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The post BlackRock boosts AI and US equity exposure in $185 billion models appeared on BitcoinEthereumNews.com. BlackRock is steering $185 billion worth of model portfolios deeper into US stocks and artificial intelligence. The decision came this week as the asset manager adjusted its entire model suite, increasing its equity allocation and dumping exposure to international developed markets. The firm now sits 2% overweight on stocks, after money moved between several of its biggest exchange-traded funds. This wasn’t a slow shuffle. Billions flowed across multiple ETFs on Tuesday as BlackRock executed the realignment. The iShares S&P 100 ETF (OEF) alone brought in $3.4 billion, the largest single-day haul in its history. The iShares Core S&P 500 ETF (IVV) collected $2.3 billion, while the iShares US Equity Factor Rotation Active ETF (DYNF) added nearly $2 billion. The rebalancing triggered swift inflows and outflows that realigned investor exposure on the back of performance data and macroeconomic outlooks. BlackRock raises equities on strong US earnings The model updates come as BlackRock backs the rally in American stocks, fueled by strong earnings and optimism around rate cuts. In an investment letter obtained by Bloomberg, the firm said US companies have delivered 11% earnings growth since the third quarter of 2024. Meanwhile, earnings across other developed markets barely touched 2%. That gap helped push the decision to drop international holdings in favor of American ones. Michael Gates, lead portfolio manager for BlackRock’s Target Allocation ETF model portfolio suite, said the US market is the only one showing consistency in sales growth, profit delivery, and revisions in analyst forecasts. “The US equity market continues to stand alone in terms of earnings delivery, sales growth and sustainable trends in analyst estimates and revisions,” Michael wrote. He added that non-US developed markets lagged far behind, especially when it came to sales. This week’s changes reflect that position. The move was made ahead of the Federal…

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Exploring how the costs of a pandemic can lead to a self-enforcing lockdown in a networked economy, analyzing the resulting changes in network structure and the existence of stable equilibria.