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.

US tech stocks have catapulted the Nasdaq to a record high as investors bet on strong earnings from corporate heavyweights. The tech-heavy Nasdaq Composite Index rose 0.8 percent on Tuesday, as Google's parent company Alphabet reported forecast-beating earnings for the third quarter. Alphabet's revenue jumped 15 percent to 88.3bn during the July-September period, while profit surged 34 percent to 26.3 bn. Google and Alphabet CEO Sundar Pichai said the company was experiencing "extraordinary" momentum due to the strong performance of its search and cloud businesses as well as its focus on innovation, including artificial intelligence. "Our commitment to innovation, as well as our long-term focus and investment in AI, are paying off and driving success for the company and for our customers," Pichai said on an earnings call.

Alphabet, parent of Google and YouTube, saw a third straight quarter of better-than-anticipated gains as it reported earnings on Tuesday. The tech giant had largely exceeded analyst expectations for the previous two quarters, and Tuesday's results showed growth in both digital advertising and demand for Google Cloud. Shares rose in after-hours training. "The momentum across the company is extraordinary. Our commitment to innovation, as well as our long-term focus and investment in AI, are paying off with consumers and partners benefiting from our AI tools," said the CEO, Sundar Pichai. Analysts expected 12% year-on-year revenue growth, to 86.23bn, and earnings per share of 1.85.

Fact-checking is extensively studied in the context of misinformation and disinformation, addressing objective inaccuracies. However, a softer form of misinformation involves responses that are factually correct but lack certain features such as clarity and relevance. This challenge is prevalent in formal Question-Answer (QA) settings such as press conferences in finance, politics, sports, and other domains, where subjective answers can obscure transparency. Despite this, there is a lack of manually annotated datasets for subjective features across multiple dimensions. To address this gap, we introduce SubjECTive-QA, a human annotated dataset on Earnings Call Transcripts' (ECTs) QA sessions as the answers given by company representatives are often open to subjective interpretations and scrutiny. The dataset includes 49,446 annotations for long-form QA pairs across six features: Assertive, Cautious, Optimistic, Specific, Clear, and Relevant. These features are carefully selected to encompass the key attributes that reflect the tone of the answers provided during QA sessions across different domain. Our findings are that the best-performing Pre-trained Language Model (PLM), RoBERTa-base, has similar weighted F1 scores to Llama-3-70b-Chat on features with lower subjectivity, such as Relevant and Clear, with a mean difference of 2.17% in their weighted F1 scores. The models perform significantly better on features with higher subjectivity, such as Specific and Assertive, with a mean difference of 10.01% in their weighted F1 scores. Furthermore, testing SubjECTive-QA's generalizability using QAs from White House Press Briefings and Gaggles yields an average weighted F1 score of 65.97% using our best models for each feature, demonstrating broader applicability beyond the financial domain. SubjECTive-QA is publicly available under the CC BY 4.0 license

Tesla shares saw an 8% jump after reporting its third quarter earnings on Wednesday. The electric car manufacturer was able to bounce back from a tough second quarter, beating Wall Street expectations for earnings per share. The company reported an earnings-per-share of 0.72, surpassing investors' projection of 0.60. At the end of the second quarter, Tesla's chief executive, Elon Musk, said the nearly 50% drop in profits was temporary and due to difficulty competing with cheaper or price-slashed electric vehicles by rival companies such as BYD. "We don't see this as a long-term issue," Musk said in July, "but really fairly short term."