저자: 미상 · 외국인
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레이 달리오의 데이터 중심·투명성 원칙과 브리지워터의 장기 접근을 소개하고, LTCM의 채권 차익거래 전략이 1998년 러시아 채무불이행 이후 과도한 레버리지와 유동성 경색으로 붕괴한 사례를 분석한다. 두 사례를 통해 모델 의존의 한계, 레버리지 위험, 적응력의 중요성을 강조한다. 특정 자산에 대한 가격 방향 예측 발언은 없다.
다듬은 글
all right let's dive into the world of Finance today you clearly enjoy this high stakes investing World sending over those articles on Ray Doo and uh long-term Capital Management ltcm talk about two sides of the same coin huh yeah fascinating right Doo the founder Bridgewater one of the biggest hedge funds and then you have ltcm this fund that seemed Invincible then crashed and burn so badly both teach us about how markets work or well how they don't work maybe exactly so Doo first this guy is a bit of a finan Rockstar everyone's talking about his unique approach and that book of his principles what's his story Dalia's story is about building Bridgewater on this Foundation of get this radical transparency it's like imagine a workplace where every idea every single decision is openly debated even if it comes from doio himself data driven decision-making to the extreme okay data over gut feeling I get that but radical transparency sounds intense maybe a bit too intense but hey it's working for them definitely not your average workplace but this radical transparency is tied to Doo's philosophy learning from M Stakes he had a huge set back in 1982 misread the market lost big but he used it as like a launching pad his motto pain plus reflection progress that pretty much sums up his whole approach so he turns his mistakes into fuel I can dig that but what about these principles what's the big deal well one key principle is that datadriven approach Dalo really believes in studying history you know crunching the numbers understanding Cycles to see where the market is going he even built a model the economic machine to help him predict those cycles and get this he was one of the few who actually predicted the 2008 financial crisis wait he saw that coming now I need to hear about this economic machine what's he putting into it that everyone else is missing that's the big question right part of it is he's not just looking at economic data he's factoring in political shifts cultural Trends even things that seem you know completely unrelated to finance looking for those hidden connections the signals others might miss it's not just about numbers it's about understanding the bigger picture the human element behind them that's fascinating but hold on we talked about data over gut feeling but isn't this radical transparency thing kind of emotional that's a good point it's not about being emotional it's about removing your own biases using the collective intelligence of the team everyone's challenging assumptions poking holes and ideas even Doos getting to the truth interesting crowdsourcing critical thinking on a huge scale okay so data transparency what else does doo do another big one is his long-term Vision he's not interested in those get-rich quick schemes or chasing uh Trends he's in it for the long haul strategies that can take years to play out long-term Vision great in theory but is it realistic for everyone I mean when doio was starting out didn't he go for some quick wins too you're right it's easier to talk about long-term Vision when you're already at the top but doio argues even early on he was focused on building a system a way of thinking that would lead to consistent success not just chasing uh fleeting profits so build the right system the right mindset and the results will follow I like that but how do we take these principles and actually apply them to our own financial decisions that's the interesting part and that's what we'll be looking at throughout this deep dive how can you use data to make smarter choices how can you cultivate that learning mindset and maybe how much transparency is too much lots to think about so we've got doia the data Maestro playing a long game but then there's ltcm these math Wizards who uh seem to crack the Cod and went bust spectacularly what was their story their story is all about what happens when you have these Brilliant Minds meeting the the realities of the market they were exploiting price discrepancies in the bond market using complex models and leverage uh to make big bets price discrepancies okay break that down what does that even mean imagine two bonds almost identical but one is pric a little higher ltcm would bet that that Gap would close profiting from the uh the correction they were Financial arbitragers finding tiny inefficiencies and exploiting them sounds like they were playing this high stakes game of whack-a-mole financially speaking but it was working right they were making a lot of money oh yeah they were the Envy of Wall Street returns of over 40% per year I'm pulling in billions mhm but their success had a well a downside their models brilliant as they were had a fatal flaw okay so ltcm this high stakes game and it was working but you said their models had a flaw a fatal flaw what was it their models were built on this idea that well at their core markets are rational they thought their math could you know predict very accurately how prices would move but uh but they missed a big thing the thing that throws all those calculations out humanid Behavior right when fear Panic kicks in logic kind of goes out the window people aren't that rational and they're worried about losing everything yeah exactly and that's where ltcm takes a a a bad turn 1998 Russia having this financial meltdown defaults on its bonds you'd think that's unrelated to ltcm right right wrong that's where the the crack start to show hold on how did a Russian debt crisis affect this you know this Wall Street hedge fund remember those price discrepancies they were using the Russian default it it set off this chain reaction in the bond market massive volatility and those tiny price gaps well they became huge so they models didn't see this coming this event that messed up their entire strategy exactly and here's where their other big flaw comes in leverage they were borrowing a ton of money to amplify their bets High leverage High rewards but also huge losses when things go south like playing roulette with uh with borrowed chips a good analogy and as they lost more they had to sell assets to to cover their positions stay afloat but and here's the thing they weren't alone other investors hit by the Russian crisis they were also trying to fell a liquidity crunch liquidity crunch I'm picturing like a cash register but empty imagine your ltcm needing to sell billions in bonds quickly but everybody else is selling too very few buyers not enough cash flowing to handle all the selling it's like a fire sale but the fire is spreading to the whole market and everyone trying to escape exactly and the more ltcm sold prices dropped even more a downward spiral systemic risk one big player failing could bring the whole system down this is starting to sound like a a financial horror movie what happened then did they go bankrupt almost it got so bad that the Federal Reserve they had to step in they organized a bailout some major Banks involved to to get cash back into the market prevent a total collapse wow so the government had to save the day and uh probably save Wall Street from itself ltcm brought down by what a Russian debt crisis a liquidity Crunch and too much leverage a perfect storm bad decisions unexpected events and and too much faith in models they had the smarts but lacked one thing humility humility in the face of the market I like that yeah so we have Doo adapting learning from mistakes and ltcm holding on to their models as the market crashed around them two approaches very different outcomes and it's in that uh that contrast that we find the lessons for anyone dealing with Finance or any well any complex system because the real question is how do we balance the data and models with with the need to adapt to use human judgment that's a big question and I I guess that's what we'll be looking at in the final part of this deep dive okay so doio the the adaptable data King right and ltcm these Brilliant Minds but they bet big and went bust feels like a big lesson here but I'm I'm not quite seeing it it's about that balance you know relying on data and models but also being flexible ready to adapt because the unexpected always happens especially in finance that's what's so interesting Doo he almost seems to to sniff out change before it happens adapt before before the market shifts but ltcm they just they doubled down as things went bad that's the that's the difference diio is not blindly following a model he's testing refining always adapting like a chameleon changing with the uh the market ltcm they were uh well it's like that deer in the headlights Frozen while the trucks coming okay I get it doio says the data saying something new time to change ltcm thought they had it all figured out missed the uh the warning signs like navigating by the Stars versus you know just relying on a GPS the Stars can guide you but you got to read them change course when a storm comes the GPS well it's great until you hit a detour or or lose signal dolly was looking at the whole map ltcm staring at the screen but how do we find that balance data is important but we don't want to be slaves to it that's the the art of it right models and data they're tools guides not the absolute truth know their limits mhm you know just because a model worked before doesn't mean it always will so they're like simplified versions of reality and reality well reality throws curveballs yeah exactly and when those curveballs come that's for judgment your experience that's what matters you got to step back look at everything and say okay the model's saying this but my gut my experience says something else what do I do makes me think about that radical transparency thing again maybe that helps having other smart people you know to challenge you make you see those blind spots you got it diversity of thought different perspectives we all have biases blind spots the key is knowing that and being humble enough to say I might be wrong right even the smartest can get it wrong ltcm proved that and their leverage that really hurt them in the end huh leverage it's like um an accelerant it can boost your gains but also you know send you crashing down diio gets that he uses leverage but carefully managing the risk ltcm they went all in and when the tide turned they were well they were wiped out lesson learned don't bet the farm especially with borrowed money but what are the the big takeaways for someone who's you know interested investing or just trying to to make sense of the world First Data and models powerful tools but just tools don't let them run your life got it data's good but don't be a data zombie what else second leverage it's tempting but dangerous Handle With Care leverage Handle With Care okay writing that down anything else and the most important one maybe adaptability the world changes markets change being able to adjust to learn that's how you how you thrive be like doo the chameleon not ltcm stuck in the headlights adapt or well or fade away I like it how do we actually do that become more adaptable it's a it's a lifelong thing right seeking out new information challenging yourself and being around people who are you know smarter than you embrace the uncertainty don't be afraid of it it's true Finance life it's all uncertain it's about how we deal with that how we learn from mistakes and adapt to change yeah well said and maybe just maybe takes a bit of humility humility adaptability a little bit of skepticism we found some real gems today so as we wrap up this deep dive into Doo ltcm and the world of investing I'm left thinking how can you use these insights in your own financial decisions your own risk tolerance because in the end it's not just about money it's about how we think how we learn and how we deal with this complex changing World thanks for joining us
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