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Yahoo
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- Yahoo
Bank of England may face lower inflation despite ‘Oasis bump'
Bank of England forecasts on inflation for July could be too high despite suggestions Oasis concerts could make prices jump, forecasters have said. The Bank's last Monetary Policy Report took a gloomy view on the state of the UK economy as it claimed inflation would hit 3.8 per cent in the month of July. But leading forecasters have said the Bank's prediction could be too high, with inflation expected to come in in slightly lower than it forecast. Such a scenario could ease concerns that inflation has proven more difficult to bring down, which would lead to further calls for interest rates to be cut. A Bloomberg poll of economists said consumer price index (CPI) inflation would hit 3.7 per cent while the Bank of England said earlier this month it could reach 3.8 per cent. Inflation data is key for coming months July is a key month for price data given retail price index (RPI) inflation, which includes some housing costs and is less widely used as a metric, is used to calculate increases in train ticket prices. The consensus forecast for RPI inflation is 4.6 per cent, which could lead to train fares rising by 5.6 per cent next year if recent trends are followed. Bank economists believe a rise in price growth over the summer will hit its climax in September when CPI inflation could hit four per cent, which could be more damaging for the government given the month's inflation data is used to calculate increases in benefits and the triple lock pension. Capital Economics' Paul Dales said it will be crucial how far services inflation rises over the summer months, with some 'unfavourable' effects likely to add to price growth from summer events, including in the communication sector and restaurants or hotel prices. 'The Taylor Swift effect' 'Just like the Taylor Swift effect, any upward influence on hotel prices from the Oasis concerts is likely to be very small,' Dales said, adding that less volatile items in the inflation basket may see lower price growth and offset increases. Goldman Sachs said it expects services inflation to increase marginally in July due to some initial big price hikes in June already being made. 'Much of the strength in the June number was driven by volatile components, which points to risks of payback at this print, while the annual rate of rent inflation should decline given smaller increases in non-private rents compared to a year ago,' analysts at the Wall Street bank said. 'However, base effects resulting from a sharp decrease in accommodation services price in July 2024 falling out of the figures should push up on the annual rate of services inflation.' Goldman Sachs said its models show inflation in some areas falling 'notably' while Deutsche Bank said markets could price in fewer cuts if inflation ticks up more than expected. 'If our projections [of 3.8 per cent inflation] prove to be correct, the market may further reduce the implied chance of another cut by the November meeting, which in recent days has fallen firmly below 50 per cent,' Deutsche Bank's Sanjay Raja said. Leading City analysts have suggested that the effects of falling interest rates were beginning to show in people's decision-making, with lower savings rates now having an effect on investment. 'We've reached a tipping point, where falling savings rates have convinced would-be investors that it's time to take the plunge and make the most of the huge growth potential offered by investing,' said Sarah Coles, head of personal finance, Hargreaves Lansdown. 'It seems that the Bank of England cutting rates, and banks following suit with savings deals, has persuaded some savers that they'd be better off investing.' But the threat of high inflation has continued to scare businesses, with price growth cited as a top concern in a KPMG survey of top businesses across the country. 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
Yahoo
an hour ago
- Yahoo
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.' Foreigners Are Buying US Homes Again While Americans Get Sidelined What Declining Cardboard Box Sales Tell Us About the US Economy Women's Earnings Never Really Recover After They Have Children Americans Are Getting Priced Out of Homeownership at Record Rates Survived Bankruptcy. Next Up: Cultural Relevance? ©2025 Bloomberg L.P. 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
Bloomberg
an hour ago
- Bloomberg
Turkish Appliance Giant's Debt Crashes After Second-Quarter Loss
Bonds of Turkish appliance retailer Vestel Elektronik plummeted on Wednesday after the company reported a 7.26 billion lira ($177 million) second-quarter loss, dialing up the pressure as it struggles to rein in its debt burden. Vestel's $500 million of May 2029 bonds fell by more than 8.5 cents on Wednesday before recovering slightly, price data compiled by Bloomberg show. The bonds trade well below face value and are currently quoted at around 79.9 cents on the dollar.
