What we’re reading (1/28)
“Fed Holds Interest Rates Steady In First Policy Meeting Of 2026 In Split Decision” (Yahoo! Finance). “The Federal Reserve held interest rates steady in a range of 3.5%-3.75% in its first meeting of the year, as widely expected. The decision was not unanimous, and two Federal Open Market Committee officials dissented. Federal Reserve governors Stephen Miran and Chris Waller voted to cut interest rates by 25 basis points.”
“What The Slide In The Dollar Means For Trade, Travel And Investment” (Wall Street Journal). “Wall Street is betting there will be more weakness to come, potentially ending a yearslong run in which the dollar has far outstripped many peers, enticing investors the world over to park more money in America.”
“Dow, S&P 500, Nasdaq Futures Slip As Tesla, Meta, Microsoft Diverge After Earnings” (Yahoo! Finance). “Meta (META) surged as much as 10% in extended trading after issuing a first-quarter revenue outlook that topped Wall Street estimates, even as it said its AI ambitions would fuel spending to as much as $135 billion this year. Tesla (TSLA) gained around 2% after reporting quarterly results that exceeded expectations. But Microsoft (MSFT) slid nearly 7% as investors reacted to slower cloud growth during its fiscal second quarter and higher-than-anticipated capital spending and finance lease costs. Amazon (AMZN) fell in tandem in after-hours action.”
“When All Bets Are Off, All Bets Are On” (Wall Street Journal). “A new study of speculative financial behavior over more than two centuries finds exactly what anyone with common sense would have predicted: People take more risk when stocks go up and the economy is booming, and it can last surprisingly long. ‘Epochs of high speculation coincide with higher stock market returns and higher economic growth,’ write economic historians William Quinn, John Turner and Clive Walker. They add that ‘a prolonged period of low interest rates can lead to the gradual development of a culture of more speculative investment.’”
“Behavioral Economics Of AI: LLM Biases And Corrections” (Pietro Bini, Lin William Cong, Xing Huang, and Lawrence J. Jin). “Do generative AI models, particularly large language models (LLMs), exhibit systematic behavioral biases in economic and financial decisions? If so, how can these biases be mitigated? Drawing on the cognitive psychology and experimental economics literatures, we conduct the most comprehensive set of experiments to date—originally designed to document human biases—on prominent LLM families across model versions and scales. We document systematic patterns in LLM behavior. In preference-based tasks, responses become more human-like as models become more advanced or larger, while in belief-based tasks, advanced large-scale models frequently generate rational responses. Prompting LLMs to make rational decisions reduces biases.”
