Russell Clark is a hedge fund investor who has lived and worked in both Japan and China. He writes the widely followed Substack Capital Flows and Asset Markets: https://www.russell-clark.com/
Steve and Russell discuss:
0:00 Introduction
0:52 Russell's background and experiences in Japan
13:25 Hong Kong and finance
31:53 China property bubble
48:54 Dollar status as global reserve currency
56:09 Japan and China economies from a long run perspective
1:05:07 Inflation, US economy, and macro observations
Louis-Vincent Gave of Gavekal discusses China's economic growth, its focus on education, and the global implications of its economic and political policies.
I really enjoyed this conversation. Taylor is a very unique investor who relocated his fund to Shenzhen in order to have direct access to information on Chinese tech companies.
Taylor Ogan is Chief Executive Officer of Snow Bull Capital, based in Shenzhen, China.
David Paul Goldman is an American economic strategist and author, best known for his series of online essays in the Asia Times under the pseudonym Spengler with the first column published January 1, 2000.
Steve and David discuss:
0:00 Introduction
2:22 David’s background in music, finance, and Asia
16:55 Looking back at the financial crisis
23:04 Rise of the Chinese economy
29:44 How Huawei’s strength is tied to China’s economic power
36:49 Competition in the global electric vehicles market
38:06 Why David thinks European countries like Germany will become closer with China
45:29 U.S. manufacturing is falling behind
52:08 Potential for war and ongoing U.S.-China competition
Sebastian Mallaby is a writer and journalist whose work covers financial markets, international relations, innovation, and technology. He is the author of "The Power Law: Venture Capital and the Making of the New Future."
Steve and Sebastian discuss venture capital, tech startups, business model and technology innovation, global adoption of the Silicon Valley model, and the future of innovation.
This short story has it all -- genetic genealogy, ultra high net worth physics quant banker, stripper, cop, marriage, family, New Yorker writer. It's fiction, but based on real characters and stories.
There is an audio version, read by the author, at the link.
Satellites by Rebecca Curtis (The New Yorker July 5, 2021)
My husband and Tony were anxiety-ridden workaholics who’d focussed, from a young age, on earning cash. Tony wanted enough for a good life; Conor, enough to feel safe. They were fifty-six years old, though Conor looked forty-five and Tony thirty-five. They were meticulous, but owing to oversights they’d each had five kids by four women. They were two nerds from New Hampshire.
...
His ancestors, he told me, had founded America. He’d started working at age twelve, as a farmhand, and eventually acquired a Ph.D. in quantum physics from Harvard, then served for decades as the “head quant” at a world-renowned investment bank. But he wasn’t smart enough to be skeptical when go-go dancers said, Don’t worry, I’m on the pill.
...
After high school, Tony turned down a scholarship to the University of New Hampshire. He wanted to work. He did active duty in the Marines for eight years, then served in the Air National Guard for twenty while working as a cop. Now he collected his police pension and, for fun, drove a delivery truck.
...
Conor smiled. By the way, he said, had Tony ever done 23andMe or Ancestry.com?
Tony squinted. Ancestry. Sinead bought them kits for his birthday. Why?
Conor peered up at Jupiter, approaching Saturn for the great conjunction, and the murky dimmer stars. I studied shuttered restaurants. A few bars had created outdoor dining rooms and were busy; the 7-Eleven was dark, but the ever-glowing “Fortune Teller!” sign on the adjacent cottage was lit.
No reason, Conor said. Had Tony, he asked, opted into his family DNA tree, to see his matches who’d already done Ancestry? Or elected to receive text alerts whenever some new supposed relative signed on?
Tony walked swiftly. Nah, he said. He’d done Ancestry to make Sinead happy. He shrugged. She’d made their accounts, he said. She probably opted him in; he wasn’t sure.
When we got home, Tony’s phone had twenty missed calls.
...
Men Without Women, Ernest Hemingway 1927. "Hemingway begins to examine the themes that would occupy his later works: the casualties of war, the often uneasy relationship between men and women, ..."
In “Satellites,” your story in the Fiction Issue, a woman and her husband, a retired banker, host the husband’s friend at their Jersey-shore mansion. The woman is a frustrated writer, and, to inspire her, her husband, Conor, asks the friend, Tony, a retired police officer, to tell her cop stories. How would you describe the woman’s views of these two men?
The narrator is awed by how smart Tony and her husband are, and by how hard they work. She’s impressed that they’ve read so much and educated themselves about so many diverse topics while performing demanding and often unpleasant jobs, and by the fact that they’re two of the most generous, kind people she knows. She appreciates that they’ve maintained lifelong friendships, something that she wishes she’d done herself. She doesn’t agree with all their political ideas. Earlier in her life, she believed that, one, bankers cared about money but not about art, literature, world hunger, etc.; and, two, that anyone who supported Trumpish policies (or who voted for anyone like Trump) must be an ignorant jerk. Meeting her husband (and Tony) punctured those beliefs.
The narrator views herself as the proverbial grasshopper: someone—possibly frivolous, vapid, and solipsistic—who wants to enjoy her life, sing, dance, make “art,” while working various hip-but-not-very-remunerative jobs to pay rent, never truly planning for winter. Tony and Conor are ants: anxious, alert to the dangers the world can pose, doing difficult (and sneered-upon) jobs diligently so they’ll be protected when scarcity comes. The narrator aspires to be more ant-like while remaining a grasshopper.
Tony and Conor are, in some ways, obsessed with genetics and lineage—they discuss Ancestry.com and bloodlines—but their own families (they each have five children by four women) are somewhat of a disappointment, or even an afterthought, to them. Can you say a little about that tension?
Conor and Tony suffer because—in several cases—they don’t have the ability to see their children. In the case of divorce, a time-sharing agreement may be in place, but, if the mother has principal custody and won’t permit the father’s visits, what can the father do? Possession sometimes is nine-tenths of the law. Hiring lawyers and going to court to try to force a mother who won’t honor custody agreements to do so requires copious energy, oodles of spare time, and a small fortune. Conor and Tony care deeply about their children, but they’ve lost control—in some cases, of seeing their kids, and, in others, of influencing them. They may feel powerless.
I agree with Louis Gave's take on most of the topics discussed. Gavekal manages a China fixed income fund and some other China-focused funds, so he is talking his book. But the arguments stand on their own.
At ~45m, a good discussion of digital RMB and why it will break the technology record for fastest adoption by first 1 billion users. See earlier discussion (Ray Dalio) on de-dollarization and digital RMB here.
The history of American power, as it is commonly written, is a weighty subject, a matter of military and economic heft, of ‘throw-weight’, of resource mobilisation and material culture, of ‘boots on the ground’. In his lecture, Adam Tooze examines an alternative, counterintuitive vision of America, as a power defying gravity. This image gives us a less materialistic, more fantastical and more unstable vision of America’s role in the world.
The Q&A at 1h03min is probably the best (at least most concise) part of the talk. I don't find the Geithner anecdote quite as important / symbolic as Tooze does. Geithner is expressing the point that financial markets and economies are heavily affected by animal spirits, investor confidence, etc. Geithner understands well how much the power of central banks depends on purely psychological multiplier effects.
From a YouTube comment, this outline:
1:10 - Tim Geithner; U.S. Treasury: America had been “defying gravity"
5:50 - U.S. was the “gravity” of world
11:07 - U.S. is now also subject to the “gravity” of world
13:28 - 100 years of 9 historic U.S. events; Overview
14:44 - Adam Tooze; Historian
“Ordering rather than Order, and the Disordering effects of efforts at Ordering.”
16:28 - Start at the beginning of 1800’s
17:12 - 1898 U.S. Imperialist power
17:50 - 1916 U.S. Globalist power
18:47 - Woodrow Wilson; U.S. President
22:46 - 1920s Republican domestic priority of Financial Austerity and Tax cuts.
25:59 - Great American Financial shocks/panics;
1857, 1873, 1893, 1896,
1907, 1920, 1929
26:49 - 1920s Great Depression
27:18 - 1930s U.S. Hyper militaristic power
31:51 - World War 2; One World, One War (1942)
33:48 - Post World War 2, Bretton Woods economic conference.
36:24 - Marshall Plan not the same as Bretton Woods...
41:10 - Cold War: Asia
43:30 - U.S. President Nixon abandons the Gold peg in 1971. Which results in inflation in G7 countries and Switzerland.
44:10 - Keynesian era 50s to 60s. Start of Neoliberalism or the Paul Volcker shock 1979.
45:07 - Cold War: Europe 1980s, Reagan & Gorbachev
47:13 - Concluding phase of the talk
1:01:56 - Challenges in 2019 and going forward; China and Climate Change
1:03:20 - Q&A
Also recommended: Tooze on US-China geopolitical competition (August 6 2020 Sinica podcast). This discussion focuses more on the present and future than the past and may be of more interest to readers.
This conversation with Tyler Cowen is excellent, with more focus on Europe.
This is part 3 of a discussion at the Paris School of Economics. Thomas Piketty is on the panel and his remarks are in part 2, following Tooze's presentation in part 1. I recommend part 3 as the most interesting. Topics covered include MMT, inequality, central banks, current sources of systemic risk. Note this discussion took place before the Covid19 pandemic. Tooze mentions individual hedge fund compensation in the hundreds of millions or billions of dollars. Typically in such cases a big chunk of this compensation is really returns from the individual's own net worth which is co-invested with the fund. So it's not directly comparable to other forms of compensation, such as salary or bonus.
Steve and Corey talk to Warren Hatch, President and CEO of Good Judgment Inc. Warren explains what makes someone a good forecaster and how the ability to integrate and assess information allows cognitively diverse teams to outperform prediction markets. The hosts express skepticism about whether the incentives at work in large organizations would encourage the adoption of approaches that might lead to better forecasts. Warren describes the increasing depth of human-computer collaboration in forecasting. Steve poses the long-standing problem of assessing alpha in finance and Warren suggests that the emerging alpha-brier metric, linking process and outcome, might shed light on the issue. The episode ends with Warren describing Good Judgment’s open invitation to self-identified experts to join a new COVID forecasting platform.
In mathematics, a manifold is a topological space that locally resembles Euclidean space near each point.
Steve Hsu and Corey Washington have been friends for almost 30 years, and between them hold PhDs in Neuroscience, Philosophy, and Theoretical Physics. Join them for wide ranging and unfiltered conversations with leading writers, scientists, technologists, academics, entrepreneurs, investors, and more.
Steve Hsu is VP for Research and Professor of Theoretical Physics at Michigan State University. He is also a researcher in computational genomics and founder of several Silicon Valley startups, ranging from information security to biotech. Educated at Caltech and Berkeley, he was a Harvard Junior Fellow and held faculty positions at Yale and the University of Oregon before joining MSU.
Corey Washington is Director of Analytics in the Office of Research and Innovation at Michigan State University. He was educated at Amherst College and MIT before receiving a PhD in Philosophy from Stanford and a PhD in a Neuroscience from Columbia. He held faculty positions at the University Washington and the University of Maryland. Prior to MSU, Corey worked as a biotech consultant and is founder of a medical diagnostics startup.
Steve and Corey talk with theoretical physicist turned hedge fund investor Vineer Bhansali. Bhansali describes his transition from physics to finance, his firm LongTail Alpha, and his recent outsize returns from the coronavirus financial crisis. Also discussed: derivatives pricing, random walks, helicopter money, and Modern Monetary Theory.
Added: Background on derivatives history here. AFAIK high energy physicist M.F.M. Osborne was the first to suggest the log-normal random walk model for securities prices, in the 1950s. Bachelier suggested an additive model which does not even make logical sense. See my articles in Physics World: 1 , 2
man·i·fold /ˈmanəˌfōld/ many and various.
In mathematics, a manifold is a topological space that locally resembles Euclidean space near each point.
Steve Hsu and Corey Washington have been friends for almost 30 years, and between them hold PhDs in Neuroscience, Philosophy, and Theoretical Physics. Join them for wide ranging and unfiltered conversations with leading writers, scientists, technologists, academics, entrepreneurs, investors, and more.
Steve Hsu is VP for Research and Professor of Theoretical Physics at Michigan State University. He is also a researcher in computational genomics and founder of several Silicon Valley startups, ranging from information security to biotech. Educated at Caltech and Berkeley, he was a Harvard Junior Fellow and held faculty positions at Yale and the University of Oregon before joining MSU.
Corey Washington is Director of Analytics in the Office of Research and Innovation at Michigan State University. He was educated at Amherst College and MIT before receiving a PhD in Philosophy from Stanford and a PhD in a Neuroscience from Columbia. He held faculty positions at the University Washington and the University of Maryland. Prior to MSU, Corey worked as a biotech consultant and is founder of a medical diagnostics startup.
Steve and Corey talk to Kieren James-Lubin and Victor Wong of the blockchain technology startup, BlockApps. They begin with a discussion of the COVID-19 epidemic (~25m): lockdown, predictions of ICU overload, and helicopter money. Will personal contact tracking become the new normal? Transitioning to blockchain, a technology many view as viable even in times of widespread societal disruption, they give a basic explanation of the underlying cryptographic and consensus algorithms. Kieren and Victor explain how BlockApps was founded, its business model, and history as a startup. They conclude with a comparison of startup ecosystems in China, Silicon Valley, and NYC.
Recorded on March 18, 2020. Now (March 26) I feel we can make much stronger predictions about CV-19 in the US. We will definitely see overloaded health systems (ICUs) across a broad part of the country. It is already starting to happen in NYC. I will be surprised if the US can avoid tens of thousands of fatalities by early April (say, 14 days from today).
1:08 - Lockdown and ICU Overload COVID-19 5:22 - Singapore and Taiwan Response 17:28 - Government Intervention and Helicopter Money 22:13 - End of the Lockdown?
25:58 - How BlockApps got started 28:56 - Private & Public Key Cryptography and Digital Signatures 34:40 - Blockchain 46:05 - Enterprise Blockchains 1:03:37 - Elevator Pitch 1:24:58 - Global Startup Scene
In mathematics, a manifold is a topological space that locally resembles Euclidean space near each point.
Steve Hsu and Corey Washington have been friends for almost 30 years, and between them hold PhDs in Neuroscience, Philosophy, and Theoretical Physics. Join them for wide ranging and unfiltered conversations with leading writers, scientists, technologists, academics, entrepreneurs, investors, and more.
Steve Hsu is VP for Research and Professor of Theoretical Physics at Michigan State University. He is also a researcher in computational genomics and founder of several Silicon Valley startups, ranging from information security to biotech. Educated at Caltech and Berkeley, he was a Harvard Junior Fellow and held faculty positions at Yale and the University of Oregon before joining MSU.
Corey Washington is Director of Analytics in the Office of Research and Innovation at Michigan State University. He was educated at Amherst College and MIT before receiving a PhD in Philosophy from Stanford and a PhD in a Neuroscience from Columbia. He held faculty positions at the University Washington and the University of Maryland. Prior to MSU, Corey worked as a biotech consultant and is founder of a medical diagnostics startup.
First some basic assumptions, for which I think the evidence is strong (reference):
1. R0 ~ (2-3) or higher in a permissive environment -- no strong efforts at social distancing, quarantine, etc.
2. Fatality rate: roughly 1 percent of cases, heavily concentrated in older individuals and/or those with pre-existing conditions. Note this assumes a well-functioning health system and resources for the 5% or so of cases that need intensive care. See below.
3. In situations like #1 above, doubling time could be as short as a few days. Number of infections in Italy grew by ~1000x over the month of February -- i.e., 2^10 or 2+ doublings per week!
USA has perhaps ~1M total hospital beds, over half already occupied, and perhaps 50k ICU spaces. For those infected, the distribution of severity is roughly (again, concentration in vulnerable sub-populations):
So roughly 1M infected at a given time would overwhelm US health capabilities. We probably have at least ~1000 infected in the country at the moment, so in the absence of serious measures like social distancing (cancellation of sporting events, large meetings, moving to K12 and college distance learning, etc.), we would reach the health system breaking point in about a month. Many other countries, in Europe and elsewhere, are facing a similar situation.
Whether we impose draconian social measures (which would have a strongly negative effect on cafes, restaurants, hotels, airlines, theaters, etc.) or let COVID-19 infect millions of people, we are in for at least a one quarter downturn (recession?) with the possibility of more significant nonlinear events (complete market collapse, systemic failures). Traders already understand this, which is why equities are in huge decline despite a 50 bp Fed rate cut last week. I went largely to cash already...
We have technology that could help us fight the epidemic. The article below, in the Journal of the American Medical Association, describes how Taiwan successfully handled the epidemic -- less than 50 cases! -- despite close proximity and extensive travel to China. (Note, Taiwan in Jan-Feb is a bit warmer than Milan, but I don't think climate is the entire reason for their good performance...) Google and Apple have these technical (geolocation, tracking) capabilities, but they don't like to emphasize it to the public.
JAMA. Published online March 3, 2020. doi:10.1001/jama.2020.3151
Taiwan is 81 miles off the coast of mainland China and was expected to have the second highest number of cases of coronavirus disease 2019 (COVID-19) due to its proximity to and number of flights between China.1 The country has 23 million citizens of which 850 000 reside in and 404 000 work in China.2,3 In 2019, 2.71 million visitors from the mainland traveled to Taiwan.4 As such, Taiwan has been on constant alert and ready to act on epidemics arising from China ever since the severe acute respiratory syndrome (SARS) epidemic in 2003. Given the continual spread of COVID-19 around the world, understanding the action items that were implemented quickly in Taiwan and assessing the effectiveness of these actions in preventing a large-scale epidemic may be instructive for other countries.
COVID-19 occurred just before the Lunar New Year during which time millions of Chinese and Taiwanese were expected to travel for the holidays. Taiwan quickly mobilized and instituted specific approaches for case identification, containment, and resource allocation to protect the public health. Taiwan leveraged its national health insurance database and integrated it with its immigration and customs database to begin the creation of big data for analytics; it generated real-time alerts during a clinical visit based on travel history and clinical symptoms to aid case identification. It also used new technology, including QR code scanning and online reporting of travel history and health symptoms to classify travelers’ infectious risks based on flight origin and travel history in the past 14 days. Persons with low risk (no travel to level 3 alert areas) were sent a health declaration border pass via SMS (short message service) messaging to their phones for faster immigration clearance; those with higher risk (recent travel to level 3 alert areas) were quarantined at home and tracked through their mobile phone to ensure that they remained at home during the incubation period.
Google + Apple + cell carriers have the data to detect near collisions (proximity) between COVID-19 spreaders (once diagnosed) and other individuals. This can be done anonymously: i.e., public health services get a warning that a spreader visited nursing home X on timestamp T without naming the spreader. Another alternative (see WSJ video below) is to have an OPT-IN app that checks location history and warns if you were in close proximity to a spreader. The PRC government version of this app has been used by 200M+ Chinese already -- note it's OPT-IN. The companies above have the data to do this but don't like it to be known that they can.
Note Added: Latest results from the beginnings of broader testing in Seattle suggest that the virus is widespread in the community already. The number of cases detected is primarily limited by number of tests:
Nature: “We are past the point of containment,” says Helen Chu, an infectious-disease specialist at the University of Washington School of Medicine (UW Medicine) in Seattle. “So now we need to keep the people who are vulnerable from getting sick.”
I checked the weather records for Seattle in February 2020: daily highs in the mid-40s to mid-50s. There is very little chance that wintry areas of the US will be warmer than this over the next 30 days, even with an early spring. So I don't see that US spread in those regions can be mitigated by anything less than social distancing and other strong measures. Weather will probably not save us.
Front line tweets from Italian doctors describe medical resources pushed to the breaking point. I hope we do not experience this in the US, but I don't see how we will avoid it: [1] [2]
Some interesting information about transmission (surfaces, air in confined spaces) at the beginning; Italian overload at 17m; S. Korean data at 19m.
... Before long, the Bank of England (the British equivalent of the Federal Reserve, whose economists are most free to speak their minds since they are not formally part of the government) rolled out an elaborate official report called “Money Creation in the Modern Economy,” replete with videos and animations, making the same point: existing economics textbooks, and particularly the reigning monetarist orthodoxy, are wrong. The heterodox economists are right. Private banks create money. Central banks like the Bank of England create money as well, but monetarists are entirely wrong to insist that their proper function is to control the money supply. In fact, central banks do not in any sense control the money supply; their main function is to set the interest rate—to determine how much private banks can charge for the money they create. Almost all public debate on these subjects is therefore based on false premises. For example, if what the Bank of England was saying were true, government borrowing didn’t divert funds from the private sector; it created entirely new money that had not existed before.
[[ Certainly central banks influence the money supply, but the degree to which they control animal spirits, lending practices and standards, the price of credit risk in general, etc. via a single part of the yield curve is highly debatable, dependent on many factors such as investor psychology and recent events, etc. etc. There is no doubt this is a complex question worthy of deep analysis ...
At any instant in time there is a certain level of tolerance for borrowing from the future (private and public debt), and merely by changing this level of tolerance one can in effect create money out of thin air ... This level of tolerance is a completely emergent phenomenon and no one fully controls it. ]]
... one of the most significant books to come out of the UK in recent years would have to be Robert Skidelsky’s Money and Government: The Past and Future of Economics. Ostensibly an attempt to answer the question of why mainstream economics rendered itself so useless in the years immediately before and after the crisis of 2008, it is really an attempt to retell the history of the economic discipline through a consideration of the two things—money and government—that most economists least like to talk about.
On the question of whether academic economists understand how the world works, I'll just reiterate that at the time of the last financial crisis (circa 2007-2008) I became aware through direct experience that many very prominent economists did not know what a Credit Default Swap was, did not know how the credit markets actually worked, did not know how credit risk was priced. Instead, their mental model consisted of coarse graining over all of this activity (quants, traders, mobs, speculators, thieves, fraudsters) as simply a (more or less) rational and efficient market not worthy of deep inspection.
They will all deny it now, of course. But I was there.
Note added: In the 1990s, in part due to the collapse of the Soviet empire and resulting mass emigration of top scientists to the West, there were very few opportunities in theoretical physics and related fields for young researchers. Consequently large numbers of extremely talented people left the field (largely against their will) and perhaps most of them ended up in finance. As might be expected a large number of big brains began thinking about previously obscure topics such as options pricing (derivatives, Black Scholes), credit risk, the yield curve, etc. Immediately it was noted, by myself and others, that methods from imaginary time quantum mechanics, path integrals, etc., could be applied to the pricing of derivatives -- especially exotic derivatives which had, up to that time, required significant computational resources to simulate.
The yield curve and credit derivatives are especially challenging problems. One reason is that they deal with a potentially infinite (if a continuous curve is assumed) number of degrees of freedom. As one of my former Caltech-Harvard collaborators (by the 1990s a quant-trader, now a hedge fund magnate) described it, modeling the yield curve compared to pricing equity derivatives is like quantum field theory compared to simple quantum mechanics.
In modeling the yield curve one immediately asks: what are the underlying dynamics? What are reasonable consistency conditions? What is the impact of a "shock" like a change in the Fed funds rate? A moment of reflection reveals that market psychology plays a huge role in setting the model parameters... A bit of historical investigation shows radical changes in the yield curve (and, consequently, the effective "money supply") over time. One can in effect create money out of thin air!
Ron Unz is the publisher of the Unz Review, a controversial but widely read alternative media site hosting opinion outside of the mainstream, including from both the far right and the far left. Unz studied theoretical physics at Harvard, Cambridge and Stanford. He founded the software company Wall Street Analytics, acquired by Moody’s in 2006, and was behind the 1998 ballot initiative that ended bilingual education in California.
In mathematics, a manifold is a topological space that locally resembles Euclidean space near each point.
Steve Hsu and Corey Washington have been friends for almost 30 years, and between them hold PhDs in Neuroscience, Philosophy, and Theoretical Physics. Join them for wide ranging and unfiltered conversations with leading writers, scientists, technologists, academics, entrepreneurs, investors, and more.
Steve Hsu is VP for Research and Professor of Theoretical Physics at Michigan State University. He is also a researcher in computational genomics and founder of several Silicon Valley startups, ranging from information security to biotech. Educated at Caltech and Berkeley, he was a Harvard Junior Fellow and held faculty positions at Yale and the University of Oregon before joining MSU.
Corey Washington is Director of Analytics in the Office of Research and Innovation at Michigan State University. He was educated at Amherst College and MIT before receiving a PhD in Philosophy from Stanford and a PhD in a Neuroscience from Columbia. He held faculty positions at the University Washington and the University of Maryland. Prior to MSU, Corey worked as a biotech consultant and is founder of a medical diagnostics startup.
Yuan-Dollar-Oil discussion starts about 20min in. Any professionals want to weigh in?
In the past the main drivers of oil prices were supply-demand and dollar confidence (all transactions in dollars). Now you can add dollar-yuan fx factors... even gold.
Have idiots screaming about Khashoggi thought about what happens if Saudi starts accepting Yuan for oil, as Russia, Iran, and Venezuela do now?
David Goldman writes the Spengler column for the Asia Times. He has been a keen observer of geopolitics, economics, and finance in the Asia-Pacific region for many decades, as well as a banker and financial analyst.
This talk is an entertaining blend of insight and sensationalism ;-)
...The encouragement that the fifty-five-year-old psychology professor offers to his audiences takes the form of a challenge. To “take on the heaviest burden that you can bear.” To pursue a “voluntary confrontation with the tragedy and malevolence of being.” To seek a strenuous life spent “at the boundary between chaos and order.” Who dares speak of such things without nervous, self-protective irony? Without snickering self-effacement?
“It’s so sad,” he says. “Every time I go to these talks, guys come up and say, ‘Wow, you know, it’s working.’ And I think, Well, yeah. No kidding! Nobody ever fucking told you that.”
"...When he says, ‘Life is suffering,’ that resonates very deeply. You can tell he’s not bullshitting us."
This is a profile of a guy I happen to have met recently at a fancy event (thx for cigars, Mike!), but it's also a reflection on the evolution (or not) of finance over the last few decades.
... And yet the majority of the hedge funders I befriended were not living happier or more interesting lives than my friends who had been exiled from the city. They had devoted their intellects and energies to winning a game that seemed only to diminish the players. One book I was often told to read was “Reminiscences of a Stock Operator,” first published in 1923. Written by Edwin Lefèvre, the novel follows a stockbroker named Lawrence Livingston, widely believed to be based on Jesse Livermore, a colorful speculator who rose from the era of street-corner bucket shops. I was astounded by how little had changed between the days of ticker tape and our own world of derivatives and flash trading, but a facet that none of the book’s Wall Street fans had mentioned was the miserableness of its protagonist. Livingston dreams of fishing off the Florida coast, preferably in his new yacht, but he keeps tacking back up to New York for one more trade. “Trading is addictive,” Novogratz told me at the Princeton reunion. “All these guys get addicted.” Livermore fatally shot himself in New York’s Sherry-Netherland Hotel in 1940.
... Novogratz had described another idea to me, one several magnitudes more audacious—certainly more institutional, and potentially more durable—than a mere half-a-billion-dollar hedge fund. He wanted to launch a publicly traded merchant bank solely for cryptocurrencies, which, with characteristic immodesty, he described as “the Goldman Sachs of crypto,” and was calling Galaxy Digital. “I’m either going to look like a genius or an idiot,” he said.
... On the day we met at his apartment, a regulatory crackdown in China, preceded by one announced in South Korea, was pushing the price of bitcoin down. (It hasn’t returned to its December high, and is currently priced at around seven thousand dollars.) Meanwhile, it appeared that hedge funds, many of which had ended 2016 either ailing or dead, were reporting their best returns in years. After six years of exploring finance, I concluded that, despite the expertise and the intelligence on display, nobody really knows anything. “In two years, this will be a big business,” Novogratz said, of Galaxy Digital. “Or it won’t be.”
Here I am with an espresso, waiting for a meeting with Mike at Galaxy Digital.
The arXiv paper below looks at stochastic dynamical models that can transform initial (e.g., Gaussian) talent distributions into power law outcomes (e.g., observed wealth distributions in modern societies). While the models themselves may not be entirely realistic, they illustrate the potentially large role of luck relative to ability in real life outcomes.
We're used to seeing correlations reported, often between variables that have been standardized so that both are normally distributed. I've written about this many times in the past: Success, Ability, and All That , Success vs Ability.
But wealth typically follows a power law distribution:
Of course, it might be the case that better measurements would uncover a power law distribution of individual talents. But it's far more plausible to me that random fluctuations + nonlinear amplifications transform, over time, normally distributed talents into power law outcomes.
The largely dominant meritocratic paradigm of highly competitive Western cultures is rooted on the belief that success is due mainly, if not exclusively, to personal qualities such as talent, intelligence, skills, smartness, efforts, willfulness, hard work or risk taking. Sometimes, we are willing to admit that a certain degree of luck could also play a role in achieving significant material success. But, as a matter of fact, it is rather common to underestimate the importance of external forces in individual successful stories. It is very well known that intelligence (or, more in general, talent and personal qualities) exhibits a Gaussian distribution among the population, whereas the distribution of wealth - often considered a proxy of success - follows typically a power law (Pareto law), with a large majority of poor people and a very small number of billionaires. Such a discrepancy between a Normal distribution of inputs, with a typical scale (the average talent or intelligence), and the scale invariant distribution of outputs, suggests that some hidden ingredient is at work behind the scenes. In this paper, with the help of a very simple agent-based toy model, we suggest that such an ingredient is just randomness. In particular, we show that, if it is true that some degree of talent is necessary to be successful in life, almost never the most talented people reach the highest peaks of success, being overtaken by mediocre but sensibly luckier individuals. As to our knowledge, this counterintuitive result - although implicitly suggested between the lines in a vast literature - is quantified here for the first time. It sheds new light on the effectiveness of assessing merit on the basis of the reached level of success and underlines the risks of distributing excessive honors or resources to people who, at the end of the day, could have been simply luckier than others. With the help of this model, several policy hypotheses are also addressed and compared to show the most efficient strategies for public funding of research in order to improve meritocracy, diversity and innovation.
Here is a specific example of random fluctuations and nonlinear amplification:
Nonlinearity and Noisy Outcomes: ... The researchers placed a number of songs online and asked volunteers to rate them. One group rated them without seeing others' opinions. In a number of "worlds" the raters were allowed to see the opinions of others in their world. Unsurprisingly, the interactive worlds exhibited large fluctuations, in which songs judged as mediocre by isolated listeners rose on the basis of small initial fluctuations in their ratings (e.g., in a particular world, the first 10 raters may have all liked an otherwise mediocre song, and subsequent listeners were influenced by this, leading to a positive feedback loop).
It isn't hard to think of a number of other contexts where this effect plays out. Think of the careers of two otherwise identical competitors (e.g., in science, business, academia). The one who enjoys an intial positive fluctuation may be carried along far beyond their competitor, for no reason of superior merit. The effect also appears in competing technologies or brands or fashion trends.
If outcomes are so noisy, then successful prediction is more a matter of luck than skill. The successful predictor is not necessarily a better judge of intrinsic quality, since quality is swamped by random fluctuations that are amplified nonlinearly. This picture undermines the rationale for the high compensation awarded to certain CEOs, studio and recording executives, even portfolio managers. ...
Tyler Cowen and rationality [my comments]: ... The excerpt below deals with rationality in finance theory and strong and weak versions of efficient markets. I believe the weak version; the strong version is nonsense. (See, e.g, here for a discussion of limits to arbitrage that permit long lasting financial bubbles. In other words, capital markets are demonstrably far from perfect, as defined below by Cowen.)
Although you might think the strong version of EMH is only important to traders and finance specialists, it is also very much related to the idea that markets are good optimizers of resource allocation for society. Do markets accurately reflect the "fundamental value of corporations"? See related discussion here.
...
As you can tell from my comments, I do not believe there is any unique basis for "rationality" in economics. Humans are flawed information processing units produced by the random vagaries of evolution. Not only are we different from each other, but these differences arise both from genes and the individual paths taken through life. Can a complex system comprised of such creatures be modeled through simple equations describing a few coarse grained variables? In some rare cases, perhaps yes, but in most cases, I would guess no. Finance theory already adopts this perspective in insisting on a stochastic (random) component in any model of security prices. Over sufficiently long timescales even the properties of the random component are not constant! (Hence, stochastic volatility, etc.)
Highly recommended! I blogged about this guy 5 years ago here: From physics to Goldman to Y Combinator. The book is hilarious and pretty accurate, AFAICT. I don't know much about Facebook corporate culture or that particular era of ad monetization, but the finance and startup stuff all rings true.
The reality is, Silicon Valley capitalism is very simple:
Investors are people with more money than time.
Employees are people with more time than money.
Entrepreneurs are the seductive go-between.
Startups are business experiments performed with other people's money.
I was a Berkeley PhD student in physics when the first dot-com bubble grew to bursting and popped around 2001. Between the month-long backpacking trips and the telenovela-esque romances, I switched thesis topic three times, and felt my twenty-something vitality slipping away in academic wankery. Inspired by Michael Lewis’ Liar’s Poker and the prior example of many a failed physicist, I looked for a Wall Street gig as a way out. Very improbably, I landed a job on the trading desk of Goldman Sachs, earning twice what my tenured professor made, pricing and modeling credit derivatives at ground-zero of the credit bubble. I may have owned one pair of lace-up shoes at the time, but I got used to speaking in quantities of hundreds of millions of dollars, and thinking a million was a ‘buck’, i.e., a rounding error for most purposes. I was very far away indeed from Berkeley.
Right around 2008, when Lehman Brothers and Bear Stearns blew up, I knew the financial jig would be up for a while (and possibly forever), unlike most of my colleagues, who seemed to think orgies of rapacious greed lasted forever. The only piece of the US economy that would be spared the apocalypse was clear in my mind: the Bay Area tech of my languid grad school days, and all that VC money that (hopefully) hadn’t touched the mortgage bubble..
Two weeks later, I started as employee number seventy-something at a venture-backed advertising startup so incompetent and vile I’ll save the historical distaste for later. Bookended as it was by experiences at Facebook and Goldman, my time there was instructive in its awfulness and how not to run a company. But there was one piece of upside: I learned how online advertising worked, specifically its ad exchange variants. As a ‘research scientist’ I tortured every piece of data until it confessed, and used it to predict user behavior, value of media purchased, and optimal bids in the largest ad auctions in the world. Dull stuff you might say, but it’s what pays for the Internet, and it would set me light-years ahead of anyone inside Facebook Ads, when the time came.
But we’re jumping ahead.
Along with the two best engineers at Shitty Unnamed Company, I applied and was accepted to Y Combinator, the Valley’s leading startup incubator. We pitched some wild, ridiculous idea around local businesses that was doomed from the start, which eventually morphed into a novel tool for managing Google search campaigns for small businesses. The tool was beautiful, innovative, and didn’t make us a dime. More bad news: We got vindictively and frivolously sued by Shitty Company and fought an existential legal battle we narrowly won by being lying, ruthless little shits. We couldn’t raise money. We had co- founder and morale issues. Every ill that plagues early-stage startups visited us in turn, like some admonitory biblical tale about what happens if you fuck with the Israelites. ...
... Every startup entrepreneur faces the immense disadvantage of playing a crooked, complex game for the first time, against a world composed mostly of masters. Arrayed against you is an army of wily, self-interested venture capitalists who know term sheets better than their wife’s ass. Or seductive sales execs who could make pedophilia and genocide enforceable via a legal contract. Or petulant co-founders with hidden agendas and momentarily suppressed grievances. Or ungrateful employees who are exploiting your startup until they can start their own. Or thick-headed journalists with urgent deadlines who just want you for a misleading quote. You get trounced again and again, and the only hope is that you learn something of the game before expiring. This is your principal challenge as a first-time entrepreneur: to learn the game faster than you burn cash and relationships.
