While automatic summarization techniques have made significant advancements, their primary focus has been on summarizing short news articles or documents that have clear structural patterns like scientific articles or government reports. There has not been much exploration into developing efficient methods for summarizing financial documents, which often contain complex facts and figures. Here, we study the problem of bullet point summarization of long Earning Call Transcripts (ECTs) using the recently released ECTSum dataset. We leverage an unsupervised question-based extractive module followed by a parameter efficient instruction-tuned abstractive module to solve this task. Our proposed model FLAN-FinBPS achieves new state-of-the-art performances outperforming the strongest baseline with 14.88% average ROUGE score gain, and is capable of generating factually consistent bullet point summaries that capture the important facts discussed in the ECTs.

E-commerce titan Amazon on Tuesday said profit in the first three months of 2024 tripled as its cloud, ads, and retail businesses thrived. Amazon shares were up about 1% in after-market trades that followed the release of the earnings figures, with Wall Street keeping a close eye on the impact of AI as well as the costs involved. "It was a good start to the year across the business," Amazon chief executive Andy Jassy said in an earnings release.

In the realm of financial analytics, leveraging unstructured data, such as earnings conference calls (ECCs), to forecast stock performance is a critical challenge that has attracted both academics and investors. While previous studies have used deep learning-based models to obtain a general view of ECCs, they often fail to capture detailed, complex information. Our study introduces a novel framework: \textbf{ECC Analyzer}, combining Large Language Models (LLMs) and multi-modal techniques to extract richer, more predictive insights. The model begins by summarizing the transcript's structure and analyzing the speakers' mode and confidence level by detecting variations in tone and pitch for audio. This analysis helps investors form an overview perception of the ECCs. Moreover, this model uses the Retrieval-Augmented Generation (RAG) based methods to meticulously extract the focuses that have a significant impact on stock performance from an expert's perspective, providing a more targeted analysis. The model goes a step further by enriching these extracted focuses with additional layers of analysis, such as sentiment and audio segment features. By integrating these insights, the ECC Analyzer performs multi-task predictions of stock performance, including volatility, value-at-risk (VaR), and return for different intervals. The results show that our model outperforms traditional analytic benchmarks, confirming the effectiveness of using advanced LLM techniques in financial analytics.

Google's parent company has hit a stock market value of 2tn ( 1.6tn) as investors reacted to a declaration of its first ever dividend alongside strong results on Thursday. Shares in Alphabet rose 10% in early Wall Street trading on Friday to give the tech group a stock market capitalisation – a measure of a corporation's value – of more than 2tn. Alphabet last hit that level in intraday trading in 2021, but has yet to close above that benchmark after a day's trading. Alphabet's shares rose after it posted results on Thursday that exceeded analyst's expectations. Microsoft also reported strong figures on Thursday, amid heavy investment in artificial intelligence, and investors pushed the company past the 3tn mark, a level it has already crossed this year.

Elon Musk is fighting many battles right now: Against a Brazilian Supreme Court judge, the Australian Prime Minister, Don Lemon, OpenAI, and a nonprofit watchdog, to name a few. But Musk says that he's now spending the majority of his work time on one of his oldest ventures: Tesla. And Tesla badly needs help. The carmaker released its quarterly earnings report yesterday and revealed that its profits fell 55% and revenue fell 9%--figures even worse than many analysts had anticipated. The company announced its intentions to lay off more than 10% of its staff, or about 14,000 people, including major cuts in California and Texas.

M&A activities are pivotal for market consolidation, enabling firms to augment market power through strategic complementarities. Existing research often overlooks the peer effect, the mutual influence of M&A behaviors among firms, and fails to capture complex interdependencies within industry networks. Common approaches suffer from reliance on ad-hoc feature engineering, data truncation leading to significant information loss, reduced predictive accuracy, and challenges in real-world application. Additionally, the rarity of M&A events necessitates data rebalancing in conventional models, introducing bias and undermining prediction reliability. We propose an innovative M&A predictive model utilizing the Temporal Dynamic Industry Network (TDIN), leveraging temporal point processes and deep learning to adeptly capture industry-wide M&A dynamics. This model facilitates accurate, detailed deal-level predictions without arbitrary data manipulation or rebalancing, demonstrated through superior evaluation results from M&A cases between January 1997 and December 2020. Our approach marks a significant improvement over traditional models by providing detailed insights into M&A activities and strategic recommendations for specific firms.

Elon Musk, who sued OpenAI for violating its non-profit mission and chasing profits, allegedly wanted the organization to merge with Tesla when it was starting to plan its transition into a for-profit entity in order to accomplish its goals. Well, either that or get full control of the company, OpenAI said in a blog post. The organization responded to Musk's lawsuit by publishing old emails from 2015 to 2018 when he was still involved in its operations. When OpenAI introduced itself to the world back in 2015, it announced that it had 1 billion in funding. Apparently, Musk was the one who suggested that figure, even though OpenAI had raised less than 45 million from him and around 90 million from other donors.

In this study, ChatGPT is utilized to create streamlined models that generate easily interpretable features. These features are then used to evaluate financial outcomes from earnings calls. We detail a training approach that merges knowledge distillation and transfer learning, resulting in lightweight topic and sentiment classification models without significant loss in accuracy. These models are assessed through a dataset annotated by experts. The paper also delves into two practical case studies, highlighting how the generated features can be effectively utilized in quantitative investing scenarios.

Ten years, billions of dollars, multiple leadership changes, and dozens of rumors later, the Apple Car project is dead. A new report from Bloomberg's Mark Gurman says that Apple has officially canceled the car, breaking the news to nearly 2,000 employees who had been working on it on Tuesday. As part of the change, Apple will move "many employees working on the car" to the company's artificial intelligence division where they will focus on generative AI projects, which Apple is expected to share more about later this year, according to a statement by CEO Tim Cook on the company's earnings call earlier this month. But the car team also included hundreds of hardware engineers and car designers, some of who, Bloomberg reports, will be able to apply for jobs in other divisions of the company. The rest are likely to be laid off.

Large Language Models (LLMs) like ChatGPT and GPT-4 have demonstrated impressive proficiency in comprehending and generating natural language. However, they encounter difficulties when tasked with adapting to specialized domains such as accounting. To address this challenge, we introduce Kuaiji, a tailored Accounting Large Language Model. Kuaiji is meticulously fine-tuned using the Baichuan framework, which encompasses continuous pre-training and supervised fine-tuning processes. Supported by CAtAcctQA, a dataset containing large genuine accountant-client dialogues, Kuaiji exhibits exceptional accuracy and response speed. Our contributions encompass the creation of the first Chinese accounting dataset, the establishment of Kuaiji as a leading open-source Chinese accounting LLM, and the validation of its efficacy through real-world accounting scenarios.