Elon Musk becomes world's first trillionaire as SpaceX soars in stock market debut Elon Musk on Friday became the world's first trillionaire after shares in his SpaceX rocket company soared during the biggest-ever stock market debut. The Tesla and SpaceX founder comfortably cemented his status as the world's richest man, with his total net worth standing at $1.11tn (£828bn) according to the Bloomberg rich list. It came as the rocket, telecommunications and artificial intelligence (AI) company listed on the Nasdaq stock exchange with a value of $2.2tn. The company said its shares would be offered at $135 each, but trading opened at $150 and briefly reached $176.50 in a show of investor enthusiasm for potential business related to space and companies associated with Musk. SpaceX shares closed on Friday at about $161.

Elon Musk's SpaceX valued at nearly $1.8tn ahead of record share sale SpaceX has raised $75bn (£56bn) from financial firms ahead of it becoming a publicly traded company on Friday, in what is expected to be the highest-value stock listing in history. In a filing with the US Securities and Exchange Commission, the space exploration and artificial intelligence (AI) company said it had sold $75bn in shares priced at $135 each. The share price matches the estimate SpaceX gave last week, leaving the firm's expected initial stock market value to be nearly $1.8tn. At that value, chief executive Elon Musk - already the richest man in the world - is set to become the world's first trillionaire. Once shares start trading, their value could rise or fall depending on how many shares are made available for sale, and how strong the demand is for those shares.

Taiwan's economy is booming thanks to AI. For Li, an engineer at Taiwanese computer giant ASUS, the AI boom sweeping Taiwan has made it an exciting time to work in tech. Taiwan is a semiconductor powerhouse, producing about 90 percent of the most advanced chips used to power leading AI models such as ChatGPT and Claude. Still, Li worries that the spoils of Taiwan's AI windfall are not being shared equally. "Most industries unrelated to tech don't seem to be feeling the benefits, so it doesn't feel evenly distributed at the moment," Li said, explaining that many of his former classmates working outside of tech do not appear to be doing as well.

Elon Musk's SpaceX has revealed its plans to go public in the US, allowing people to trade shares in the firm on the stock market. SpaceX makes rockets, offers a satellite internet service called Starlink, and also owns Musk's controversial artificial intelligence (AI) firm xAI. The initial public offering (IPO) on the US stock market is set to be the largest in Wall Street history and could start next month under the ticker symbol SPCX. Because of the shares he will own in SpaceX, the IPO could make billionaire Musk, who is already the world's richest person, a trillionaire. SpaceX values itself at $1.25tn, and Musk's majority ownership of the company means his share could be worth more than $600bn.

Datacentres and industrial complexes used by Google, Microsoft and Amazon in Medemblik, the Netherlands. Datacentres and industrial complexes used by Google, Microsoft and Amazon in Medemblik, the Netherlands. 'We could hit a wall': why trillions of dollars of risk is no guarantee of AI reward Progress of artificial general intelligence could stall, which may lead to a financial crash, says Yoshua Bengio, one of the'godfathers' of modern AI Will the race to artificial general intelligence (AGI) lead us to a land of financial plenty - or will it end in a 2008-style bust? Trillions of dollars rest on the answer. The figures are staggering: an estimated $2.9tn (£2.2tn) being spent on datacentres, the central nervous systems of AI tools; the more than $4tn stock market capitalisation of Nvidia, the company that makes the chips powering cutting-edge AI systems; and the $100m signing-on bonuses offered by Mark Zuckerberg's Meta to top engineers at OpenAI, the company behind ChatGPT. These sky-high numbers are all propped up by investors who expect a return on their trillions.

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Stock markets had a stellar run in 2025. From North America to Europe and Asia, markets racked up some of the biggest gains in years. The globally-focused MSCI ACWI Ex-US index had its strongest performance since 2009, when the global financial crisis was in full swing. The index, which tracks non-US stocks in more than 40 markets, was on Wednesday on track to finish 2025 up about 30 percent, compared with the nearly 18 percent return of the benchmark S&P 500. The bullish streak marks a break from the decade-plus trend of US stocks dominating global indexes.

Much of the US economy rests on AI's future. On this episode of podcast, cohost Joe Weisenthal breaks down why AI's impact on finance goes beyond billion-dollar investments. If you read any of WIRED's recent AI edition, you know that lots of people are spending lots of time talking about how the technology is revolutionizing pretty much everything--from coding to writing to accounting. You've also probably heard by now, from us or somebody else, that we might very well be in an economic bubble of AI origin, one wherein the billions and billions of dollars being funneled into the industry is creating an untenable economic scenario that could turn catastrophic. Of course, you may also have read that I'm really sick of being asked about AI . I'm still not sick, though, of asking other people about it--especially when they're much smarter about this stuff than I am. Enter Joe Weisenthal, the cohost of Bloomberg's fantastic podcast, and a former coworker of mine. Trust me: As someone who spent a year listening to Joe lose his mind in the office--loudly!--anytime the economy hiccuped, few people think more about our country's, and our planet's, financial circumstances than Joe does. And right now, Joe's concerns aren't strictly about what happens if or when that AI bubble bursts. His worries are more focused on what's going right and wrong with the US economy writ large. For this week's episode of, Joe and I talked about weird market indicators, US competition with China, and whether or not we should all prepare for an AI economic apocalypse. Nice to see you again. We were just talking about how [you] and I worked together--what was that, like nine years ago? I think you were there 2014, 2015, so maybe 10 years ago or something? Yeah, I worked at Bloomberg. I lasted about a year. But Joe, you were there, you were loud, you were proud, you were always very excited about the economy.

Understanding sentiment in financial documents is crucial for gaining insights into market behavior. These reports often contain obfuscated language designed to present a positive or neutral outlook, even when underlying conditions may be less favorable. This paper presents a novel approach using Aspect-Based Sentiment Analysis (ABSA) to decode obfuscated sentiment in Thai financial annual reports. We develop specific guidelines for annotating obfuscated sentiment in these texts and annotate more than one hundred financial reports. We then benchmark various text classification models on this annotated dataset, demonstrating strong performance in sentiment classification. Additionally, we conduct an event study to evaluate the real-world implications of our sentiment analysis on stock prices. Our results suggest that market reactions are selectively influenced by specific aspects within the reports. Our findings underscore the complexity of sentiment analysis in financial texts and highlight the importance of addressing obfuscated language to accurately assess market sentiment.

Stock trading strategies play a critical role in investment. However, it is challenging to design a profitable strategy in a complex and dynamic stock market. In this paper, we propose an ensemble strategy that employs deep reinforcement schemes to learn a stock trading strategy by maximizing investment return. We train a deep reinforcement learning agent and obtain an ensemble trading strategy using three actor-critic based algorithms: Proximal Policy Optimization (PPO), Advantage Actor Critic (A2C), and Deep Deterministic Policy Gradient (DDPG). The ensemble strategy inherits and integrates the best features of the three algorithms, thereby robustly adjusting to different market situations. In order to avoid the large memory consumption in training networks with continuous action space, we employ a load-on-demand technique for processing very large data. We test our algorithms on the 30 Dow Jones stocks that have adequate liquidity. The performance of the trading agent with different reinforcement learning algorithms is evaluated and compared with both the Dow Jones Industrial Average index and the traditional min-variance portfolio allocation strategy. The proposed deep ensemble strategy is shown to outperform the three individual algorithms and two baselines in terms of the risk-adjusted return measured by the Sharpe ratio. This work is fully open-sourced at \href{https://github.com/AI4Finance-Foundation/Deep-Reinforcement-Learning-for-Automated-Stock-Trading-Ensemble-Strategy-ICAIF-2020}{GitHub}.