Nissan and Honda are expected to formally begin merger talks on Monday, NHK reported. The possibility of the two combining has been one of the options discussed as the carmakers work to stay competitive in the face of overcapacity and the need for huge investments related to electric-vehicle and self-driving technologies. If the two companies unite along with Mitsubishi Motors, Japan's auto industry will essentially be reduced to two groups: Toyota and Honda-Nissan. Their merger could create the world's third-largest auto group, following Toyota, and related companies, and the Volkswagen Group. Competition in the auto industry is intensifying, with nontraditional players expanding into the business in response to the rise of new technologies, such as connected vehicles and autonomous vehicles, as well as the ongoing process of electrification.

Post-earnings volatility prediction is critical for investors, with previous works often leveraging earnings call transcripts under the assumption that their rich semantics contribute significantly. To further investigate how transcripts impact volatility, we introduce DEC, a dataset featuring accurate volatility calculations enabled by the previously overlooked beforeAfterMarket attribute and dense ticker coverage. Unlike established benchmarks, where each ticker has only around two earnings, DEC provides 20 earnings records per ticker. Using DEC, we reveal that post-earnings volatility undergoes significant shifts, with each ticker displaying a distinct volatility distribution. To leverage historical post-earnings volatility and capture ticker-specific patterns, we propose two training-free baselines: Post-earnings Volatility (PEV) and Same-ticker Post-earnings Volatility (STPEV). These baselines surpass all transcripts-based models on DEC as well as on established benchmarks. Additionally, we demonstrate that current transcript representations predominantly capture ticker identity rather than offering financially meaningful insights specific to each earnings. This is evidenced by two key observations: earnings representations from the same ticker exhibit significantly higher similarity compared to those from different tickers, and predictions from transcript-based models show strong correlations with prior post-earnings volatility.

One of the promises of the next era of generative AI is that the technology will be agentic, or have the ability to perform tasks autonomously on behalf of us chaotic humans. That means AI agents will theoretically be able to "reason" about the next steps they should take, allowing them to execute multiple actions from a single query. The possibilities are endless, if you believe the hype--think maximum efficiency and productivity, plus a host of other buzz word-latent phrases that one might hear during a tech giant's quarterly earnings call. All I want AI to do for me, however, is to shop. I understand that some people find shopping to be a pleasurable act, but the options overwhelm me, whether I'm in an actual store or stuck in an endless scroll.

Comcast is spinning out Rotten Tomatoes, Fandango and a bunch of NBCUniversal (NBCU) cable networks into a separate company. That means USA Network, CNBC, MSNBC, Oxygen, E!, SYFY and Golf Channel will soon have a new home. Comcast is hanging onto other NBCU operations, namely NBC, Peacock, film and TV studios, Telemundo and theme parks. Bravo is also sticking around to help keep feeding Peacock's ever-hungry reality TV maw. Comcast says the new entity will be a "tax-free spin-off" and the step is "expected to be accretive to revenue growth at Comcast and approximately neutral to Comcast's leverage position."

As financial markets grow increasingly complex, there is a rising need for automated tools that can effectively assist human analysts in equity research, particularly within sell-side research. While Generative AI (GenAI) has attracted significant attention in this field, existing AI solutions often fall short due to their narrow focus on technical factors and limited capacity for discretionary judgment. These limitations hinder their ability to adapt to new data in real-time and accurately assess risks, which diminishes their practical value for investors. This paper presents FinRobot, the first AI agent framework specifically designed for equity research. FinRobot employs a multi-agent Chain of Thought (CoT) system, integrating both quantitative and qualitative analyses to emulate the comprehensive reasoning of a human analyst. The system is structured around three specialized agents: the Data-CoT Agent, which aggregates diverse data sources for robust financial integration; the Concept-CoT Agent, which mimics an analysts reasoning to generate actionable insights; and the Thesis-CoT Agent, which synthesizes these insights into a coherent investment thesis and report. FinRobot provides thorough company analysis supported by precise numerical data, industry-appropriate valuation metrics, and realistic risk assessments. Its dynamically updatable data pipeline ensures that research remains timely and relevant, adapting seamlessly to new financial information. Unlike existing automated research tools, such as CapitalCube and Wright Reports, FinRobot delivers insights comparable to those produced by major brokerage firms and fundamental research vendors. We open-source FinRobot at \url{https://github. com/AI4Finance-Foundation/FinRobot}.

Financial documents are filled with specialized terminology, arcane jargon, and curious acronyms that pose challenges for general-purpose text embeddings. Yet, few text embeddings specialized for finance have been reported in the literature, perhaps in part due to a lack of public datasets and benchmarks. We present BAM embeddings, a set of text embeddings finetuned on a carefully constructed dataset of 14.3M query-passage pairs. Demonstrating the benefits of domain-specific training, BAM embeddings achieve Recall@1 of 62.8% on a held-out test set, vs. only 39.2% for the best general-purpose text embedding from OpenAI. Further, BAM embeddings increase question answering accuracy by 8% on FinanceBench and show increased sensitivity to the finance-specific elements that are found in detailed, forward-looking and company and date-specific queries. To support further research we describe our approach in detail, quantify the importance of hard negative mining and dataset scale.

According to The Exchange Act, 1934 unlawful insider trading is the abuse of access to privileged corporate information. While a blurred line between "routine" the "opportunistic" insider trading exists, detection of strategies that insiders mold to maneuver fair market prices to their advantage is an uphill battle for hand-engineered approaches. In the context of detailed high-dimensional financial and trade data that are structurally built by multiple covariates, in this study, we explore, implement and provide detailed comparison to the existing study (Deng et al. (2019)) and independently implement automated end-to-end state-of-art methods by integrating principal component analysis to the random forest (PCA-RF) followed by a standalone random forest (RF) with 320 and 3984 randomly selected, semi-manually labeled and normalized transactions from multiple industry. The settings successfully uncover latent structures and detect unlawful insider trading. Among the multiple scenarios, our best-performing model accurately classified 96.43 percent of transactions. Among all transactions the models find 95.47 lawful as lawful and $98.00$ unlawful as unlawful percent. Besides, the model makes very few mistakes in classifying lawful as unlawful by missing only 2.00 percent. In addition to the classification task, model generated Gini Impurity based features ranking, our analysis show ownership and governance related features based on permutation values play important roles. In summary, a simple yet powerful automated end-to-end method relieves labor-intensive activities to redirect resources to enhance rule-making and tracking the uncaptured unlawful insider trading transactions. We emphasize that developed financial and trading features are capable of uncovering fraudulent behaviors.

Apple reported strong demand for the iPhone 16 in its quarterly earnings report on Thursday, though overall sales in China slightly decreased year-over-year. The company reported 94.9bn in revenue, up 6% year-over-year, and 1.64 in earnings per share (EPS). The company's earnings slightly beat Wall Street projections of 94.4bn in sales and an EPS of 1.60. The company saw 46.2bn in revenue from iPhone sales, up from 43.8bn year-over-year. Fourth-quarter revenue from its services division, which include subscriptions, increased from 22.31bn to 24.97bn year-over-year.

Meta's blowout year continues after the company reported another stellar financial quarter on Wednesday. But shares fell in after-hours trading after the company missed Wall Street expectations for daily active users. Wall Street analysts had high expectations for the Instagram and WhatsApp parent company, projecting an 18% jump in sales year over year. The company reported 40.6bn in sales, a 19% increase year over year that outpaced investor expectations of 40.19bn. Meta, which saw a 25% jump in its share price over the past two months, reported 6.03 in earnings per share (EPS), surpassing Wall Street's expectations of an EPS of 5.29.

Microsoft reported better-than-expected earnings on Wednesday fueled by growth in its Azure cloud business, as five of the "Magnificent Seven" tech megacaps roll out quarterly earnings this week. "AI-driven transformation is changing work, work artifacts, and workflow across every role, function, and business process," the company's CEO, Satya Nadella, said in a press release. "We are expanding our opportunity and winning new customers as we help them apply our AI platforms and tools to drive new growth and operating leverage." All eyes were on Azure, Microsoft's fastest-growing division that has received billions of dollars of investment as the company focuses attention on artificial intelligence. Revenue from the division increased by 22%, according to a press release. A day earlier, Google's parent, Alphabet, reported that its cloud business grew nearly 35% from a year earlier to 11.35bn, beating analyst estimates.