We’ve constantly been sharing a list of our recent reads in our weekly emails for The Good Investors.
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But since our readership-audience for The Good Investors is wider than our subscriber base, we think sharing the reading list regularly on the blog itself can benefit even more people. The articles we share touch on a wide range of topics, including investing, business, and the world in general.
Here are the articles for the week ending 07 April 2024:
1. China’s capitalist experiment – Michael Fritzell
I just read a great new book by analyst Anne Stevenson-Yang. It’s called Wild Ride and is available for pre-order on Amazon.
The book tells the story of China’s economic miracle from the late 1970s until today － how Deng Xiaoping’s reforms unleashed a wave of entrepreneurship and led to China’s economy becoming one of the largest in the world.
However, it also discusses some of the system’s fragilities and how the country now seems to be turning inwards again…
…China under Mao Zedong was a closed-off, repressive society. Meat was a once-in-a-week luxury. Cooking was done outside. And personal freedoms were more or less non-existent…
…After Mao died in 1976, a power struggle ensued. Ultimately, Mao’s former ally, Deng Xiaoping, emerged victorious from this struggle. One of his first tasks was to open up the economy to the outside world. For this, he would need hard currency.
Practical considerations took priority in those early years. When Deng Xiaoping travelled to the United States in 1979, he ordered an inventory of all hard currency in China’s banks. He came up with only US$38,000 － hardly enough to pay for his delegation.
This was a low point for the Chinese economy. Deng recognized that China needed exports. Japan, Korea, and Taiwan became wealthy by promoting the export of manufactured goods. So Deng adopted a twin strategy of promoting exports in special economic zones while shielding ordinary Chinese from foreign cultural influences…
..Deng’s special economic zones were newly incorporated entities acting as quasi-governments. What made them different was that their managers were rewarded by meeting targets focused on the scale of capital investment and gross tax revenues…
…Initially, foreign influence was kept at bay. Foreign nationals were required to live in special compounds, use separate medical facilities, and even use special currencies. Romantic relationships between foreigners and Chinese were forbidden as well.
The special economic zones in the Southern parts of the Guangdong province, such as Shenzhen, were particularly successful. One of the reasons was that they were near port facilities. But perhaps even more importantly, they had access to financial powerhouse Hong Kong, with its banks and talented entrepreneurs. While, of course, having access to hundreds of millions of workers from inland provinces.
In fact, Shenzhen became a model for the China that was about to develop. It was the first city to abolish the food coupon system, thus allowing residents to buy food with their own money. And residents were soon allowed to lease their own land…
…Another important part of Deng’s reforms was allowing farmers to grow whatever they pleased after meeting some quota. They could then sell any surplus in newly established markets. This unleashed immense rural income growth of 12% per year throughout the 1980s.
A similar system was later introduced to state-owned enterprises as well. They were now allowed to retain profits, either for reinvestment or pay them out as bonuses to employees. Managers suddenly realized they had incentives to increase revenues and profits, and some became wealthy…
…But beneath the surface, discontent was growing. Students were devouring books brought in from overseas. They were clamoring not only for economic gains but also for political reforms. By 1987, Beijing students regularly held marches from the university districts to Tian’anmen Square to protect against political restrictions…
…The crackdown on the student demonstrations in Beijing in June 1989 led to a significant political shift. For two years after the massacre, the country closed off, and dissidents were hunted down and jailed. Anyone who participated in the protests was either disappeared, jailed, demoted or unable to attend university or get a good job.
After the student protests, the Communist Party shifted its strategy to maintaining control. It upped its propaganda efforts, conveying that if the party were to collapse, China would end up in total anarchy.
In the aftermath of Tian’anmen, a communication system was established that improved the party’s control over the provinces. Tax collection and audits were tightened, and a criminal detection and surveillance system was developed…
…One of Deng’s buzzwords during this era was “to get rich is glorious” (致富光荣). You no longer had to be ashamed of pursuing wealth; it was promoted from the top down.
The Communist Party bet that as long as people felt their livelihoods improved, they would not rock the boat. The restive students who protested at Tian’anmen Square would now focus on economic opportunity rather than spiritual dissatisfaction.
After his come-back in the early 1990s, Deng picked out young talent Zhu Rongji to push for further reforms. In a long list of achievements, Zhu Rongji managed to:
- Cut the government bureaucracy in half
- Privatize housing
- Sell off 2/3 of the companies in the state sector
- Unify the dual currencies used prior to 1994
- Introduce a nationwide tax system
- Take control of the appointment of all provincial-level governors…
…After the reforms of the 1990s, China’s economic growth really took off. Exporters in China’s coastal regions benefitted from the country’s admission into the WTO, and Chinese returnees started businesses left and right…
…It was also during the 2000s that the property boom really kicked into high gear. In the late 1990s, Zhu Rongji instituted reforms that allowed state-owned enterprises to sell worker housing back to tenants for a pittance. As prices rose throughout the 2000s, tenants now held significant household equity, which they could then leverage to buy new, even fancier, commodity housing.
A change in the tax structure also incentivized local governments to promote construction. In the mid-1990s, the central government established its own offices to collect taxes directly. In other words, local governments had less ability to raise taxes themselves, instead relying on remittances from the central government. Local governments thus became cash-poor.
To fund their spending programs, they instead set up local government financing vehicles (LGFVs), which used land as collateral for borrowing. And since they were government entities, they were seen as quasi-sovereign borrowers enjoying full access to loans from state banks. Over time, the number of LGFVs grew to over 10,000. They operate urban infrastructure, subway systems, water and gas utilities, etc. Some of them are profitable, but many of them are not…
…The privatization of China’s housing market, which provided collateral for new loans, created one of the biggest credit booms the world has ever seen. Later on, in just five years, more credit was created than the entire value of the US banking system…
…After the Great Financial Crisis of 2008, the Communist Party leadership unleashed a CNY 4 trillion stimulus program that brought forward demand for infrastructure and spending targets.
At this point, it was already becoming clear that the capital stock for infrastructure was starting to exceed those of most other developing or even developed economies. By 2012, China had 8x the length of highways per unit of GDP as that of Japan. At the time, more than 70% of China’s airports were failing to cover their own costs, even though such costs tend to be modest…
…Meanwhile, with the state pushing for big stimulus packages, the government increasingly directed economic resources. Concepts such as “advance of the state, retreat of the private sector” (国进民退) became more common, reflecting a shift in the economy away from private sector entrepreneurship…
…And indeed, with the emergence of Xi Jinping, the state has started to reassert control. State companies are now receiving most of the loans from China’s banks. State media is now talking of “national rejuvenation”, trying to unite the country around nationalist sentiment and acceptance of a “moderately prosperous lifestyle” (小康社会). This is a clear break from the era of Deng Xiaoping’s reforms when getting rich was perhaps the greatest virtue in life…
…Further, she believes that a Russia-Iran-China bloc is currently being formed and that China’s financial system could serve as a bedrock for trade within the bloc:
“If, however, China were someday to shrink its network of trading partners to other dictatorships like Russia and North Korea, its dedicated financial system could become the principal one used for trade among those nations.”
In other words, Anne believes that China is withdrawing from its informal pact with Western nations about open trade, with the experiment in Western-style capitalism that commenced in 1979 over. The Chinese economy is now morphing into a different system, one where the state reigns supreme and will become an influential partner in a new trading bloc formed by China’s current geopolitical allies.
2. 20 Lessons From 20 Years of Managing Money – Ben Carlson
1. Experiences shape your perception of risk. Your ability and need to take risk should be based on your stage in life, time horizon, financial circumstances and goals.
But your desire to take risk often trumps all that, depending on your life experiences. If you worked at Enron or Lehman Brothers or AIG or invested with Madoff, your appetite for risk will be forever altered.
And that’s OK as long as you plan accordingly.
2. Intelligence doesn’t guarantee investment success. Warren Buffett once wrote, “Investing is not a game where the guy with the 160 IQ beats the guy with the 130 IQ. Once you have ordinary intelligence, what you need is the temperament to control the urges that get other people into trouble in investing.”
I’ve met so many highly educated individuals who are terrible investors. They can’t control their emotions because their academic pedigree makes them overconfident in their abilities.
Emotional intelligence is the true sign of investment smarts.
3. No one lives life in the long-term. Long-term returns are the only ones that matter but you have to survive a series of short-terms to get there.
The good strategy you can stick with in those short-terms is preferable to the perfect strategy you can’t stick with…
…9. The biggest risks are always the same…yet different. The next risk is rarely the same as the last risk because every market environment is different.
On the other hand, the biggest mistakes investors make are often the same — timing the market, recency bias, being fearful when others are fearful and greedy when others are greedy and investing in the latest fads.
It’s always a different market but human nature is the constant…
…16. Experience is not the same as expertise. Just because you’ve been doing something for a long time doesn’t mean you’re an expert.
I know plenty of experienced investors who are constantly fighting the last war to their own detriment.
How many people who “called” the 2008 crash completely missed the ensuing bull market? All of them?
How many investment legends turn into permabears the older they get becasue they fail to recognize how markets have changed over time?
Loads of investment professionals who have been in the business for many years make the same mistakes over and over again…
…18. There is a big difference between rich and wealthy. Lots of rich people are miserable. These people are not wealthy, regardless of how much money they have.
There are plenty of people who wouldn’t be considered rich based on the size of their net worth who are wealthy beyond imagination because of their family, friends and general contentment with what they have.
19. Optimism should be your default. It saddens me to see an increasing number of cynical and pessimistic people every year.
I understand the world can be an unforgiving place and things will never be perfect but investing is a game where the optimists win.
3. 8 Google Employees Invented Modern AI. Here’s the Inside Story – Steven Levy
EIGHT NAMES ARE listed as authors on “Attention Is All You Need,” a scientific paper written in the spring of 2017. They were all Google researchers, though by then one had left the company…
…Recurrent neural networks struggled to parse longer chunks of text. Take a passage like Joe is a baseball player, and after a good breakfast he went to the park and got two hits. To make sense of “two hits,” a language model has to remember the part about baseball. In human terms, it has to be paying attention. The accepted fix was something called “long short-term memory” (LSTM), an innovation that allowed language models to process bigger and more complex sequences of text. But the computer still handled those sequences strictly sequentially—word by tedious word—and missed out on context clues that might appear later in a passage. “The methods we were applying were basically Band-Aids,” Uszkoreit says. “We could not get the right stuff to really work at scale.”
Around 2014, he began to concoct a different approach that he referred to as self-attention. This kind of network can translate a word by referencing any other part of a passage. Those other parts can clarify a word’s intent and help the system produce a good translation. “It actually considers everything and gives you an efficient way of looking at many inputs at the same time and then taking something out in a pretty selective way,” he says. Though AI scientists are careful not to confuse the metaphor of neural networks with the way the biological brain actually works, Uszkoreit does seem to believe that self-attention is somewhat similar to the way humans process language.
Uszkoreit thought a self-attention model could potentially be faster and more effective than recurrent neural nets. The way it handles information was also perfectly suited to the powerful parallel processing chips that were being produced en masse to support the machine learning boom. Instead of using a linear approach (look at every word in sequence), it takes a more parallel one (look at a bunch of them together). If done properly, Uszkoreit suspected, you could use self-attention exclusively to get better results…
…The transformer crew set about building a self-attention model to translate text from one language to another. They measured its performance using a benchmark called BLEU, which compares a machine’s output to the work of a human translator. From the start, their new model did well. “We had gone from no proof of concept to having something that was at least on par with the best alternative approaches to LSTMs by that time,” Uszkoreit says. But compared to long short-term memory, “it wasn’t better.”
They had reached a plateau—until one day in 2017, when Noam Shazeer heard about their project, by accident. Shazeer was a veteran Googler—he’d joined the company in 2000—and an in-house legend, starting with his work on the company’s early ad system. Shazeer had been working on deep learning for five years and recently had become interested in large language models. But these models were nowhere close to producing the fluid conversations that he believed were possible.
As Shazeer recalls it, he was walking down a corridor in Building 1965 and passing Kaiser’s workspace. He found himself listening to a spirited conversation. “I remember Ashish was talking about the idea of using self-attention, and Niki was very excited about it. I’m like, wow, that sounds like a great idea. This looks like a fun, smart group of people doing something promising.” Shazeer found the existing recurrent neural networks “irritating” and thought: “Let’s go replace them!”
Shazeer’s joining the group was critical. “These theoretical or intuitive mechanisms, like self-attention, always require very careful implementation, often by a small number of experienced ‘magicians,’ to even show any signs of life,” says Uszkoreit. Shazeer began to work his sorcery right away. He decided to write his own version of the transformer team’s code. “I took the basic idea and made the thing up myself,” he says. Occasionally he asked Kaiser questions, but mostly, he says, he “just acted on it for a while and came back and said, ‘Look, it works.’” Using what team members would later describe with words like “magic” and “alchemy” and “bells and whistles,” he had taken the system to a new level.
“That kicked off a sprint,” says Gomez. They were motivated, and they also wanted to hit an upcoming deadline—May 19, the filing date for papers to be presented at the biggest AI event of the year, the Neural Information Processing Systems conference in December. As what passes for winter in Silicon Valley shifted to spring, the pace of the experiments picked up. They tested two models of transformers: one that was produced with 12 hours of training and a more powerful version called Big that was trained over three and a half days. They set them to work on English-to-German translation.
The basic model outperformed all competitors—and Big earned a BLEU score that decisively shattered previous records while also being more computationally efficient. “We had done it in less time than anyone out there,” Parmar says. “And that was only the beginning, because the number kept improving.”…
…TRANSFORMERS DID NOT instantly take over the world, or even Google. Kaiser recalls that around the time of the paper’s publication, Shazeer proposed to Google executives that the company abandon the entire search index and train a huge network with transformers—basically to transform how Google organizes information. At that point, even Kaiser considered the idea ridiculous. Now the conventional wisdom is that it’s a matter of time.
A startup called OpenAI was much faster to pounce. Soon after the paper was published, OpenAI’s chief researcher, Ilya Sutskever—who had known the transformer team during his time at Google—suggested that one of its scientists, Alec Radford, work on the idea. The results were the first GPT products. As OpenAI CEO Sam Altman told me last year, “When the transformer paper came out, I don’t think anyone at Google realized what it meant.”
The picture internally is more complicated. “It was pretty evident to us that transformers could do really magical things,” says Uszkoreit. “Now, you may ask the question, why wasn’t there ChatGPT by Google back in 2018? Realistically, we could have had GPT-3 or even 3.5 probably in 2019, maybe 2020. The big question isn’t, did they see it? The question is, why didn’t we do anything with the fact that we had seen it? The answer is tricky.”
Many tech critics point to Google’s transition from an innovation-centered playground to a bottom-line-focused bureaucracy. As Gomez told the Financial Times, “They weren’t modernizing. They weren’t adopting this tech.” But that would have taken a lot of daring for a giant company whose technology led the industry and reaped huge profits for decades. Google did begin to integrate transformers into products in 2018, starting with its translation tool. Also that year, it introduced a new transformer-based language model called BERT, which it started to apply to search the year after.
But these under-the-hood changes seem timid compared to OpenAI’s quantum leap and Microsoft’s bold integration of transformer-based systems into its product line. When I asked CEO Sundar Pichai last year why his company wasn’t first to launch a large language model like ChatGPT, he argued that in this case Google found it advantageous to let others lead. “It’s not fully clear to me that it might have worked out as well. The fact is, we can do more after people had seen how it works,” he said…
…Does Google miss these escapees? Of course, in addition to others who have migrated from the company to new AI startups. (Pichai reminded me, when I asked him about the transformer departures, that industry darling OpenAI also has seen defections: “The AI area is very, very dynamic,” he said.) But Google can boast that it created an environment that supported the pursuit of unconventional ideas. “In a lot of ways Google has been way ahead—they invested in the right minds and created the environment where we could explore and push the envelope,” Parmar says. “It’s not crazy that it took time to adopt it. Google had so much more at stake.”
Without that environment: no transformer. Not only were the authors all Google employees, they also worked out of the same offices. Hallway encounters and overheard lunch conversations led to big moments. The group is also culturally diverse. Six of the eight authors were born outside the United States; the other two are children of two green-card-carrying Germans who were temporarily in California and a first-generation American whose family had fled persecution, respectively.
4. In Depth: Local Governments Struggle to Tackle Mountain of Hidden Debt – Cheng Siwei, Wang Juanjuan, Zhang Yuzhe, Ding Feng and Zhang Yukun
The central government has been trying to address the problem of LGFV debt for years, mainly through piecemeal measures that had limited success. But in July, the Politburo vowed to formulate and implement a comprehensive strategy to resolve local government hidden debts.
These off-the-books liabilities, which include LGFV bonds with implicit official backing, have accumulated over the years to around 30 trillion to 70 trillion yuan according to some estimates, and become a threat to the country’s fiscal and financial stability and sustainability.
One of the main instruments being used to repay hidden debt in this round of debt resolution is special refinancing bonds — on-balance-sheet local government bonds whose proceeds are used to repay outstanding hidden debt. Issuance has stepped up significantly since early October after the Ministry of Finance launched a special refinancing bond swap program.
From October to December, almost all provincial-level regions on the Chinese mainland issued these special refinancing bonds, raising nearly 1.4 trillion yuan to repay hidden borrowings, according to calculations by analysts at Tianfeng Securities Co. Ltd. The regions include heavily indebted Guizhou province, which topped the list with issuance of 226.4 billion yuan.
Many regions have announced plans to issue more such bonds in February and March, with planned issuances totaling more than 100 billion yuan, the Tianfeng analysts wrote in a January report.
The campaign to resolve hidden debt has tightened rules for new debt issuance and cut some localities off from their previous financing channels, depriving them of resources to pay interest on hidden debt. The proceeds of special refinancing bonds cannot be used to make interest payments.
“The core issue now is that we can’t make our interest payments,” a source who works for an economic development zone in West China told Caixin, noting that without new financing, the fiscal revenue of the region can only sustain government agencies’ day-to-day operations and preferential policies for attracting businesses. He said his local government has stopped making all other payments, including those to project developers, to ensure it can meet interest payments on outstanding LGFV debt…
…The renewed push to bring hidden debt onto the books and restructure or swap LGFV debt, however, has reinforced the belief that the central government won’t allow LGFVs to default on their bonds, reviving investor sentiment. That’s led to a surge in demand for LGFV bonds over the past few months, even as the central government has repeatedly highlighted the need to stem any renewed buildup in hidden debt…
…Although LGFV bonds are back in hot demand, tightened oversight has made it more difficult for some vehicles, especially those with heavy debt burdens, to continue issuing new debt. This has curbed growth in hidden debt to some extent, but it has added to default risks of some LGFV bonds as there is less money available to make the interest repayments.
The central government ordered provincial officials to compile a list of LGFVs owned by local authorities in their jurisdictions…
…Obtaining new bank loans has become much harder for LGFVs on the list, as banks heed the central government’s instruction to prevent new LGFV debt.
Regarding existing LGFV debt, the State Council in September issued guidance that banks, among the most important creditors of LGFVs, should become involved in debt resolution in 12 provincial-level regions with high government leverage, which include Liaoning, Heilongjiang, and Jilin, the three rustbelt provinces in Northeast China. The guidance set out that banks should focus on restructuring or swapping existing loans, high-interest non-standard debt, and other types of borrowing.
5. Conviction and Quality – Josh Tarasoff
Conviction is no doubt the foundation of long-term business ownership. How is it formed? What is it like to have it? Why does it falter? In my experience there are two distinct kinds of conviction. Explicit conviction, as I call it, comes from having figured something out. It entails a useful prediction, like “our ETA is 5pm” or “majoring in economics will lead to better career prospects than majoring in philosophy.” There is an underlying logic to it, which can be explained and used to persuade. Implicit conviction, on the other hand, is exemplified by the trust one might have in a family member, a dear friend, a close colleague, to do the right thing, to get the job done, to come through. It is felt as opposed to believed. This kind of conviction doesn’t make predictions so much as align with what is good. It doesn’t theorize about goodness but rather knows it when it sees it…
…In the context of investing, one might develop the thesis that a particular company can capture X% market share, generate Y dollars in annual revenue, achieve Z% operating margins, and therefore has an intrinsic value within a certain range. One might have high confidence because of the presence of competitive advantages and management with a very good track record. One would have a range of expected returns from owning the shares over time. All of this would fall into the explicit category.
Sooner or later, the investment would encounter a confounding surprise. Perhaps execution turns choppy, a new competitive vector emerges out of nowhere, an exogenous crisis turns the world upside down, etc. Old projections are now in doubt, previous plans and strategies are being reworked, everything is less fun. These things are actually happening all the time— something explicit conviction has a way of tuning out! Only genuine and well-placed implicit conviction, a qualitative knowing that the company will do what it needs to and ought to do, is equipped to ably traverse this kind of terrain. Unlike analysis-based explicit conviction, implicit conviction comes from something deeper than the cause and effect we perceive in the unfolding of events—it is both analytical and, crucially, intuitive (about which more later)…
…While in everyday life implicit conviction arises naturally, in the context of investing I can’t help but feel it is somewhat alien. In part, this is because few companies are truly deserving. Even so, I suspect that implicit conviction is proffered by investors even less than it ought to be. It isn’t difficult to see why the investment industry is inhospitable to implicit conviction, and why its partner rules the roost. Implicit conviction forms of its own accord and cannot be planned. It defies quantification, eliciting the charge of being too “fuzzy” to matter. Nor can it be fully captured in words. Implicit conviction is impossible to transmit from analyst to portfolio manager or from portfolio manager to client, which is highly inconvenient for the business of managing money. It is primarily personal. It is quiet. By contrast, the appeal of the explicit is clear. Explicit conviction furnishes the comfort of knowability and modeled outcomes. It projects the legitimacy of diligence and precision. It is thought to be reliably manufactured via “repeatable process.” It is clever and self assured….
…Nonetheless, because literal communication necessitates choosing a word, I will use “Quality” (capitalized to distinguish it from the ordinary sense of the term) to indicate the deeper-something on which implicit conviction is based. Using “Quality” in this way is consistent with my prior writing and pays homage to the work of Robert Pirsig, which was a formative influence.
Analysis plays an important but limited role in detecting Quality. For example, the following is a selection of (neither necessary nor sufficient) indicators that I have found to be suggestive of Quality in companies:
- “Wow” customer experiences
- Mission to solve an important problem
- Domain mastery (the best at what they do)
- First-principles-based thinking and invention
- Unlimited ambition combined with no-nonsense realism
- Overcapitalized balance sheet
- Founder mentality (life’s work).
While carefully looking for indicators like these is helpful, I think it would be a misstep to attempt to systematize the search, constructing a Grand Unified Theory of Quality and attendant comprehensive processes for finding and evaluating it. Quality emerges from the complexity of the system in action; it is in the how rather than the what. Thus, when Quality is broken down into parts and analyzed, its essence is lost. This explains why analysis alone has trouble discerning the authentic from the artificial. Moreover, Quality frozen in a theory or process cannot be recognized in sufficiently new contexts, such as in a company that is novel to one’s experience or in the same company as it evolves (they always do!).
So where does that leave us? With intuition. Well-honed intuition does what analysis cannot by perceiving Quality directly, as opposed to through an intellectual process. What I suspect is happening in the direct perception of Quality is subconscious pattern recognition, based upon a dynamic, holistic experience of the thing in question. Of course, the ability to intuitively recognize patterns in a specific domain must be earned through experience and feedback; indeed, I have found that the value of my own intuition has grown (starting at zero) over many years. Interestingly, I also find that experiencing Quality in any one domain (e.g., music or meditation, to use examples that are dear to me) can be helpful for recognizing it in other domains (including business) because Quality’s nature is universal, even as its manifestations are necessarily particular.
Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. We currently have a vested interest in Alphabet (parent of Google) and Microsoft. Holdings are subject to change at any time.