ted Williams did that in 1941. And, you know, the argument he said was because essentially the standard deviation of batting average has come down a lot, which is there's more uniform skills. Even though the batting average hasn't changed all that much. In fact, the powers would be at majorly baseball. We'll change the rules to keep it sort of in a reasonable band. The standard deviations come down a lot. So Ted Williams was a three standard deviation event. When if you're a three standard deviation event now, you'd hit like 380 or 385, which is awesome. You'd win the batting title, but you're not anywhere near breaching that 400 level. So I think the same thing's true in investing, and we can measure that, right? We measured by looking at essentially the equivalent of batting average, which is a standard deviation of alpha. And historically, that's come down a lot. Now, I'll just say one thing Justin, which is a little bit weird, is it it's actually flattened and bumped up a little bit in the last couple of years. So for reasons, I'm not sure I can fully fully put my arms around. So a part of that might just be it's correlated with the actual dispersion of markets to some degree. So there's some other external factors that come into play. But yeah, so that's the way to measure it. So when you look around and by the way, sports leagues, this is a really good sort of framework to think about this. Sportly, sports leagues are actually grinding toward parity. And some have like salary caps in order to try to encourage parity, but they're all grinding toward parity because these guys are all so good. So even the bad teams are really good, right? And certain sports like baseball or hockey are very, very, the level of skills is very high in very uniform. And as a consequence, the luck has it appears to make a bigger, have a bigger role in the outcome. So that, this is the thing. And I push back, you know, in Danny Connames book thinking fast and slow, he's got a little section where he talks about investment managers and G either so clueless about there's no skill in this industry. And I went to him and I said, you know, it's actually the opposite. You know, the way to think about it is the skill is not not only high, but it's uniform, right? And you think about how many smart people go into this industry and how motivated they are and how hardworking they are and how thoughtful they are, it just defies logic that there's no skill. There's huge amounts of skill. It's just that's the problem, right? And that skill gets reflected in prices and if prices to be reduced to their largely efficient, that means that means the random wall kind of thing comes into play. So this is again counterintuitive. You would think as absolute skill rises and I think in many places, I mean, he's talked about sports. We've taught in investing, you know, you've got computers, you know, way more computer power. You've got smarter people. I mean, it's hard not to argue that absolute skill is higher in the investing world. You would think what I need to do is my relative skill becomes more important now because everyone is so good. I got to be even better. But what happens is locked becomes much more important because you have less variation. You have less standard deviation. So as an active manager, what I want is I might have this this median average outcome that everybody gets. I want a lot of spread around that because that spread gives me an opportunity to be in the top portion of that spread and to be one of the outperforming people. But as you narrow that spread, it becomes harder and harder and harder and harder to be the active manager. It becomes harder and harder and harder to express your skill. And it also, in this regard, we got into this with Adam Butler, it also becomes harder to distinguish your skill from luck. Like our episode with Adam Butler was it was something like your alpha, you know, your alpha's hard to distinguish from noise or something was our title. And the idea is it's very hard to even tell if you're skilled in a world like that. It's hard to distinguish the skill from luck in real time. This concept of just thinking and layers of thinking about you as you compare to yourself, then you as you compare to the two up here group and then the peer group as it compares across the peer group. Those layers are just enormously useful. And as that bar gets raised for what's considered skill or what we perceive as luck, you start to understand kind of the only thing you can control is going back to yourself and figuring out what makes you either potentially have that edge over the peer group or just be able to stand out. And you can't have all these things. It's really hard at the highest levels to do this stuff. This concept of absolute versus relative skill when there's luck was at the success equation that book that he wrote to another one that just lays this out across different domains. So intensely valuable and so many more walks of life than just investing markets. The second last one is a concept that I struggle with with a lot of investor struggle with, which is you'll see these expected returns for markets. A lot of the big places do it. The GMOs, the research affiliates, Vanguard does it. A lot of people will say, based on where we are in the markets, here's kind of your seven to ten year expected return.
他基本上說，如果你仔細想想，這大致上應該是對的。雖然不完全準確，但大致上是正確的，對吧？所以這就是回報率。我們就說市場吧，是的，標普 500 指數顯然規模更大。為了限縮討論範圍，我們就以標普 500 為例。其中一部分資金會投資於指數型基金，對吧？它們將獲得市場回報率減去少量費用。另一部分則是主動型管理，對吧？而整體而言，主動型基金經理也將獲得市場回報率，對吧？因為這兩部分的總和必須等於整體市場，對吧？所以根據定義，它們將獲得市場回報率。關鍵在於，主動型管理的費用比指數型基金更高，對吧？因此根據定義，整體而言，指數型基金的表現將優於準確的主動型經理人。這情況已經持續很長一段時間，並不是什麼新鮮事。現在讓我們稍微聚焦在主動型經理人身上，對吧？我們看到的是一個回報率的分佈，對吧？所以，如果你想獲得正的阿爾法值，甚至在扣除費用之前，先不談費用，如果你有正的阿爾法值，就必須有人有負的阿爾法值，對吧？ 而且金額完全相同，對吧？但從數學上來看，這必然成立。阿爾法值是迴歸分析中的 Y 軸截距，沒錯吧？既然是 Y 軸截距，淨值就必須為零。所以問題在於：誰在我的交易對面？我的論點是——雖然不確定這完全正確，但從倫理方向來看，多數人應該會大致同意——資金已從主動投資群體中較弱勢的方流出。這些可能是較弱的共同基金或較弱的散戶投資者，他們大多轉向了指數投資。結果是，留下來的人實際上更精明，對吧？用個比喻來說，就像撲克牌桌一樣。假設週五晚上你邀朋友來玩撲克，當然朋友們的牌技會有高低之分。隨著時間推移，較聰明的玩家會從較弱的玩家那裡贏錢，這點毋庸置疑——雖然不一定每次都是如此。但如果突然間，較弱的玩家決定不再玩了，或者他們只是來喝你的啤酒（他們可能真的這麼做）而不玩牌，那會怎樣？ 順帶一提，從某種意義上來說，主動投資社群——這裡要特別說明清楚——指數投資社群其實是在搭主動投資社群的便車，對吧？所以這裡確實存在搭便車現象，這點必須特別釐清。這也正是為什麼「喝你的啤酒」是種搭便車行為。但如果那些實力較弱的玩家退出，週五晚上圍坐在你撲克牌桌旁的就只剩下最頂尖的玩家。可想而知，若桌上只剩高手，要贏牌就變得更加困難。所以我認為這個撲克牌比喻其實能很好地說明這個現象——如果你接受這個前提（而我相信我們多半能證實這點）：那些逃離主動投資市場的是較弱的玩家，留下來的則是更強悍的對手，於是局面就變得更艱難。沒錯，這觀點確實精闢。我的意思是，顯然地，當我最初深入思考時並未意識到——隨著主動型基金經理人因表現不佳而受挫，被動投資日益盛行，留下來的豈不都是菁英？這確實讓競爭變得更具挑戰性。 所以，認為活躍經理人越少會導致資產管理業務越難做，這完全違反直覺，但我想事實就是如此。我們以前常參加這些週日的臨時足球賽，我那時還是鎮上的小孩。一開始，只有一些球隊的爸爸們、他們的兒子，還有其他一些剛好在附近的人。但後來情況變了——這就是我的比喻：就像過去可能有一些小型的活躍市場和零散的臨時比賽，但沒什麼人參加。不知為何，突然間，我的朋友亞當有一些年紀較大的表兄弟，他們加入聯盟，還帶了其他一些朋友來。突然間，臨時比賽變成了一個更大的活動。但這個更大的活動突然間吸引了一些大學運動員，甚至是 D1 級別的選手。這又吸引了幾個移民家庭——比如，我們有一位 60 歲的意大利老先生，他的孫子們也會來參加。 你突然間把這些才華洋溢的人都拉進了這個圈子。這就像是一場臨時湊成的週日聯賽，但卻出現了一群從其他領域跳槽過來的超級高手，加入了我們的戰局。結果就是，整體技能水平不斷攀升、再攀升，直到形成一個高壓競技場，讓你覺得自己簡直要被徹底打趴。被動與主動投資的議題也是如此，隨著門檻不斷提高，那些實力不足的參與者就會被淘汰出局。我認為，我們現在看到的許多現象——當聽到像麥克·格林或鐵角（Ironhorn）這樣的人談論這個話題時——實際上，這仍然至關重要，而且競爭異常激烈，因為那些堅持下來的人真的非常、非常聰明，也非常、非常優秀。他們在一個更有限的範圍內競爭：我們何時會判斷正確？這件事何時會產生影響？這將如何運作？而他們都清楚，這就是他們彼此競爭的戰場。 這確實改變了動態，呃，就像我週日聯盟的臨時足球賽對上我的皮膚一樣。那麼接下來的這個建議，在很多方面都與此相關。Michael 在這裡談到了技能悖論的概念。這是其中一個例子。乍看之下並不直觀，但我認為它是正確的。當你思考技能時，可以從兩個層面來考慮。第一個是絕對技能。我想我們都會同意，所有聽眾也會同意，如果你看看世界各地，不僅僅是投資，還有商業、體育或其他任何領域，絕對技能水平從未如此之高。對吧？部分原因是因為我們有更好的訓練、更好的技術、更好的投資。我們擁有計算能力、資訊獲取途徑等等。所以我認為這點沒有太多疑問。然而，第二個問題對我們的討論更為重要，那就是相對技能，也就是每個領域中最優秀者與平均參與者之間的差距。我了解到這個概念，這完全不是我的想法，我是從 Stephen J.那裡學到的。 古爾德在 1990 年代中期寫了一本名為《滿堂彩》的書，他當時正在思考為何自泰德·威廉斯在 1941 年達成後，就再也沒有人能立即在棒球中打出超過四成的打擊率。他提出的論點基本上是，打擊率的標準差已經大幅下降，這代表球員的技能更加均勻。儘管打擊率本身並沒有太大變化。事實上，大聯盟棒球的權力機構會改變規則，以將其保持在合理的範圍內。標準差已經大幅下降。所以泰德·威廉斯當時是一個三標準差的事件。如果現在你是一個三標準差的事件，你大概能打出.380 或.385 的打擊率，這已經非常出色了。你會贏得打擊王頭銜，但你離突破四成打擊率還差得遠。所以我認為投資也是如此，而我們可以測量這一點，對吧？我們透過觀察相當於打擊率的東西來測量，也就是阿爾法的標準差。歷史上，這個數值已經大幅下降。現在，賈斯汀，我要說一件有點奇怪的事，那就是在過去幾年裡，它實際上已經趨於平穩，甚至還略微上升了一點。 所以基於某些原因，我不確定自己能否完全理解透徹。部分原因可能在於，這與市場實際的離散程度存在某種程度的相關性。因此，還有其他外部因素在起作用。但沒錯，這就是衡量它的方式。順帶一提，當你觀察體育聯盟時，這確實是個很好的思考框架。實際上，體育聯盟正逐漸趨向實力均衡。有些聯盟設有薪資上限以促進公平競爭，但所有聯盟都在朝均衡發展，因為這些運動員都太優秀了。即使表現較差的隊伍其實也很強，對吧？像棒球或冰球這類運動，技術水準極高且非常一致。因此，運氣因素對比賽結果的影響似乎更大。這就是關鍵所在。我對此有所保留——在丹尼爾·康納曼的《快思慢想》中，他有一小節談到投資經理人，並暗示這個行業根本不存在技術含量。我曾當面告訴他：實際上情況恰恰相反。 你知道嗎？思考這個問題的方式在於，技能不僅要高，還要一致，對吧？想想有多少聰明人投身這個行業，他們有多麼積極、多麼勤奮、多麼深思熟慮，要說這裡沒有技能，簡直不合邏輯。技能其實非常豐富，問題就在這裡，對吧？而這些技能會反映在價格上，如果價格大致趨於有效，那就意味著隨機性開始發揮作用。所以這又是一個反直覺的現象。你可能會認為，隨著絕對技能的提高——他在很多地方都談到過，比如體育領域，我們在投資教學中也提到——現在有電腦，運算能力強得多，還有更聰明的人。很難否認投資界的絕對技能比以前更高了。你可能會想，我現在需要做的是提升自己的相對技能，因為每個人都這麼優秀，我必須更出色才行。但實際上，運氣變得更加重要，因為變異性減少了，標準差也更小了。 因此，作為一位主動型經理人，我所期望的是能夠獲得與眾人相同的平均中位數結果。我渴望在該結果周圍有廣泛的分散度，因為這種分散為我提供了機會，使我能夠位於該分散度的頂端，成為表現優異的一員。然而，隨著分散度的縮小，要成為一位成功的主動型經理人變得越來越困難。要展現你的技能也變得愈發艱難。此外，在這方面，我們與亞當·巴特勒討論過，要區分技能與運氣也變得更加困難。就像我們與亞當·巴特勒的那集節目，標題大概是「你的阿爾法難以與噪音區分」之類的。重點在於，在這樣的世界裡，甚至很難判斷你是否具備技能。在即時情況下，很難區分技能與運氣。這種層層思考的概念——從與自己比較，到與這裡的兩人小組比較，再到與同儕群體比較——這些層次極其有用。 而當衡量技能或我們所認為的運氣的標準不斷提高時，你會開始理解，唯一能掌控的就是回歸自我，找出那些可能讓你在同儕中擁有優勢或脫穎而出的特質。你不可能同時擁有所有這些特質。在最高層次上做到這一點真的非常困難。當運氣存在時，絕對技能與相對技能的概念，正是他書中成功方程式所探討的，另一本書則在不同領域中詳細闡述了這一點。這不僅在投資市場中極具價值，在生活的許多其他方面也是如此。倒數第二個概念是許多投資者（包括我自己）都難以掌握的，那就是你會看到市場的預期回報。許多大型機構都在做這方面的預測，例如 GMO、Research Affiliates，還有 Vanguard。 許多人會根據市場目前所處的位置表示，這大概是你未來七到十年的預期回報。

And here's Michael talking about what he thinks those are good and what they're not good for. Well, the first thing I'll just say is unless there are ranges, you should be very careful about paying attention to it. So, you know, you can audit some of these, you mentioned all these guys doing these forecasts. You can audit what they've said at the past and see how they've done. And it's not very pretty. The answer is, it's a range of outcomes and people should think about that specifically. Now, I will say that when I think about for looking returns and I've see, you know, we do things like cost to capital calculations and I do these for my course. What I actually use is as worth the motor and, you know, as every month as wealth publishes a market risk premium to which you can add the risk free rate to tenure treasury no yield and you come up with an expected return for the market. So, the other day, we just did this fairly recently. I was keen to understand, like, how good is this, right? And so, what we did is, and as wealth has these data back to 1961. So, we actually, on the x-axis, put the, you know, the number for that particular year. And then we looked at the 10-year subsequent total shareholder returns. On the correlation was about 0.7. So, it's not perfect, but it's pretty good. And again, that's a single point estimate. I'm sure as wealth himself would say for sure, you should have a range around those things like you said 68% up or down based on one standard deviation. And so, that's pretty good. So, I think that gets you into the ballpark. And the other thing I'll say, Justin, I think is important is when people get worked up about this is we have a lot of other touchstones and markets that we can appeal to that allow us to give us some guidance. And by far the most important for equity investors is obviously credit, bond markets, right? So, we know, for example, you can go to the triple beat, go to Fred, you go to website every day, and you'll get the triple B spread for instance. So, you know exactly what bonds are returning. So, that's going to be, and there's usually a relationship between, you know, these are, they're just stacked on the capital structure. There's going to be some relationship between these things over time. So, then you can even look at things like implied volatility. You can look at things like credit default swaps, where there's liquidity. So, there's a bunch of different ways that you can try to kind of create a little bit of a mosaic to understand where you are. The punchline, by the way, today, because I mentioned to a few moments ago, is that expect the returns are fairly muted. I think as well as numbers for December 1st were something like an expected return for the market in the low sixes. That's nominal, by the way. So, if you apply, you know, who knows what inflation's going to be, but if you do that 10 year break even, you know, you sort of get in a three and a half, four and a half percent real return for markets, equity markets, in the U.S. And that's, you know, that historically it's been closer to six to seven percent. So, you know, it's, I don't know what that is, two thirds, 70 percent of the historical returns is probably a reasonable expectation today. And so, it's more muted. Now again, that's again, you see that everywhere. If you buy bonds you're in the same boat and so on and so forth. So, it's a great question. It's a tease just to think about these things probably, realistically, the key is to think about things scenario. I think the, one of the things he said that's really important is, and this, he was talking about forecasts and he was talking about expected returns. And they're a little bit different. But the idea is like, I think you want to see a range for somebody who's doing this well.
而這是麥可談論他認為這些預測好在哪裡，以及它們不適合用於哪些方面。首先我要說的是，除非有範圍區間，否則你應該非常謹慎地關注它。你知道，你可以審核其中一些預測，你提到所有這些人在做這些預測。你可以審核他們過去說過的話，看看他們的表現如何。結果並不怎麼好看。答案是，這是一個結果範圍，人們應該具體思考這一點。 現在我要說的是，當我考慮預期回報時——你知道，我們會做像資本成本計算這樣的事情，我在我的課程中也會做這些。我實際使用的是 Aswath Damodaran 的數據，你知道，每個月 Aswath 都會發布一個市場風險溢價，你可以加上無風險利率——十年期國債的收益率——然後得出市場的預期回報。所以，前幾天我們剛剛做了這個計算，就在最近。我很想了解，這個方法到底有多好，對吧？於是我們做了這樣的事：Aswath 的數據可以追溯到 1961 年。所以我們實際上在 x 軸上放了，你知道，特定年份的數字。 接著我們檢視了後續 10 年的股東總回報。相關性約為 0.7。所以，這並非完美，但相當不錯。同樣地，這只是一個單點估計值。我確信韋爾斯本人也會明確指出，正如你所說，應該要有一個範圍，例如基於一個標準差上下 68%的區間。因此，這相當不錯。我認為這能讓你掌握大致方向。賈斯汀，我想強調的另一個重點是，當人們對此感到焦慮時，我們其實有許多其他市場參考指標可供借鑑，這些能為我們提供指引。而對股權投資者而言，迄今最重要的顯然是信用債券市場，對吧？舉例來說，你可以查詢 AAA 級債券利差——每天上 FRED 網站就能看到 BBB 級利差等數據。這樣你就能確切掌握債券的報酬狀況。這將成為重要參考，畢竟這些工具在資本結構中是層層堆疊的，長期來看它們之間必然存在某種關聯性。此外，你甚至可以觀察像隱含波動率這類指標。 你可以觀察像是信用違約交換這類具有流動性的指標。總之，有許多不同方法可以嘗試拼湊出整體樣貌，以理解當前所處位置。順帶一提，今天的重點（正如我稍早提到的）在於預期回報相對平淡。我認為截至 12 月 1 日的數據顯示，市場預期回報率大約在 6%低檔。順帶說明，這是名目值。若套用——雖然無人能確知通膨將如何發展——但若採用 10 年平衡通膨率來計算，美國股市的實質回報率約落在 3.5%至 4.5%區間。而歷史數據顯示，這個數字通常更接近 6%至 7%。因此，當前預期回報大約僅有歷史水準的三分之二或 70%，這可能是現階段較合理的預估。整體而言，回報確實更趨平緩。再次強調，這種現象隨處可見：即便投資債券也面臨類似狀況，諸如此類。這確實是個值得深思的好問題。 光是想想這些事就讓人覺得心癢難耐，但現實來說，關鍵在於要從情境的角度來思考。我認為他提到的一點非常重要，當時他談到預測，也談到預期報酬，這兩者其實有些微差異。 但核心概念是這樣的：我認為你會想看到那些擅長此道的人，他們所呈現的預測範圍。

And you will see this from the, from the good people to do expected returns. They won't tell you, you know, expected returns are 4.2 percent or something. They'll give you a range of, of different outcomes. And I think that's really important. And that's a good way to do it. But the, the other thing to think about is, it's just important as an investor. And I don't know that these expected returns. I mean, you can talk a little bit more about how you use them in modeling and stuff. But it's, it's hard for investors to use these expected returns because there is a wide range around them, like he said. And a lot of times like what people thought were expected returns for the markets. Like if you go back, you know, three, five years from now and you look at what people thought the expected returns were going to be, what they actually ended up being, they're usually very, very different. And so, although you can say over, you know, seven, ten year timeframe, we can more accurately predict expected returns than we can over one, two, three years. There's still a lot of variation in there. So it's hard for individual investors. They can be very useful in the planning process. But it's very hard for individual investors to figure out what to do with that. Another, in the middle concept that I learned from people like Mobison and a professor domitaron and some of the others. This is again, starting in the middle. It's when you're thinking about probabilities and you're thinking about probabilistic, probabilistic, collegies. I can't talk today. Going forward, I find it very useful, whether it's in an investment case or in a financial plan or in anything else. Any time you're modeling the future, you're basically looking at not just how did we get up to this point in time before we project forward to understand what's here in the present. But also to understand going forward, I need not just my, my bull case, not just my here's my long-term average expected returns. I want to actually have a base case, long-term averages. I want to have a bull case, a long-term above average and I want to have some form of a bearish case, a below average expectation. And in simple equity terms as he described, I think in the clip, the like you can have 10% expected with a plus and minus 15% variance in either direction for those cases. You can go really simple as something like that or you can tease out what happens in these and then you can probabilistically wait them. You can say, okay, we think, you know, over this, especially with a short-term period, you might want to evenly disperse them. If it's over a long-term period, you might want to just go, here's how we plan for three potential eventualities. Here's how we plan for if everything goes wrong, we end up in long-term care, we have all these growing expenses and all these things. Great.
你會從那些擅長計算預期報酬的人身上看到這一點。他們不會直接告訴你預期報酬是 4.2%之類的數字，而是會給出一個包含不同結果的範圍。我認為這點非常重要，也是一種很好的處理方式。但另一方面值得思考的是，對投資者而言，更重要的是——我其實不太確定這些預期報酬的實用性。當然你可以多談談如何在建模中運用它們，但正如他所說，由於預期報酬的波動範圍很大，投資者很難實際運用這些數據。很多時候，人們對市場預期報酬的判斷——如果你回顧三、五年前的預測，對照最終實際結果，通常會發現兩者差距極大。雖然我們可以說，在七到十年的時間框架下，預期報酬的預測準確度會比一兩年來得高，但其中仍然存在許多變數。因此對散戶投資者而言，這確實不易掌握。不過在規劃過程中，這些預期報酬數據仍可能相當有用。 但對於個別投資者來說，要弄清楚該如何處理這些資訊是非常困難的。另一個我從莫比森、多明塔隆教授等人那裡學到的中間概念——這同樣是從中間開始思考。當你在考慮機率、思考概率性的推論時（抱歉，我今天說話不太順暢）。我發現無論是在投資案例、財務規劃還是其他任何情境中，這種思考方式都非常有用。每當你對未來進行建模時，你不僅要回顧我們如何走到當前這個時間點（在向前預測之前，先理解當下的狀況），還要理解：展望未來時，我不僅需要我的樂觀情境（不只是我的長期平均預期回報），我實際上需要一個基準情境（長期平均值）、一個樂觀情境（長期高於平均），以及某種形式的悲觀情境（低於平均的預期）。就像他在影片片段中描述的，以簡單的股權術語來說，你可以設定 10%的預期回報，並為這些情境設定正負 15%的變動範圍。 你可以選擇非常簡單的方式，像是那樣處理，或者你可以仔細分析這些情況的發展，然後以機率的方式來等待它們。你可以說，好吧，我們認為，尤其是在短期內，你可能會想要平均分散這些情況。如果是長期的話，你可能會直接規劃三種可能結果的應對方案。這就是我們如何規劃，如果一切出錯，我們最終需要長期照護，面臨所有這些增加的開支等等。很好。

That is a bear case worth discussing. But it's not worth discussing without also having the contest context of a base case. This is what it probably looks like going forward and a bull case. Oh, hey, if that equity exposure actually gives us above average returns for this period of our life, like, how do we use the analogy? How do we pick the apple off the tree and when it's ripe? How do we know when it's ripe when something went like, oh, guess what? We did always want that vacation home. We always did want to build that second place, you know, back in, back where we grew up. The market or the business or life for whatever was kind to us, now it's here and now it's okay to go out and do that. This exercise of having a bull case, a base case, and a bear case, to plan probabilistically about the future is enormously useful in narrow walks like with one investment and in broader walks like financial planning and across all the life.
這是一個值得討論的熊市情境。但若沒有基準情境的對照背景，討論便失去意義。以下是未來可能呈現的樣貌，以及一個牛市情境。噢，對了，如果這段人生期間的股票投資確實帶來高於平均的回報，我們該如何運用這個比喻？如何在蘋果成熟時從樹上摘取？當事情發展如「猜怎麼著？我們其實一直想要那棟度假屋，一直想在成長的地方建造第二個家」時，我們如何判斷時機成熟？無論市場、事業或生活對我們展現了善意，現在機會來了，現在正是付諸行動的時刻。這種同時構思牛市、基準與熊市情境，以機率思維規劃未來的方式，無論在單一投資的狹義層面，或財務規劃乃至人生各面向的廣義層面，都具有極大的實用價值。

Yeah, one place I think you maybe can add a little value of these expected returns isn't tilting your portfolio, but it's something you have to be really, really careful about. Because so for instance, I could say right now the expected returns of international stocks, particularly international value, are way, way, way higher than the expected returns of like the US market. So I could say, I want to make a slight adjustment to my portfolio overweight international. Now the way I would argue against myself with that is I could have done that a decade ago, I could have done it before that with international stocks and it just didn't work out. And so you almost have to have, if you're going to do stuff like that, you almost have to have these really, really long timeframes to put it to play out. Because although expected returns are usually listed over seven, ten years, a lot of times it takes more than seven to ten years for what you expect to actually play out. Yeah, I'm a big, big, big fan, especially in capital market assumptions for people who do allocation work. This is where you should have your long-term expected returns. And you should have some type of more dynamic, shorter bound assumption. There's a number of ways you can do this. They don't have to be ultra-shorts. They don't have to be like, you know, three weeks, but some people do it as short as, you know, days. But they should be like somewhere in that like, you know, one to five year range. And basically go long-term, this is what we expect to get. Short-term, you can be a little bit more tactical or opportunistic, especially across different account types, different types of things. And it's easy to get yourself in trouble, though, if you fall in love with the idea to your point, like international value over the last, whatever, 30 years. You have had a couple of blips in there, though, where that really worked. So the question would be in the blip. And this is where the people who only use the short-term project, or the long-term projections, I think, get in trouble. They have the long-term projections. And they forget to go when I have the blip and get the way above average short-term result, actually want to be trimming in those environments. And that's that is way easier to say, way harder to do. You just need to wrap a process around that. So pretty much everything we talked about could have been the answer to this, but we always ask at the end of our episodes, all our guests, you know, based on your experience in markets, what's the one lesson you teach the average investor? And here's what Michael said. I think that I would encourage people to learn about and apply base rates as they think about the world of investing. By the way, it's not just a valuable for investing, but really business or your life, actually. It's a career life. And again, a base rate, you know, the basic setup is the natural way to think about the world or solve your problems is to gather a bunch of information and combine it with your own inputs and experience and project into the future. And that's what we all do left our own devices. Using base rates says, I'm going to think about what I'm facing now or my problem as an instance of a larger reference class. I'm going to basically ask what happened when other people or organizations were in this position before. And it's a very natural way to think because you have to leave aside, you know, sort of your own information gathering and your own experience. We all tend to place a lot of value on that. And you have to find and appeal to the base rate, which may not be your fingertips and often it's not. So you have to go out and make a little effort to find it. But once you do, I think it reshapes how you think a lot about the world. And I think makes you more grounded in terms of how you think about how things are likely to unfold. So to me, if that would be the one idea is to say, let's think about base rates. You know, you mentioned before jokingly that we're in that sort of season where people do forecasts, you know, that's a great example where base rates would be very helpful. And you sort of made the joke 10% with some standard deviation. But that's actually the right way to think about it. That's actually the right answer. And that was that's informed by base rates. So you got to the right place and the right way to think about it using that actual technique. So to me, that would be the one bit of advice I would give. And if I could go back to my 20 year old self, that's certainly what I would teach. So you and I highlighted this as one of our best answers to this question. We did a separate episode on all 119 answers we've gotten. But this is such an interesting concept. And I'm just surprised and Michael kind of touched on this. I'm surprised how little people use it or understand it. This idea that I always want to look at the facts in front of me. And I want to try to make my own analysis. And I want to try to predict what's going to happen in the future. But most of the time, just asking myself, what happened when similar situations happened in the past, what happened in the future, using what they call the outside view, most of the time, that is a superior way than trying to do all this detailed analysis. And it's widely, as Michael said, it's widely underused, almost no one does it. You don't see this smart when you do it, which is probably one of the reasons it's widely underused. But it does work out better. I think most of the time. This idea. And this is, I think, one of the core tenets. It may be takeaways from anybody who does factor investing or anybody who thinks in a more quantitative way can really, this can be really useful. It's, it's to say, there are, there are factors or different things to describe things that have happened over time. And we can go look as something as an instance, an a prior or a larger reference class. And we can find, hey, this kind of rhymes with that. When's the last time a company, like, forget about what they're promising in their team and their total addressable market? And think about like, okay, when's the last time a company was on this type of a growth curve? And maybe I have to look out at a different industry. Professor Dometeron is really great for doing this regularly. Movason is really great in his work for giving some really wonderful examples of this. But just start to look with places where it's not an exact match, but things rhyme and use that as a way to anchor your analysis, anchor your thinking on, okay, you know, if somebody's going to grow at this fast of a clip, how long of other companies sustain this rate of growth? Or even using conditions like that, using it as a supplement. So let me give a concrete example. So let's say I've got a company that trades at 20 times sales that I want to invest in. And let's say I've looked at the company and I think it's got incredible growth prospects and all that stuff. That's great. I might be right about that. But one of that. So that's my inside view. My analysis of the company that says, I think this company's worth a lot more money. I might want to couple that with the outside view, which says, if I look at all companies historically that traded at 20 times sales are above, what kind of returns that I get on those companies? And what you're going to get is a very sobering thing when you look at that. You're going to find out the reference class has had very, very poor performance. Now, that doesn't mean I'm not right. Maybe with my inside view, but at least means I want to make the supplement my inside view by using that fact that when this has happened in the past, the returns for investors have been really, really poor. And I think you can apply that at the individual security or company level. You can apply it all the way up to the portfolio construction level. Bring it back to like the international value case that you made earlier. I can take something like that, and I can say, I can now contextualize the places this has in my portfolio. If nothing else, as a hedge against, if I have potentially overvalued US stocks, at least as of several months ago, I have potentially undervalued international. Here's a case for me to do a little bit of rebalancing and just understanding how to pair these things together. And you're just never using one factor in isolation. It is just enormously useful. And base rates are a little bit clunky. That's why they're kind of messy. And they will make you sound less smart or less exciting.
是的，我認為有個地方或許能增添些許價值——這些預期報酬並非在調整你的投資組合，而是需要你極度、極度謹慎對待的事。舉例來說，我現在可以說國際股票的預期報酬，尤其是國際價值型股票，遠比美國市場這類的預期報酬高出許多。所以我可能會想：我要稍微調整投資組合，增持國際部位。然而，我會這樣反駁自己：我十年前就能這麼做，甚至更早之前就能佈局國際股票，但結果就是行不通。因此，如果你打算做這類操作，幾乎必須擁有非常、非常長的時間框架來讓它發酵。因為儘管預期報酬通常以七到十年為期列出，但很多時候，你預期的結果真正實現所需的時間遠超過七到十年。沒錯，我對此非常、非常、非常推崇，特別是對於從事資產配置工作者的資本市場假設而言。這正是你應該設定長期預期報酬的領域。 你應該要有某種更動態、較短期的假設。有幾種方法可以做到這一點。它們不必是極短期的，不必像三週那麼短，但有些人會做到短至幾天。不過它們應該落在大概一到五年的範圍內。基本上長期來看，這是我們預期能獲得的。短期內，你可以稍微更具策略性或機會主義，尤其是在不同的帳戶類型、不同的事物之間。然而，如果你過於執著於某個想法，就像你提到的國際價值股在過去 30 年那樣，很容易讓自己陷入困境。雖然其中有幾次波動確實奏效了。所以問題在於這些波動期間。我認為，這就是那些只使用短期預測或長期預測的人會遇到麻煩的地方。他們有長期預測，卻忘記在出現波動、獲得遠高於平均的短期結果時，其實應該在那些環境中進行減碼。這說起來容易，做起來卻難得多。 你只需要為這個過程建立一個框架。所以我們討論的幾乎所有內容都可以作為這個問題的答案，但我們總是在每集節目的最後詢問所有來賓：根據你在市場中的經驗，你會教給普通投資者哪一課？以下是麥可的回答。我想鼓勵人們在思考投資世界時，學習並應用基礎比率。順帶一提，這不僅對投資有價值，實際上對商業或生活也是如此，這是一種職業生涯的智慧。再次強調，基礎比率的基本設定是：人們自然會透過收集大量資訊，結合自己的輸入和經驗，來預測未來，這是我們在自主思考時都會做的事。而運用基礎比率則意味著，我會將當前面臨的情況或問題視為一個更大參考類別中的實例，基本上我會問：當其他人或組織過去處於類似位置時，發生了什麼事？ 這是一種非常自然的思考方式，因為你必須暫時放下自己的資訊收集和個人經驗。我們往往會賦予這些東西很高的價值。你必須尋找並參考基礎比率，這可能不是隨手可得的資訊，而且通常確實如此。所以你需要花點功夫去尋找。但一旦找到，我認為它會重塑你對世界的許多看法，並讓你在思考事情可能如何發展時更加腳踏實地。 對我來說，如果只能給一個建議，那就是：讓我們思考基礎比率。你之前開玩笑提到現在正值人們做預測的季節，這正是基礎比率能派上用場的絕佳例子。你開玩笑說「10%加減些標準差」，但這其實是正確的思考方式，也是正確的答案——而這正是基於基礎比率得出的結論。所以你已經用這個實際技巧找到了正確的思考路徑。因此對我而言，這就是我想給的建議。 如果我能回到二十歲的自己，那無疑是我會傳授的智慧。因此，你和我都將此視為對這個問題的最佳回答之一。我們曾針對收到的 119 個回答製作了一集特別節目。但這個概念實在太有趣了，而麥可也稍微提到了這點——我驚訝於人們運用或理解它的程度竟如此之低。我總是想審視眼前的現實，試圖進行自己的分析，並預測未來會發生什麼。然而大多數時候，只需問問自己：過去類似情況發生時，未來結果如何？運用所謂的「外部觀點」，在多數情況下，這遠比試圖進行所有細部分析更為有效。正如麥可所言，這種方法被嚴重低估，幾乎沒人這麼做。當你這麼做時，看起來並不顯得聰明——這或許正是它未被廣泛採用的原因之一。但我認為，大多數時候，這種方法確實能帶來更好的結果。這個概念，我認為正是核心原則之一。 這可能對任何進行因子投資或思考方式較為量化的人來說，都是非常實用的收穫。意思是說，存在著一些因子或不同的方式來描述隨著時間發生的事情。我們可以將某件事視為一個實例、一個先驗或更大的參考類別來檢視。然後我們會發現，嘿，這和那件事有點相似。上一次有公司像這樣是什麼時候？先別管他們團隊承諾的目標和總可觸及市場有多大。而是想想，好吧，上一次有公司處於這種成長曲線是什麼時候？或許我得看看不同的產業。多梅特隆教授在這方面做得非常出色，經常進行這樣的比較。莫博辛在他的工作中也提供了許多精彩的例子。但我們可以從那些並非完全匹配，卻有相似之處的地方開始觀察，並以此作為錨定分析的基礎，錨定你的思考：如果某家公司要以這麼快的速度成長，其他公司能維持這種成長率多久？甚至可以用這樣的條件作為補充。 讓我舉一個具體的例子。假設我想投資一家以 20 倍營收倍數交易的公司。假設我研究過這家公司，認為它具有驚人的成長前景等等。這很好，我可能在這方面是正確的。但這只是我的內部觀點——我對公司的分析讓我認為這家公司價值遠高於當前價格。 然而，我應該將這個內部觀點與外部觀點結合起來。外部觀點會問：如果回顧歷史上所有曾以 20 倍或更高營收倍數交易的公司，投資這些公司能獲得什麼樣的回報？當你檢視這個數據時，會得到一個非常令人清醒的結果：你會發現這類參考群體的表現非常、非常糟糕。 這並不意味著我的內部觀點一定是錯的——或許我對這家公司的判斷是正確的。但至少這意味著，我需要用「過去發生類似情況時，投資者回報確實極差」這個事實來補充我的內部觀點。 我認為這個方法可以應用在個別證券或公司層面，甚至可以一路延伸到投資組合建構的層級。 回到你之前提到的國際價值案例。我可以這樣理解，並說：我現在能將它在我的投資組合中定位。至少，作為一種對沖手段，如果我持有的美國股票可能被高估（至少在幾個月前是如此），那麼我持有的國際股票可能被低估。這給了我一個理由進行一些再平衡，並理解如何將這些資產配對。你永遠不會單獨使用一個因素。這真的非常有用。基準利率有點笨拙，這就是為什麼它們有點混亂。而且它們會讓你聽起來不那麼聰明或不那麼令人興奮。

You'll be a little bit of a debby down or at the party when you go, ah, that 20 times sales opportunity. You know, less than 1% of the time does this ever work out, especially when you're sitting there next to the party man going like, hey, none of my companies have any sales. Look how much they've gone up. I'd be the debby down a guy at the party who's saying that. So I can certainly relate. But yeah, hopefully, you know, if you've enjoyed these, please comment on YouTube or like it. And these, we're going to try to do more of these because one of the things we've realized when we go back through our old interviews is there's so many insights and they still apply today. And although maybe people don't watch the interviews as much that happened three years ago, there's so much stuff we can bring forward and maybe apply to what's going on on the current market. So we're going to do more of these, you know, hopefully people learn from it. Thank you for watching and we'll see you next time.
當你在派對上提到那個 20 倍銷售機會時，可能會顯得有點掃興。你知道嗎？這種情況成功的機率不到 1%，尤其是當你坐在派對狂熱者旁邊，聽他說：「嘿，我投資的公司根本沒有營收，看看它們漲了多少！」我大概就是那個在派對上潑冷水的傢伙。所以我完全能理解這種感受。 總之，如果你喜歡這些內容，請在 YouTube 留言或按讚。我們打算製作更多這類影片，因為回顧過往訪談時，我們發現其中蘊含大量至今仍適用的洞見。雖然三年前的訪談可能觀看數不高，但許多內容都能延伸應用在當前市場狀況中。希望這些內容能帶給大家啟發，感謝觀看，我們下次見！