$IBM (+1,55%)

NEW YORK / MUNICH (IT BOLTWISE) - IBM recently released its quarterly results, which failed to meet market expectations despite a slight increase in revenue of nearly 1% to $14.5 billion.

While earnings per share exceeded analysts' estimates, concerns about economic uncertainty and government cost-cutting remain.

IBM, a giant in the technology industry, has presented its latest quarterly results, which failed to meet investor expectations despite a slight increase in revenue of almost 1% to 14.5 billion US dollars. Earnings per share, adjusted for certain items, amounted to 1.60 US dollars, exceeding the average analysts' estimates. Nevertheless, the company's shares fell the most in a year on Thursday due to concerns about economic uncertainty and the potential impact of US government cost cuts.

Economic uncertainties, exacerbated by global trade disputes and geopolitical tensions, could hurt IBM's business prospects. In particular, the impact of tariffs and trade restrictions on supply chains and production costs are of concern to many companies, including IBM. These factors could significantly impact the company's ability to maintain its margins and continue to grow.

Another aspect that worries investors is the cost cuts announced by the US government. These could impact spending on IT services and products, which in turn could dampen demand for IBM's solutions. In an environment where companies are increasingly focusing on efficiency and cost savings, this could further intensify competition in the technology market.

From a technology perspective, IBM remains a pioneer in the development of AI-powered solutions and cloud services. The company continues to invest in improving its AI platforms and integrating cloud technologies to provide its clients with innovative and scalable solutions. These investments are crucial to remain competitive in the highly competitive enterprise software market.

Competition in the enterprise software space is intense, with strong competitors such as Microsoft and Amazon also investing in AI and cloud services. These companies offer similar solutions and are trying to gain market share by continuously improving and expanding their products. IBM must therefore continue to strengthen its innovative power and market strategy in order to maintain its position.

Looking ahead, IBM's focus on AI and cloud technologies could help the company to tap into new growth opportunities. Demand for AI-powered solutions and cloud services is growing steadily as organizations around the world drive their digital transformation strategies. IBM could benefit from this development by leveraging its technological strengths and adapting to changing market conditions.

Actually, I didn't want to buy anything extra at the moment (only monthly savings installments). But now I've taken advantage of today's low to buy a little at $IBM (+1,55%) and $QCOM (+1,46%) (10 shares each). No idea whether it was smart...

SPICK LETTEL

- $IONQ -- The market leader in precision quantum computing uses ion trap technology to deliver scalable, high-precision systems. IonQ is already solving real-world problems, enabling $AMZN and $MSFT to tackle challenges in logistics, AI and materials science.

- $NVDA (+4,72%) -- A cornerstone of quantum innovation, NVIDIA's CUDA Quantum platform enables developers to simulate quantum algorithms on GPUs, bridging the gap between quantum theory and real-world application across industries.

- $RGTI -- Rigetti focuses on superconducting qubits and aims to compete with industry leaders, but faces scalability and reliability challenges. Its Ankaa-3 system is promising, but the company needs to solve critical problems to remain competitive.

- $QBTS (+0,77%) -- D-Wave specializes in quantum annealing and is excellent for solving optimization problems, but lacks the broader capabilities of general-purpose quantum systems. Despite decades of experience in this field, its commercial viability remains limited.

- $ARQQ -- As a leader in quantum-safe encryption, Arqit's QuantumCloud provides practical solutions to protect sensitive data from quantum-based threats, making it a cornerstone of innovation in cybersecurity.

- $GOOG (-1,83%) -- Google Quantum AI, powered by its Willow processor, is advancing superconducting systems to achieve quantum superiority and drive breakthroughs in material simulation and AI optimization.

-$IBM (+1,55%) -- Delivering accessible quantum systems to the enterprise through the Qiskit platform and cloud-based hardware. IBM is focused on scaling fault-tolerant systems, delivering practical tools today while working to develop cutting-edge processors such as Eagle and Condor.

- $HON (-0,22%) -- Utilizing Trapped Ion technology, Honeywell delivers unmatched precision for real-world applications in aerospace and logistics. The partnership with Cambridge Quantum positions the company as a leading provider of quantum solutions for the enterprise.

Last week, Saturday's episode of "All About Stocks" [1] featured an excerpt from the study "Do Stocks Outperform Treasury Bills?" [2], which sounded quite interesting and the contents of which I subsequently read again in more detail.

The essence of the study is that only a small proportion of companies are responsible for the majority of returns.

It therefore serves as a reminder that it is probably the best choice for the "average investor" to invest in a well-diversified ETF.

Here is a brief presentation of the results.

Hendrik Bessembinder from the W.P. Carey School of Business at Arizona State University has investigated which stocks really drive the market in the long term.

According to the study, since 1926 only 4% of all stocks have generated the total net profit of the US stock market [2].

The other 96% of stocks in total have only generated as much return as safe one-month US government bonds or even less [2]. The average monthly return here was 0.37% (which is roughly equivalent to an annual return of 4.53% when compound interest is taken into account).

Almost more interesting is the following:

The top 50 companies were responsible for 39.29% of the total value creation of the US stock market and.

... the top 90 stocks (only 0.36% of all companies) even generated more than 50% of the total market profit [2].

The 4% mentioned still represent just under 1,092 of over 25,000 companies. It doesn't seem so unrealistic to find them.

The only problem is:

The best stocks are usually only recognized in retrospect

• Apple, Mircosoft and Amazon were still small, unknown companies 30 years ago.
• Many investors would have bet on "safe" large companies back then, but some of them (e.g. Kodak or Nokia) are no longer among the top performers today.
• We will only know today's 4% winners in the future.

Even professionals often fail

• Active fund managers try to do just that: find the best stocks and avoid the bad ones.
• But most fund managers do not beat the market over the long term.

Timing is often extremely difficult

• Many of the best stocks looked like losers in the meantime.
• For example, the Amazon share fell $AMZN (+0,25%) after the dotcom crash by almost - 90 %, even after mid-2021 Amazon lost almost - 50 %, would you have held it?

More than half of all stocks have even generated negative returns over their entire lifetime [2].

That means: The average stock return we all know is not generated by the "broad" market, but only by these 4% of stocks.

Further results of the study:

Value creation on the stock market is extremely unevenly distributed.

• ExxonMobil $XOM (+0,12%) alone generated the most shareholder value creation with 1 trillion dollars and was responsible for 2.88% of the total market development from 1926 to 2016 [2].
• Apple $AAPL (+0,28%) ($745.7 billion), Microsoft $MSFT (+1,32%) ($629.8 billion), General Electric $GE (+0,58%) ($608.1 billion) and IBM $IBM (+1,55%) ($520.2 billion) are among the top 5 companies that together account for over 10% of total stock market value creation [2].

The question now for us as investors is:

Do I really think I can buy these 4% winning stocks early can find them early?

... and at the same time can I at least stay away from the biggest losers from the remaining 96%?

... or do I prefer to stick with John Bogle the founder of Vanguard, who gave the following famous quote:

🧠 "Don't try to find the needle in the haystack. Just buy the whole haystack."

The haystack is in that sense an ETF:

• ETFs are a self-optimizing system in which well-performing sectors and companies are overweighted and weak companies gradually lose importance.
• You don't have to find the 4% yourself, the ETF does it for you.

Conclusion

Yes, it is theoretically possible to find the 4% yourself, to time it correctly and to hold it, as well as to stay away from the biggest losers of the 96% in the long term.

The question is: do you want to bet your portfolio on it, or would you rather make sure you automatically profit from the 4%.

💡For most investors, a simple ETF investment as a "core" is therefore probably the best choice.

Thank you for reading 🤝

__________

P.S. The study was published in 2017 and last revised in 2018.

With regard to a more recent analysis, which refers specifically to the last few years, the study does not contain separate results for shorter periods. However, it does mention that this effect has been even more pronounced in recent decades, particularly since the 1980s. To get a detailed current analysis, one would have to search more recent research.

__________

Source:

[1] https://open.spotify.com/episode/7ik1W0e9zq7TBYacPW0eVl?si=Sw2Mu0XSSH2SQFp5cHtpLQ

[2]

published 01/2017, revised 06/2018
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2900447&utm_source=chatgpt.com

#Aktien
#ETF
#StockPicking

Graphic: AI generated

The rapid development of AI has triggered a veritable boom in data centers. Companies such as OpenAI and DeepSeek are driving this revolution and the demand for high-performance servers is growing exponentially.

However, the increase in computing power is also accompanied by massive energy consumption, an issue that is leading to global discussions about infrastructure, efficiency and future investments [1].

At the same time, the question arises as to whether there is currently an overinvestment in computing power. The Chinese AI company DeepSeek, for example, has presented a model that works more efficiently than previous large language models (LLMs).

Does this mean that we will soon need less computing power?

Or will the Jevons paradox occur instead, i.e. the effect that more efficient technologies actually increase overall consumption in the long term? [2, 3]

In this article, I will focus on the key developments in the data center sector, the growing demand for energy, regional characteristics, current challenges and potential investment opportunities.

As always, the article is intended to shed light on the background to current events, provide food for thought and give impetus. The stocks mentioned do not, of course, constitute investment advice.

🤖 Data centers: the foundation of the AI revolution

The growing global demand for AI-supported software and digital applications requires powerful data centers. Goldman Sachs analysts forecast that the global demand for power from data centers will increase by 50% by 2027 and by up to 165 % could increase [1].

This chart below forecasts the energy consumption of data centers (in terawatt hours) by 2030, distinguishing between AI- and non-AI-based applications in the US and the rest of the world. Total consumption is expected to rise to over 1,000 TWh by 2030 [4].

Our analysts expect data center power consumption to increase by more than 160% by 2030

Source: [4], primary: Masanet et al. (2020), Cisco, IEA, Goldman Sachs Research

This data shows how AI applications will massively increase energy consumption. The rapid increase in the area of "US AI" and "Rest of world AI" is particularly striking.

The three main reasons for this increase are

• Larger AI models:

New models such as GPT-5 or DeepSeek AI require more and more computing power. Training and operating these models requires trillions of calculations [1].

• Real-time AI applications:

Companies are integrating AI into numerous applications: from search engines to personalized financial and healthcare services.

• Cloud computing & data storage:

As digitalization progresses, the global demand for data storage and cloud services is increasing [1].

Which companies dominate the market?

On the demand side for data centers, large hyperscale cloud providers and other companies are building large language models (LLMs) that are capable of processing and understanding natural language. These models need to be trained on huge amounts of information using power-intensive processors [4].

On the supply side, hyperscale cloud companies, data center operators and asset managers are deploying large amounts of capital to build new high-capacity data centers.

These include, among others:

• Microsoft $MSFT (+1,32%) : Operator of Azure Cloud and partner of OpenAI
• Alphabet $GOOGL (-1,98%) : With Google Cloud and DeepMind
• Amazon $AMZN (+0,25%) : AWS, the world's leading cloud provider
• Meta $META (+0,53%) : Develops its own AI chips and continues to expand its infrastructure

In addition, specialized data center providers such as Equinix $EQIX (+0,22%) and Digital Realty $DLR (+3,04%) as they supply physical infrastructure to the hyperscalers [6].

According to Goldman Sachs Research, demand for data center infrastructure will increasingly outstrip supply in the coming years.

The utilization rate of existing data centers is expected to rise from around 85% in 2023 to more than 95% by the end of 2026. However, the situation is expected to ease from 2027 onwards as new data centers are commissioned and demand growth driven by AI slows down (see chart below) [1].

Goldman Sachs currently estimates that the global power consumption of the data center market is around 55 gigawatts (GW). This is made up of cloud computing workloads (54%), traditional workloads such as email or data storage (32%) and AI (14%) [1].

For the future, analysts predict that electricity demand will increase to 84 GW by 2027. The share of AI is expected to grow to 27%, while the cloud share will fall to 50% and traditional workloads to 23% [1].

By the end of 2030, around 122 gigawatts (GW) of data center capacity will be online.

At this point, I asked myself as a layman how the units mentioned so far are to be understood, in my first graphic I speak of 1,000 TWh of energy consumption of all data centers by 2030 and now here is talk of 122 GW of data center capacity? In order not to go completely beyond the scope of the article, I have added a section at the very end in case some of you also feel like a layman and want to put the "units" into perspective.

... and now on with the article...

One central problem remains:
Where does all the energy come from?

⚡️Energieversorgung: Can the grid keep up?

According to estimates by Goldman Sachs, more than 720 billion US dollars will have to be invested in expanding the power grid worldwide by 2030 in order to supply the new data centers with sufficient energy [1].

Europe in particular, where electricity consumption was expected to decline for many years, is experiencing a veritable "demand shock" [1].

Which energy sources supply data centers?

• Natural gas & battery storage:

Natural gas is seen as a realistic short-term solution to meet continuous demand. It serves as a bridging technology until renewable energy and storage solutions are further developed, as renewable energy is not available around the clock [4].

• Renewable energies:

Wind and solar energy could cover around 80 % of demand in the long term, provided that sufficient storage solutions are integrated [4].

In practice, solar plants run on average only about 6 hours per day, while wind power plants run on average 9 hours per day. There is also a daily volatility in the capacity of these sources, depending on the radiation of the sun and the strength of the wind [4].

The graph shows the fluctuations in capacity factors for wind and solar energy in the USA in 2023. The capacity factor indicates how efficiently an energy source utilizes its maximum output throughout the year.

• Wind energy (light blue line): The highest capacity factors occur in the winter months (Jan-March) and drop significantly in the summer months (Jun-Aug).

• Solar energy (dark blue line): Efficiency rises in the spring (Mar-May) and reaches its maximum in the summer months (Jun-Aug) before falling in the winter (Nov-Dec).

The graph illustrates that wind and solar energy can complement each other seasonally: While wind is more efficient in winter, solar energy provides the highest yields in summer. This shows how important a balanced energy mix is to ensure security of supply.

In addition to finding environmentally friendly energy sources to power data centers, technology providers can reduce emissions intensity through efficiency gains.

The following chart shows the development of the workload and energy consumption of data centers between 2015 and 2023. Although the workload almost tripled, energy consumption remained almost constant until 2019 thanks to efficiency gains. The efficiency gains then slowed down from 2020 onwards.

Source: [4], primary: Masanet et al. (2020), IEA, Cisco, Goldman Sachs Research

This chart supports the discussion on the Jevons paradox (see below). Efficiency gains could be offset or even exceeded in the long term by higher workloads and AI demand. This highlights the need to make data center energy sources more sustainable.

• Nuclear energy:

Meanwhile, governments are also becoming more supportive of nuclear energy on the whole. Switzerland is reconsidering the use of nuclear generators for its electricity supply, while nuclear energy enjoys bipartisan support in the US and the Australian opposition party has put forward plans to introduce nuclear reactors [4].

Participants at the COP28 conference at the end of 2023, an annual summit convened by the United Nations to combat climate change, agreed to triple global nuclear capacity by 2050 [4].

Nuclear energy is considered the ideal option for basic power supply as it provides a reliable and constant supply of energy.

As a result, more and more large tech companies such as Alphabet, Amazon and Microsoft are turning to small modular nuclear power plants (SMRs).

📊 Increasing efficiency & the Jevons paradox

With new technologies such as DeepSeek, AI could work more efficiently in the future. But does greater efficiency automatically mean that less computing power is required?

The Jevons paradox: More efficiency = more consumption?

The Jevons paradox describes the fact that increases in efficiency often do not lead to lower consumption, but to higher consumption overall.

-Example:

In the 19th century, more efficient steam engines did not lead to lower coal consumption; on the contrary, as the machines became cheaper and more versatile, coal consumption actually increased.

With cars: more fuel-efficient engines did not lead to less gasoline consumption, but to people driving more cars.

-Applied to AI:

As AI models become more efficient, the cost per computation decreases. This makes AI applications attractive in even more areas, which in turn leads to a higher overall demand for computing power.

🌎 Regional distribution and global expansion of data center infrastructure

Current distribution: Where are the data centers located today?

Today, most data centers are located in the Asia-Pacific region and North America. Well-known locations are:

North America:

- Northern Virginia

- San Francisco Bay Area

Asia: Beijing

- Beijing

- Shanghai

These regions are characterized by high computing power, intensive data traffic and strong demand from corporate campuses [1].

The chart also shows the historical development of data center capacity by region (North America, APAC, etc.) from 2017 to 2024. The figures illustrate how fast the infrastructure for the AI revolution is growing and underlines why the energy requirements of data centers are increasing so rapidly.

The increase in capacity from around 20 GW in 2017 to almost 60 GW in 2024 shows an enormous growth trend. This correlates directly with the increasing demand for AI applications and cloud computing.

How is supply growing?

Goldman Sachs Research estimates that global data center capacity will increase to around 122 GW by the end of 2030, as mentioned above. The share of hyperscalers and specialized operators will increase from the current 60% to around 70% [1].

• Asia-Pacific:

The largest expansion of data centers has been recorded here in the past ten years.

• North America:

The largest expansion of new data centers is planned in North America over the next five years.

📈 Investment opportunities: Some winners of the AI and data center revolution

US shares e.g.:

• Carrier Global $CARR (-0,26%) : Precise cooling technology and air conditioning for data centers
• Vertiv Holdings $VRT (+4,02%) : Specialist in cooling and power solutions specifically for data centers
• Brookfield Renewable Partners $BEP.UN : Leading provider of renewable energy (hydropower, solar, wind) - supply contracts (PPAs) with data centers
• ON Semiconductor $ON (-0,71%) : Leader in chips for energy efficiency and thermal management. Solutions reduce power consumption in data centers and support AI integration
• Texas Instrumentes $TXN (+1,12%) : Energy-saving semiconductor products used in data center servers
• Equinix $EQIX (+0,22%) : Specialized in data center infrastructure
• Digital Realty $DLR (+3,04%) : Provider of physical infrastructure for data centers
• IBM $IBM (+1,55%) : Quantum computing technologies that potentially consume less energy and development of energy-efficient AI solutions
• Arista Networks $ANET (+3,99%) : Specialist in high-speed networking products for data centers
• Nvidia $NVDA (+4,72%) : Leader in AI GPUs, Leader in AI training market. Best choice for large AI models and data center training
• AMD $AMD (+2,72%) Competing with Nvidia with its own AI chips, but better positioned in the AI interference market where energy efficiency and cost-effectiveness are key. The interference market will be the next most important market, perhaps even the more important one.
• Broadcom $AVGO (+2,75%) Profits from network solutions for data centers
• Microsoft $MSFT (+1,32%) Google $GOOGL (-1,98%) Amazon $AMZN (+0,25%) : The big hyperscalers investing heavily in AI and cloud

European stocks e.g.:

• Siemens Energy $ENR (+2,01%) Important role in modernizing power grids, integrating renewable energies and improving storage solutions for data center reliability
• Schneider Electric $SU (+2,74%) : Leader in the development of energy management and cooling technology for data centers - specialty in the automation of both systems.
• ASML $ASML (-0,08%) : Indispensable for modern chip production
• Infineon $IFX (+1,84%) and STMicroelectronics $STM (+0,98%) : Leading semiconductor companies with a focus on AI applications
• RWE $RWE (+0,06%) and Enel $ENEL (-0,07%) Utilities that are increasingly focusing on renewable energies for data centers

Japanese stocks e.g:

• Daikin Industries $6367 (-0,82%) World market leader in air conditioning and cooling, offers specialized cooling systems for data centers and AI-supported plant management systems to further increase efficiency
• Tokyo Electron $8035 (+3,22%) : Important supplier for semiconductor manufacturing
• Mitsubishi Heavy Industries $7011 (+1,33%) : Works on the development of new nuclear power plants to secure the energy supply

🧠 Conclusion: AI, data centers & energy as the trend of the century?

Although some analysts warn of possible overinvestment, the figures indicate that the demand for computing power and energy for AI data centers will continue to rise sharply.

• Efficiency gains from models such as DeepSeek or new chip technologies could reduce energy consumption per computer, but the Jevons paradox means that overall demand will increase because more efficient systems will be used more often.

The biggest winners are therefore:

• Semiconductor companies: They supply the AI chips needed.
• Data center operators: They build the necessary infrastructure.
• Energy suppliers: They ensure the energy supply for the AI revolution.

In the long term, these companies could be among the biggest beneficiaries of the coming decades.

👨🏽‍💻 How do I position myself?

Personally, I think I am well positioned with the NASDAQ 100 $CSNDX (+1,22%) (portfolio share of 23%), as the focus is on US technology and growth stocks. The ETF complements my All-World with a stronger weighting in innovative sectors such as AI and cloud computing.

In the near future, I will also take a closer look at Daikin Industrie $6367 (-0,82%) share in order to increase the exposure to Japan and the share price offers an entry point at first glance.

In addition, AMD $AMD (+2,72%) has also caught my attention, the reason being its positioning in the aforementioned interference market. Most of the capital is currently flowing into the expansion of new AI models. However, as soon as these become a "commodity" and everyone uses them, most of the capital will probably flow into the interference market (market for the application of AI models).

Furthermore, I have Siemens AG $SIE (+3,19%) with approx. 2.3% portfolio share (still growing to approx. 4%), which I also see as well positioned for the future for the following reasons (in the context of the article):

Network stability

• Develops technologies for intelligent power grids ("smart grids"), essential for integrating renewable energies into the supply of data centers.

Data center control

• Provides automation and monitoring systems that optimize the energy consumption and efficiency of data centers

Efficient building structure

• The "Smart Infrastructure" division supports data centers with energy-efficient solutions for lighting, air conditioning and building monitoring

Not directly cooling systems, but:

• offers technologies that increase the energy efficiency of cooling systems by optimizing energy flows and data analysis

What is your opinion❓

• Which companies do you have on your radar?
• Is there a threat of overinvestment or are we just at the beginning of a century revolution?

Thanks for reading! 🤝

...Said digression follows after the sources...

__________

Sources:

[1] https://www.goldmansachs.com/insights/articles/ai-to-drive-165-increase-in-data-center-power-demand-by-2030

[2] "The Coal Question"
http://digamo.free.fr/peart96.pdf

[3] https://de.m.wikipedia.org/wiki/Jevons-Paradoxon

[4] https://www.goldmansachs.com/insights/articles/is-nuclear-energy-the-answer-to-ai-data-centers-power-consumption

[5] https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai

[6] https://www.cbre.com/insights/reports/global-data-center-trends-2024

__________

🧭 Digression: on gigawatts and terawatt hours

In order to understand the relationship between the two figures, 122 GW (gigawatts) and 1,000 TWh (terawatt hours), it is important to clarify the units and their meaning:

• 122 GW (gigawatts):

Refers to the current average power capacity that data centers worldwide require to function. Power (measured in GW) describes the amount of energy consumed per second. This is therefore a snapshot of energy requirements.

• 1,000 TWh (terawatt hours):

This is an indication of energy consumption over a certain period of time, in this case one year. It describes how much energy is required in total in 12 months.

The forecast of 1,000 TWh is slightly below the value resulting from the calculation. The graph shows values slightly above 1,000 TWh; according to the calculation based on 122 GW of power capacity, energy consumption should be around 1,069 TWh.

Nevertheless, general reasons for deviations may be as follows:

• Efficiency improvements: Data centers could operate more efficiently through improved cooling, optimized hardware and software and thus consume less energy.
• Peak vs. average consumption: The figure of 122 GW could reflect peak demand, while the actual average annual demand is somewhat lower.
• Adjustments to the model: It is possible that the forecast of 1,000 TWh is conservative and does not take into account all additional loads or regional differences.

This shows how much the demand for data centers and energy will increase due to AI and digitalization by 2030

__________

Meta, Tesla, Microsoft with quarterly figures + IBM impresses with sales outlook + Stock market day today offers many new figures from companies and the economy

Microsoft $MSFT (+1,32%)disappoints with cloud revenues

• Microsoft Corp. outperforms in the second quarter with earnings per share of USD 3.23.
• The analysts' estimates of USD 3.13. Revenue of USD 69.6 billion above expectations of USD 68.92 billion.
• Microsoft records a twelve percent increase in revenue to USD 69.6 billion in the second quarter of the 2024/25 financial year.
• Operating profit increases by 17 percent to almost USD 32 billion.
• Cloud growth falls short of expectations and the share comes under pressure after the close of trading.

Meta $META (+0,53%)exceeds expectations

• Meta Platforms Inc outperforms in the fourth quarter with earnings per share of USD 8.02.
• The analysts' estimates of USD 6.73. Sales of USD 48.39 billion exceed expectations of USD 47.03 billion.
• Meta Platforms expects first quarter revenue of $39.5-41.8 billion, missing estimates of $41.67 billion.
• Meta reports a 49 percent increase in net profit to 20.8 billion dollars despite billions in losses in the digital worlds segment.
• The daily user numbers of Meta apps exceeded analysts' expectations at 3.35 billion, while the revenue forecast for the current quarter is slightly below market expectations.
• Meta CEO Mark Zuckerberg explained on the quarterly results conference call that it is still too early to assess the impact of DeepSeek on infrastructure and capital expenditure.
• He also emphasized that a shift to inference computing does not necessarily mean a reduction in overall computing power.
• According to the Wall Street Journal, Meta is paying Donald Trump 25 million dollars to settle a lawsuit over the account blocking.
• Of this, 22 million dollars will flow into Trump's presidential library fund, while the rest will cover legal fees, among other things.

Tesla $TSLA (+10,24%)misses expectations

• Tesla Inc. misses the mark in the fourth quarter with earnings per share of USD 0.73.
• The analysts' estimates of USD 0.76. Sales of USD 25.71 billion below expectations of USD 27.23 billion.
• Tesla misses Wall Street's expectations with quarterly figures, as turnover is USD 25.7 billion and profit falls by 71 percent to USD 2.3 billion.
• Electric car deliveries fell in 2024, although a record 495,570 vehicles were delivered in the fourth quarter, which was still below analysts' estimates.
• Tesla CEO Elon Musk presented a far-reaching vision for the company in the Q4 2024 results conference call.
• His remarks focused on the progress made in autonomous driving and humanoid robots, which could massively increase the company's value in the long term

IBM $IBM (+1,55%)impresses with revenue outlook

• The computer group IBM is expecting higher growth this year than last.
• The company, which is listed on the Dow Jones Industrial Average, announced in Armonk on Wednesday evening that it expects revenue adjusted for currency effects to increase by at least five percent in 2025, thanks in part to rising demand for products for the use of artificial intelligence.
• Analysts surveyed by Bloomberg had expected an increase of just under five percent.
• Last year, sales adjusted for currency translation effects rose by three percent to 62.8 billion euros (60.3 billion euros).
• Operating profit adjusted for special charges climbed by nine percent to 11.2 billion dollars.
• Due to pension provisions running into the billions, the surplus fell by a fifth to six billion dollars.
• Last year's result was roughly in line with expectations.
• On the stock market, however, the share benefited from the sales outlook.
• The IBM share price climbed by double digits at times after trading hours, reaching a record high.

Thursday: Stock market dates, economic data, quarterly figures

Stock exchange holiday South Korea, China, Hong Kong, Singapore

• Quarterly figures / company dates USA / Asia
• 12:00 UPS quarterly figures
• 12:30 Caterpillar | Cigna quarterly figures
• 14:00 Mastercard annual results
• 22:00 Intel | Visa quarterly figures
• 22:30 Apple quarterly figures
• Untimed: Altria | Dow | Weyerhaeuser | Northrop Grumman | ResMed | Southwest Airlines | Comcast | Valero Energy | International Paper quarterly figures

• Quarterly figures / Company dates Europe
• 06:30 Nordea Bank | Banco Bilbao Annual Results
• 07:00 Deutsche Bank | DWS Group | Nokia | STMicroelectronics | ABB | Roche Holding AG, annual results
• 07:30 Symrise | Sanofi | SNP Schneider | KPN annual results
• 08:00 Shell | Hennes & Mauritz annual results | Glencore Production Report 2024
• 09:00 KSB Annual Results | Deutsche Bank | Symrise BI-PK
• 10:00 DWS Group Analyst Conference
• 11:00 Deutsche Bank Analyst Conference
• 13:00 Symrise Analyst Conference
• 14:00 Roche Analyst Conference
• 14:30 Assicurazioni Generali Investor Day

• Economic data

07:30 FR: GDP (1st release) 4Q PROGNOSIS: 0.0% yoy Q3: +0.4% yoy

08:00 DE: Import/export prices December and year 2024 Import prices FORECAST: +0.4% yoy/+2.0% yoy Previous: +0.9% yoy/+0.6% yoy

08:45 FR: Private consumption December FORECAST: +0.1% yoy/+0.2% yoy previously: +0.3% yoy/+0.3% yoy

09:00 ES: HICP and consumer prices (preliminary) January HICP FORECAST: +2.5% yoy previously: +2.8% yoy

10:00 DE: GDP (1st release) 4Q seasonally and calendar adjusted yoy FORECAST: -0.1% yoy Q3: +0.1% yoy Q3: calendar-adjusted yoy FORECAST: -0.1% yoy Q3: -0.3% yoy

10:00 IT: GDP (1st release) 4Q FORECAST: +0.1% yoy/+0.6% yoy Q3: 0.0% yoy/+0.4% yoy

11:00 EU: Labor market data December Eurozone Unemployment rate FORECAST: 6.3% previous: 6.3%

11:00 EU: GDP (1st release) 4Q Eurozone FORECAST: +0.1% yoy/+1.0% yoy Q3: +0.4% yoy/+0.9% yoy

11:00 EU: Economic Sentiment Index January Economic Sentiment Eurozone Forecast: 93.7 Previous: 93.7 Industrial Confidence Eurozone Forecast: -14.0 Previous: -14.1 Consumer Confidence Eurozone Forecast: -14.2 Previous: -14.2 Previous: -14.5

14:15 EU: ECB, outcome of the Governing Council meeting Deposit rate FORECAST: 2.75% PREVIEW: 3.00

14:30 US: GDP (1st release) 4Q annualized FORECAST: +2.5% yoy Q3: +3.1% yoy Q3 GDP Deflator FORECAST: +2.3% yoy Q3: +1.9% yoy Q3

14:30 US: Initial jobless claims (week) FORECAST: 228,000 Previous: 223,000