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31
maj

China vs. USA: personal consumption

Autor: Jarosław Jamka

The American consumer consumption is as much as 68% of GDP, while in the case of China it is only 37%. For many reasons, China is unlikely to be able to even come close to the structure of American GDP, because the Chinese consumer would ultimately have to consume almost twice as much as today.

Figure 1 shows a comparison of the GDP of China and the USA according to the share of individual components.

Americans use a different methodology than the rest of the world and instead of Gross Fixed Capital Formation we have Gross Private Domestic Investment. One of the main differences concerns Gross Government Investment, which in the American approach is in the government spending component (specifically “Government Consumption Expenditures and Gross Investment”). After moving Gross Government Investment to GPDI, we get Figure 2.

But the key message is China’s different GDP „model”, where consumer spending is relatively small. Additionally, when we look at Chinese retail sales… they changed the trend after 2020… see Figure 3 and Figure 4.

Figure 4 shows Chinese retail sales as a rolling sum of last 12-month (TTM). Clearly, after 2020, there has been a “change in trend” and the Chinese consumer is now “spending less” than before Covid. The Chinese consumer is certainly also influenced by the falling house prices (and stocks on the capital markets). And in the USA, stock and house prices are rising, and the consumer himself is doing quite well in terms of his expenditures… see Figures 5 and 6.

Key takeaway: to sustain economic growth, China must rely mainly on exports and investments (Gross Capital Formation), and exports seem to be the most reasonable option… including the probable devaluation of the Chinese yuan.

Niniejszy materiał jest informacją reklamową. Ma charakter edukacyjno-informacyjny i stanowi wyraz własnych ocen, przemyśleń i opinii autora. Niniejszy materiał służy jedynie celom informacyjnym i nie stanowi oferty, w tym oferty w rozumieniu art. 66 oraz zaproszenia do zawarcia umowy w rozumieniu art. 71 ustawy z dnia 23 kwietnia 1964 r. – Kodeks cywilny (t.j. Dz. U. z 2020 r. poz. 1740, 2320), ani oferty publicznej w rozumieniu art. 3 ustawy z dnia 29 lipca 2005 r. o ofercie publicznej i warunkach wprowadzania instrumentów finansowych do zorganizowanego systemu obrotu oraz o spółkach publicznych (t.j. Dz. U. z 2022 r. poz. 2554, z 2023 r. poz. 825, 1723) czy też oferty publicznej w rozumieniu art 2 lit d) Rozporządzenia Parlamentu Europejskiego i Rady (UE) 2017/1129 z dnia 14 czerwca 2017 r. w sprawie prospektu, który ma być publikowany w związku z ofertą publiczną papierów wartościowych lub dopuszczeniem ich do obrotu na rynku regulowanym oraz uchylenia dyrektywy 2003/71/WE (Dz. Urz. UE L 168 z 30.06.2017, str. 12); Niniejszy materiał nie stanowi także rekomendacji, zaproszenia, ani usług doradztwa. prawnego, podatkowego, finansowego lub inwestycyjnego, związanego z inwestowaniem w jakiekolwiek papiery wartościowe. Materiał ten nie może stanowić podstawy do podjęcia decyzji o dokonaniu jakiejkolwiek inwestycji w papiery wartościowe czy instrumenty finansowe. Informacje zamieszczone w materiale nie stanowią rekomendacji w rozumieniu przepisów Rozporządzenia Parlamentu Europejskiego i Rady (UE) NR 596/2014 z dnia 16 kwietnia 2014 r. w sprawie nadużyć na rynku (rozporządzenie w sprawie nadużyć na rynku) oraz uchylające dyrektywę 2003/6/ WE Parlamentu Europejskiego i Rady i dyrektywy Komisji 2003/124/WE, 2003/125/WE i 2004/72/ WE. (Dz. U UE L 173/1 z dnia 12.06.20114). NDM S.A., nie ponosi odpowiedzialności za prawdziwość, rzetelność i kompletność oraz aktualność danych i informacji zamieszczonych w niniejszej prezentacji. NDM S.A. nie ponosi również jakiejkolwiek odpowiedzialności za szkody wynikające z wykorzystania niniejszego materiału, informacji i danych w nim zawartych. Zawartość materiału została przygotowana na podstawie opracowań sporządzonych zgodnie z najlepszą wiedzą NDM S.A. oraz przy wykorzystaniu informacji i danych publicznie dostępnych, chyba, że wyraźnie wskazano inne źródło pochodzenia danych.

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Komentarz poranny 31.05.2024

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31
maj

30
maj

Another risk-off?

Autor: Jarosław Jamka

Do current markets look like we’re going to have another risk-off correction?

Certainly yes… the further you go from Mag7, the more serious the risk-off seems to be … and Mag7 is practically at the new all-time high (ATH on May 28, and 0.02% below ATH on May 29) – see Figure 1.

Mag7 in May alone (MtD) increased by 13.01% (until May 29).

This would be the third risk-off correction since July 2023, although currently the S&P500 is only down by 1.02% from the peak of May 21 this year. But the Russell 2000 is already down 3.47% (since May 15 this year). The Russell 2000 is only +3.15% in May (MtD).

Table 1 shows more details.

Again rising bond yields are generally associated with the beginning of a correction in the stock market – see Figure 2.

During the S&P500 correction in 2023 – 10Y UST yield increased by 87 bps.

During the S&P500 correction in April 2024 – 10Y UST yield increased by 42 bps.

During the current correction – starting from May 21 – 10Y UST yield have already increased by 20.2 bps – see Figure 2, black arrows during „risk-off” periods.

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Komentarz poranny 29.05.2024

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28
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Komentarz poranny 28.05.2024

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Komentarz poranny 27.05.2024

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24
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Komentarz poranny 24.05.2024

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Nvidia Q1 Earnings Review

Autor: Jarosław Jamka

Nvidia once again beat earnings expectations in fine style (see Figure 1).

The current Wall Street consensus for future revenue, two days after the earnings release is up approximately 5-10% compared to the consensus immediately before the May 22 earnings release – see Figure 2.

But the most important issue for investors remains the question of further revenue growth. According to Market Platform kind of revenues, the company’s sales are split in 5 segments, but as much as 87% of sales are from the Data Centers segment. Interestingly, Data Centers segment is responsible for 97% of YoY sales growth, and for 106% of quarter-to-quarter sales growth. See Figure 3 and 4.

Approximately 45% of sales in the Data Centers segment, or approximately $10.2 billion per quarter, are sales to 4 companies: Amazon, Alphabet, Meta and Microsoft (so-called hyperscalers).

CFO commentary:

“Data Center revenue of $22.6 billion was a record, up 23% sequentially and up 427% year-on-year, driven by continued strong demand for the NVIDIA Hopper GPU computing platform. (…) Strong sequential data center growth was driven by all customer types, led by enterprise and consumer internet companies. Large cloud providers continue to drive strong growth as they deploy and ramp NVIDIA AI infrastructure at scale and represented the mid-40s as a percentage of our Data Center revenue.”

In a sense, we are dealing with a virtuous cycle when more advanced AI models need more computing power, and only better models can win this race. In this respect, the product cycle only drives Nvidia’s sales when subsequent chip versions are even faster. The second trend is to expand the market beyond Hyperscalers, including Sovereign AI, but also other industries such as carmakers, biotechnology and health-care companies.

Colette Kress on more demand for AI compute:

„As generative AI makes its way into more consumer Internet applications, we expect to see continued growth opportunities as inference scales both with model complexity as well as with the number of users and number of queries per user, driving much more demand for AI compute. In our trailing four quarters, we estimate that inference drove about 40% of our Data Center revenue. Both training and inference are growing significantly.”

On Sovereign AI demand:

“Data Center revenue continues to diversify as countries around the world invest in Sovereign AI. Sovereign AI refers to a nation’s capabilities to produce artificial intelligence using its own infrastructure, data, workforce and business networks. Nations are building up domestic computing capacity through various models. Some are procuring and operating Sovereign AI clouds in collaboration with state-owned telecommunication providers or utilities. Others are sponsoring local cloud partners to provide a shared AI computing platform for public and private sector use.”

“we believe Sovereign AI revenue can approach the high single-digit billions this year. The importance of AI has caught the attention of every nation”.

Jensen Huang on demand on AI training and inference (Training is the process of teaching an AI model how to perform a given task. Inference is the AI model in action, producing predictions or conclusions without human intervention):

“Strong and accelerated demand — accelerating demand for generative AI training and inference on Hopper platform propels our Data Center growth. Training continues to scale as models learn to be multimodal, understanding text, speech, images, video and 3D and learn to reason and plan.”

“The demand for GPUs in all the data centers is incredible. We’re racing every single day. And the reason for that is because applications like ChatGPT and GPT-4o, and now it’s going to be multi-modality and Gemini and its ramp and Anthropic and all of the work that’s being done at all the CSPs are consuming every GPU that’s out there.”

On startups demand:

“There’s also a long line of generative AI startups, some 15,000, 20,000 startups that in all different fields from multimedia to digital characters”

“the demand, I think, is really, really high and it outstrips our supply. (…) Longer term, we’re completely redesigning how computers work. And this is a platform shift. Of course, it’s been compared to other platform shifts in the past. But time will clearly tell that this is much, much more profound than previous platform shifts. And the reason for that is because the computer is no longer an instruction-driven only computer. It’s an intention-understanding computer.”

Nvidia is quickly introducing new faster products, for example in the field of Data Center GPUs: H100 Tensor Core GPU, then H200 Tensor Core GPU, and the next one is GB200 NVL72.

For example, Tesla has already purchased 35,000 H100s, Jensen Huang: „Enterprises drove strong sequential growth in Data Center this quarter. We supported Tesla’s expansion of their training AI cluster to 35,000 H100 GPUs. Their use of NVIDIA AI infrastructure paved the way for the breakthrough performance of FSD Version 12, their latest autonomous driving software based on Vision.”

The H200 boosts inference speed by up to 2X compared to H100 GPUs when handling LLMs like Llama2. H200 reduces also TCO (total cost of ownership) by 50% and energy use by 50% – see Figure 5.