Predicting earnings surprises from financial documents, such as earnings conference calls, regulatory filings, and financial news, has become increasingly important in financial economics. However, these financial documents present significant analytical challenges, typically containing over 5,000 words with substantial redundancy and industry-specific terminology that creates obstacles for language models. In this work, we propose the SAE-FiRE (Sparse Autoencoder for Financial Representation Enhancement) framework to address these limitations by extracting key information while eliminating redundancy. SAE-FiRE employs Sparse Autoencoders (SAEs) to decompose dense neural representations from large language models into interpretable sparse components, then applies statistical feature selection methods, including ANOVA F-tests and tree-based importance scoring, to identify the top-k most discriminative dimensions for classification. By systematically filtering out noise that might otherwise lead to overfitting, we enable more robust and generalizable predictions. Experimental results across three financial datasets demonstrate that SAE-FiRE significantly outperforms baseline approaches.

MarketSenseAI is a novel framework for holistic stock analysis which leverages Large Language Models (LLMs) to process financial news, historical prices, company fundamentals and the macroeconomic environment to support decision making in stock analysis and selection. In this paper, we present the latest advancements on MarketSenseAI, driven by rapid technological expansion in LLMs. Through a novel architecture combining Retrieval-Augmented Generation and LLM agents, the framework processes SEC filings and earnings calls, while enriching macroeconomic analysis through systematic processing of diverse institutional reports. We demonstrate a significant improvement in fundamental analysis accuracy over the previous version. Empirical evaluation on S\&P 100 stocks over two years (2023-2024) shows MarketSenseAI achieving cumulative returns of 125.9% compared to the index return of 73.5%, while maintaining comparable risk profiles. Further validation on S\&P 500 stocks during 2024 demonstrates the framework's scalability, delivering a 33.8% higher Sortino ratio than the market. This work marks a significant advancement in applying LLM technology to financial analysis, offering insights into the robustness of LLM-driven investment strategies.

The British engineering company Arup has confirmed it was the victim of a deepfake fraud after an employee was duped into sending HK 200m ( 20m) to criminals by an artificial intelligence-generated video call. Hong Kong police said in February that a worker at a then-unnamed company had been tricked into transferring vast sums by people on a hoax call "posing as senior officers of the company". Arup said in a statement that it was the company involved, confirming that at the beginning of the year it had "notified the police about an incident of fraud in Hong Kong". It confirmed that fake voices and images were used. It added: "Our financial stability and business operations were not affected and none of our internal systems were compromised."

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.

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.

GM is massively slashing spending on its self-driving vehicle subsidiary Cruise after a string of debilitating setbacks, according to a conference call by company executives transcribed by TechCrunch . GM Chair and CEO Mary Barra said that operations would resume in some capacity, but that any plans for Cruise moving forward would be more "deliberate." To that end, the cuts will amount to hundreds of millions of dollars in the next year. This is expected to result in widespread layoffs at the San Francisco-based company that currently employees nearly 4,000 people. Earlier this month, Cruise CEO Kyle Vogt told staffers at an all-hands meeting that he'd have information regarding layoffs in the coming weeks, but he resigned shortly thereafter along with co-founder Dan Kan.

The opening keynote address of the Google I/O developer conference today was stuffed with announcements of new devices and AI-powered features coming to familiar software tools. The company leaned hard into generative computing, loudly characterizing itself as a decades-long leader in AI tech. It also gleefully put AI at the forefront of nearly every service and device it operates, including the new Pixel phones and tablet it unveiled today. Here are all of Google's announcements from I/O 2023. Google hardware chief Rick Osterloh announces the Pixel Fold on stage.

Artificial intelligence is the latest tech flavor of the month. Industry giants Google, Microsoft and China's Baidu have all had big AI announcements in recent days, as ChatGPT bot mania is taking the corporate world by storm. All of this AI news has helped boost shares of Baidu (BIDU), Microsoft (MSFT) and Google owner Alphabet (GOOGL) this year. However, Alphabet (GOOGL) tumbled Wednesday following a rocky demo of Bard, its rival to ChatGPT. Traders have also been bidding up the stocks of much smaller, unprofitable companies that are trying to make a name for themselves in the AI arms race.

Feb 3 (Reuters) - Big Tech companies have a new obsession: artificial intelligence. This week, chief executives across the sector packed earnings calls with mentions of the heavily hyped technology, which until recently existed more in the background than as a solid contributor to the bottom line. In conference calls after financial results, tech execs uttered the phrases "AI," "generative AI," or "machine learning" from two to six times as often as they did in the previous quarter, according to a review of conference transcripts by Reuters. Executives from Microsoft Corp (MSFT.O) and Alphabet Inc (GOOGL.O), behind the latest big rivalry in tech, took their battle to the conference-call front lines. On Thursday, Alphabet appeared to edge out the competition.

The sky is no longer the limit for cloud computing giants, as the slowing economy strikes at one of tech's biggest growth stories of the past decade. Two of the top cloud providers, Amazon Web Services Inc. and Google LLC, today revealed revenues in their cloud units, which have boosted their growth and profits for years now. The results weren't bad -- but cloud is no longer on the kind of rocket ride that can overcome big slowdowns in e-commerce and ad spending. Alphabet's Google Cloud unit posted a 33% rise in its fourth quarter, to $7.315 billion, with an operating loss of $480 million, down significantly from a loss of $890 million a year ago. Zacks Consensus Forecast had Google Cloud revenue rising 32% from a year ago, to $7.3 billion, so it managed to meet expectations but was still down from 38% growth in the third quarter.