Foreign Investors Reevaluating Hedging Strategies: Berro
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Here's What Sent Microsoft Corporation (MSFT) Higher
Baron Funds, an investment management company, released its 'Baron Opportunity Fund' second-quarter 2025 investor letter. A copy of the letter can be downloaded here. In the second quarter, the fund posted solid returns, rising 23.27% (Institutional Shares), exceeding the Russell 3000 Growth Index's (the Benchmark) 17.55% gain and the S&P 500 Index's 10.94% gain. The Fund appreciated 8.52% for the first half, compared to 5.80% and 6.20% returns for the indexes. In addition, you can check the fund's top 5 holdings to determine its best picks for 2025. In its second-quarter 2025 investor letter, Baron Opportunity Fund highlighted stocks such as Microsoft Corporation (NASDAQ:MSFT). Microsoft Corporation (NASDAQ:MSFT) is a multinational software company that develops and supports software, services, devices, and solutions. The one-month return of Microsoft Corporation (NASDAQ:MSFT) was 0.77%, and its shares gained 20.19% of their value over the last 52 weeks. On August 19, 2025, Microsoft Corporation (NASDAQ:MSFT) stock closed at $509.77 per share, with a market capitalization of $3.789 trillion. Baron Opportunity Fund stated the following regarding Microsoft Corporation (NASDAQ:MSFT) in its second quarter 2025 investor letter: "Microsoft Corporation (NASDAQ:MSFT) is the world's largest software and cloud computing company. Microsoft was traditionally known for its Windows and Office products, but over the last five years it has built a $160 billion run-rate cloud business, including its Azure cloud infrastructure service and its Office 365 and Dynamics 365 cloud delivered applications. Shares outperformed on the back of a strong March quarter, driven primarily by its Azure business accelerating 400 basis points to 35% constant-currency growth, well ahead of expectations. For the June quarter, management guided Azure growth to hold at the 34% to 35% level, again above expectations of 31% to 32%, commenting that 'whereas they hoped to have supply demand in balance by end of the fiscal year, they now expect to be AI constrained past June as planned demand is growing a bit faster,' Microsoft remains well positioned across the overlapping software, cloud computing, and AI landscapes." A development team working together to create the next version of Windows. Microsoft Corporation (NASDAQ:MSFT) is in second position our list of 30 Most Popular Stocks Among Hedge Funds. As per our database, 284 hedge fund portfolios held Microsoft Corporation (NASDAQ:MSFT) at the end of the first quarter compared to 317 in the previous quarter. In the fourth quarter of fiscal year 2025, Microsoft Corporation (NASDAQ: MSFT) reported revenue of $76.4 billion, representing an 18% increase, or 17% when adjusted for constant currency. While we acknowledge the potential of Microsoft Corporation (NASDAQ:MSFT) as an investment, we believe certain AI stocks offer greater upside potential and carry less downside risk. If you're looking for an extremely undervalued AI stock that also stands to benefit significantly from Trump-era tariffs and the onshoring trend, see our free report on the best short-term AI stock. In another article, we covered Microsoft Corporation (NASDAQ:MSFT) and shared the list of trending AI stocks in focus. In addition, please check out our hedge fund investor letters Q2 2025 page for more investor letters from hedge funds and other leading investors. READ NEXT: The Best and Worst Dow Stocks for the Next 12 Months and 10 Unstoppable Stocks That Could Double Your Money. Disclosure: None. This article is originally published at Insider Monkey. Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
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Trump thinks owning a piece of Intel would be a good deal for the US. Here's what to know
SAN FRANCISCO (AP) — President Donald Trump wants the U.S. government to own a piece of Intel, less than two weeks after demanding the Silicon Valley pioneer dump the CEO that was hired to turn around the slumping chipmaker. If the goal is realized, the investment would deepen the Trump administration's involvement in the computer industry as the president ramps up the pressure for more U.S. companies to manufacture products domestically instead of relying on overseas suppliers. What's happening? The Trump administration is in talks to secure a 10% stake in Intel in exchange for converting government grants that were pledged to Intel under President Joe Biden. If the deal is completed, the U.S. government would become one of Intel's largest shareholders and blur the traditional lines separating the public sector and private sector in a country that remains the world's largest economy. Why would Trump do this? In his second term, Trump has been leveraging his power to reprogram the operations of major computer chip companies. The administration is requiring Nvidia and Advanced Micro Devices, two companies whose chips are helping to power the craze around artificial intelligence, to pay a 15% commission on their sales of chips in China in exchange for export licenses. Trump's interest in Intel is also being driven by his desire to boost chip production in the U.S., which has been a focal point of the trade war that he has been waging throughout the world. By lessening the country's dependence on chips manufactured overseas, the president believes the U.S. will be better positioned to maintain its technological lead on China in the race to create artificial intelligence. Didn't Trump want Intel's CEO to quit? That's what the president said August 7 in an unequivocal post calling for Intel CEO Lip-Bu Tan to resign less than five months after the Santa Clara, California, company hired him. The demand was triggered by reports raising national security concerns about Tan's past investments in Chinese tech companies while he was a venture capitalist. But Trump backed off after Tan professed his allegiance to the U.S. in a public letter to Intel employees and went to the White House to meet with the president, who applauded the Intel CEO for having an 'amazing story.' Why would Intel do a deal? The company isn't commenting about the possibility of the U.S. government becoming a major shareholder, but Intel may have little choice because it is currently dealing from a position of weakness. After enjoying decades of growth while its processors powered the personal computer boom, the company fell into a slump after missing the shift to the mobile computing era unleashed by the iPhone's 2007 debut. Intel has fallen even farther behind in recent years during an artificial intelligence craze that has been a boon for Nvidia and AMD. The company lost nearly $19 billion last year and another $3.7 billion in the first six months of this year, prompting Tan to undertake a cost-cutting spree. By the end of this year, Tan expects Intel to have about 75,000 workers, a 25% reduction from the end of last year. Would this deal be unusual? Although rare, it's not unprecedented for the U.S. government to become a significant shareholder in a prominent company. One of the most notable instances occurred during the Great Recession in 2008 when the government injected nearly $50 billion into General Motors in return for a roughly 60% stake in the automaker at a time it was on the verge of bankruptcy. The government ended up with a roughly $10 billion loss after it sold its stock in GM. Would the government run Intel? U.S. Commerce Secretary Howard Lutnick told CNBC during a Tuesday interview that the government has no intention of meddling in Intel's business, and will have its hands tied by holding non-voting shares in the company. But some analysts wonder if the Trump administration's financial ties to Intel might prod more companies looking to curry favor with the president to increase their orders for the company's chips. What government grants does Intel receive? Intel was among the biggest beneficiaries of the Biden administration's CHIPS and Science Act, but it hasn't been able to revive its fortunes while falling behind on construction projects spawned by the program. The company has received about $2.2 billion of the $7.8 billion pledged under the incentives program — money that Lutnick derided as a 'giveaway' that would better serve U.S. taxpayers if it's turned into Intel stock. 'We think America should get the benefit of the bargain,' Lutnick told CNBC. 'It's obvious that it's the right move to make.' Michael Liedtke, The Associated Press
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AQR's ‘Hard to Believe' Study Spurs Clash Over AI Use for Quants
(Bloomberg) -- Wall Street quants and leading financial academics are clashing over whether artificial intelligence has upended one of the core principles of systematic investing. Quant traders, who use rules-based strategies derived from data analysis, have long believed their models get less effective when they become too complicated. That's because they suck in too much of the distortive noise that makes predicting markets such a challenge in the first place. Why New York City Has a Fleet of New EVs From a Dead Carmaker Chicago Schools Seeks $1 Billion of Short-Term Debt as Cash Gone Trump Takes Second Swing at Cutting Housing Assistance for Immigrants A Photographer's Pipe Dream: Capturing New York's Vast Water System A London Apartment Tower With Echoes of Victorian Rail and Ancient Rome But a researcher at AQR Capital Management has sparked a backlash with a study claiming the opposite — that rather than being a liability, bigger and more complex models might offer advantages in finance. The paper, titled , showed that a US stock market trading strategy trained on more than 10,000 parameters and just a year of data beat a simple buy-and-hold benchmark. 'This idea of preferring small, parsimonious models is a learned bias,' said Bryan Kelly, head of machine learning at AQR and one of the paper's three authors. 'All of us are on a day-to-day basis using these large language models that were revolutionary in their success because of this push toward extraordinarily large parameterizations.' The research has triggered a heated debate since it was published in the prestigious last year, among both peers in the quant industry and those in related academic circles. At least six papers, including from scholars at Oxford University and Stanford University, have now challenged its findings. Some argue the study has a questionable design that renders it irrelevant for live trading. Others say it's less cutting-edge than it appears anyway. (Kelly has subsequently written a defense.) Among the most notable critics is Stefan Nagel, a finance professor at the University of Chicago — the very school where two of AQR's founders met and where the firm's original investment philosophy took shape. His first reaction? 'I found the empirical results hard to believe,' he said. After digging into the details of the study, Nagel concluded that because the model was dissecting just 12 months of data, it was simply copying signals that had worked more recently. In other words, it was following a momentum strategy — a well-established trading approach. 'It's not because the approach learned from the data that this effect is there,' Nagel said. 'It's because they did something mechanical implicitly, and this mechanical thing happened to work well by luck.' Jonathan Berk, a Stanford economist who was among the first and fiercest critics of the paper, called it 'virtually useless' for aiming at predictions that tell you nothing about what drives asset returns. Daniel Buncic at the Stockholm Business School said the study makes some obviously wrong design choices to reach its conclusions. Co-written with Semyon Malamud at EPFL in Switzerland and Kangying Zhou at Yale University, the paper has provoked this response because it challenges a long-held assumption about forecasting financial markets. While modern AI can perform remarkable tasks like telling cats from dogs in an image, that's because it can learn from a massive supply of photos, and because animals have defined and unchanging features. In contrast, stocks provide an inherently limited amount of data (especially for slower-moving strategies that may only trade once a month), and each can be swayed by countless different forces. The fear has always been overfitting — that complex models will learn from all the noise in historical data, much of which may not apply in future trading. So quants have traditionally relied on relatively simple insights, like the famous Fama-French three-factor model (which analyzes returns based on each company's size, valuation and relationship with the broader market). AQR itself was built on such so-called factors, which aim to outperform over long stretches of time. It is only in recent years that the $146 billion money manager has raised capital for machine-learning strategies and said not all trading signals have to be backed by economic theory. Kelly's main contention is that traditional quant models are so simple they under-fit, producing inferior forecasts, while sufficiently complex models actually learn not to overfit too much. To be sure, the critics don't argue that machine learning has nothing to offer finance. They mainly view the paper's results as too good to be true. 'The methods have a role and can be used,' said John Campbell, an economics professor at Harvard University who co-founded Arrowstreet Capital, a quant firm. 'But some of the most eye-catching results have successfully been called into question.' Even Ben Recht at the University of California, Berkeley — a renowned computer scientist who back in 2007 developed the method used in the paper — weighed in in his blog, saying 'the hype cycle gets everyone confused.' The method in the paper was far from cutting-edge AI, he said, and anyhow didn't seem necessary for the task at hand. To Kelly, who teaches at Yale alongside his AQR gig, criticisms of the paper are 'a little bit hollow' for focusing on the narrow aspects of what was ultimately proof of concept research. 'The practitioner world understands that these conceptual methods, when implemented in a more sophisticated manner, are going to be beneficial,' he said. 'The exact ideal combination of how much of frontier machine learning methods to use versus more traditional economically oriented methods — that's still something we're trying to understand.' 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