The artificial intelligence boom is pushing demand for Nvidia's products past Wall Street's already lofty expectations. The chipmaker beat analyst expectations on Wednesday by leaps and bounds when it reported fourth-quarter earnings, posting 22.1bn in revenue on an expected 20.55bn and 4.93 in earnings per share against an expected 4.64. Revenue was 22% higher than the previous quarter, up 265% from a year ago. Nvidia's most closely watched earnings figure – revenue from data centers – was up more than 400% from the same period last year, reaching 18.4bn. Jensen Huang, founder and CEO of Nvidia, said in a press release, "Accelerated computing and generative AI have hit the tipping point. Demand is surging worldwide across companies, industries and nations."

Lyft beat estimates for fourth-quarter profit on Tuesday and said it would generate positive free cash flow for the first time in 2024, as the ride-share platform reaps the benefits of heavy cost cuts. Company shares surged nearly 60% in extended trading but erased a third of those gains after the CFO corrected a major mistake in the earnings report. Erin Brewer had said that the company would grow by 500 basis points (5%) in 2024, but later said that the real increase would be a factor of 10 lower – 50 basis points (0.5%). In 2023, the stock gained about 36%. Rides to stadiums grew more than 35% last year from 2022, mainly driven by Taylor Swift's Eras Tour, Beyoncé's Renaissance World Tour and sporting events, Lyft said.

SoftBank Group shares surged for a third day on Tuesday, following the explosive rally of its Arm Holdings, the chip designer that has almost doubled in value since making the case last week for how it will benefit from the artificial intelligence (AI) boom. SoftBank's stock climbed as much as 11% to the highest level since May 2021. SoftBank held onto a stake of about 90% in Arm as it took the company public last year. Arm's shares rose 29% on Monday, pushing its gains to more than 90% since it reported financial results on Feb. 7. The company is expanding beyond its traditional base in smartphone technology into new markets like artificial intelligence applications, lifting its outlook.

UPS's largest layoffs in its 116-year history were made possible, in part, by new technologies including artificial intelligence, CEO Carol Tome said last week. Citing one example, she said that machine learning allows salespeople to put together proposals without having to ask pricing experts for guidance. UPS is among a growing number of companies facing an AI two-step of sorts: showing investors how AI helps do more with less while simultaneously avoiding the reputational impact of directly linking technology with job cuts. A UPS spokesperson later said AI is not replacing workers, and that executives did not make an explicit connection between AI and the permanent layoffs on the company's earnings call.

Large-language models (LLMs) can support a wide range of applications like conversational agents, creative writing or general query answering. However, they are ill-suited for query answering in high-stake domains like medicine because they are typically not robust - even the same query can result in different answers when prompted multiple times. In order to improve the robustness of LLM queries, we propose using ranking queries repeatedly and to aggregate the queries using methods from social choice theory. We study ranking queries in diagnostic settings like medical and fault diagnosis and discuss how the Partial Borda Choice function from the literature can be applied to merge multiple query results. We discuss some additional interesting properties in our setting and evaluate the robustness of our approach empirically.

Explaining stock predictions is generally a difficult task for traditional non-generative deep learning models, where explanations are limited to visualizing the attention weights on important texts. Today, Large Language Models (LLMs) present a solution to this problem, given their known capabilities to generate human-readable explanations for their decision-making process. However, the task of stock prediction remains challenging for LLMs, as it requires the ability to weigh the varying impacts of chaotic social texts on stock prices. The problem gets progressively harder with the introduction of the explanation component, which requires LLMs to explain verbally why certain factors are more important than the others. On the other hand, to fine-tune LLMs for such a task, one would need expert-annotated samples of explanation for every stock movement in the training set, which is expensive and impractical to scale. To tackle these issues, we propose our Summarize-Explain-Predict (SEP) framework, which utilizes a self-reflective agent and Proximal Policy Optimization (PPO) to let a LLM teach itself how to generate explainable stock predictions in a fully autonomous manner. The reflective agent learns how to explain past stock movements through self-reasoning, while the PPO trainer trains the model to generate the most likely explanations from input texts. The training samples for the PPO trainer are also the responses generated during the reflective process, which eliminates the need for human annotators. Using our SEP framework, we fine-tune a LLM that can outperform both traditional deep-learning and LLM methods in prediction accuracy and Matthews correlation coefficient for the stock classification task. To justify the generalization capability of our framework, we further test it on the portfolio construction task, and demonstrate its effectiveness through various portfolio metrics.

Generative AI tools such as ChatGPT can fundamentally change the way investors process information. We probe the economic usefulness of these tools in summarizing complex corporate disclosures using the stock market as a laboratory. The unconstrained summaries are remarkably shorter compared to the originals, whereas their information content is amplified. When a document has a positive (negative) sentiment, its summary becomes more positive (negative). Importantly, the summaries are more effective at explaining stock market reactions to the disclosed information. Motivated by these findings, we propose a measure of information ``bloat." We show that bloated disclosure is associated with adverse capital market consequences, such as lower price efficiency and higher information asymmetry. Finally, we show that the model is effective at constructing targeted summaries that identify firms' (non-)financial performance. Collectively, our results indicate that generative AI adds considerable value for investors with information processing constraints.

Meta shares soared 12% in after-hours trading following a strong fourth-quarter earnings report released the day after CEO Mark Zuckerberg took a beating in a contentious congressional hearing. The company also announced it will pay a 50 cent-per-share dividend to investors for the first time, and has authorized a 50bn share buyback program. Overall, Meta reported fourth-quarter revenue of 40.1bn, beating the predicted 39.18bn and up 25% year-over-year. The report comes as Meta, like many of its big tech peers, is seeking to integrate artificial intelligence tools into its core products. In a statement accompanying the report, Zuckerberg said Meta has "made a lot of progress on our vision for advancing AI and the metaverse".

Alphabet stock slid more than 5% in after-hours trading Tuesday despite narrowly beating overall revenue predictions for quarter four of 2023 after the tech giant fell short in its key advertising sector. The Google parent company reported a miss on predicted advertising revenue at 65.52bn compared to 65.8bn, but beat predictions for overall revenue at 86.31bn compared to 85.36bn – up 13% year over year. Referencing the overall revenue beat, Alphabet chief financial officer called the results "very strong". "We remain committed to our work to durably re-engineer our cost base as we invest to support our growth opportunities," she said. The lukewarm response to the report comes after the Google parent company laid off 1,000 employees in January, according to the Alphabet Workers Union.

Microsoft beat analyst expectations Tuesday as its heavy bets on artificial intelligence bore fruit, particularly for its Azure cloud computing unit. The software giant reported revenue of 62bn, up 18% year-over-year, surpassing anticipated earnings of 61.1bn. CEO Satya Nadella said: "We've moved from talking about AI to applying AI at scale. By infusing AI across every layer of our tech stack, we're winning new customers and helping drive new benefits and productivity gains across every sector." Microsoft Cloud revenue rose 24% year-over-year.