Generative AI, particularly large language models (LLMs), is beginning to transform the financial industry by automating tasks and helping to make sense of complex financial information. One especially promising use case is the automatic creation of fundamental analysis reports, which are essential for making informed investment decisions, evaluating credit risks, guiding corporate mergers, etc. While LLMs attempt to generate these reports from a single prompt, the risks of inaccuracy are significant. Poor analysis can lead to misguided investments, regulatory issues, and loss of trust. Existing financial benchmarks mainly evaluate how well LLMs answer financial questions but do not reflect performance in real-world tasks like generating financial analysis reports. In this paper, we propose FinAR-Bench, a solid benchmark dataset focusing on financial statement analysis, a core competence of fundamental analysis. To make the evaluation more precise and reliable, we break this task into three measurable steps: extracting key information, calculating financial indicators, and applying logical reasoning. This structured approach allows us to objectively assess how well LLMs perform each step of the process. Our findings offer a clear understanding of LLMs current strengths and limitations in fundamental analysis and provide a more practical way to benchmark their performance in real-world financial settings.

In recent years, the application of generative artificial intelligence (GenAI) in financial analysis and investment decision-making has gained significant attention. However, most existing approaches rely on single-agent systems, which fail to fully utilize the collaborative potential of multiple AI agents. In this paper, we propose a novel multi-agent collaboration system designed to enhance decision-making in financial investment research. The system incorporates agent groups with both configurable group sizes and collaboration structures to leverage the strengths of each agent group type. By utilizing a sub-optimal combination strategy, the system dynamically adapts to varying market conditions and investment scenarios, optimizing performance across different tasks. We focus on three sub-tasks: fundamentals, market sentiment, and risk analysis, by analyzing the 2023 SEC 10-K forms of 30 companies listed on the Dow Jones Index. Our findings reveal significant performance variations based on the configurations of AI agents for different tasks. The results demonstrate that our multi-agent collaboration system outperforms traditional single-agent models, offering improved accuracy, efficiency, and adaptability in complex financial environments. This study highlights the potential of multi-agent systems in transforming financial analysis and investment decision-making by integrating diverse analytical perspectives.

This paper investigates the application of machine learning models, Long Short-Term Memory (LSTM), one-dimensional Convolutional Neural Networks (1D CNN), and Logistic Regression (LR), for predicting stock trends based on fundamental analysis. Unlike most existing studies that predominantly utilize technical or sentiment analysis, we emphasize the use of a company's financial statements and intrinsic value for trend forecasting. Using a dataset of 269 data points from publicly traded companies across various sectors from 2019 to 2023, we employ key financial ratios and the Discounted Cash Flow (DCF) model to formulate two prediction tasks: Annual Stock Price Difference (ASPD) and Difference between Current Stock Price and Intrinsic Value (DCSPIV). These tasks assess the likelihood of annual profit and current profitability, respectively. Our results demonstrate that LR models outperform CNN and LSTM models, achieving an average test accuracy of 74.66% for ASPD and 72.85% for DCSPIV. This study contributes to the limited literature on integrating fundamental analysis into machine learning for stock prediction, offering valuable insights for both academic research and practical investment strategies. By leveraging fundamental data, our approach highlights the potential for long-term stock trend prediction, supporting portfolio managers in their decision-making processes.

Vivek Astvansh explains how ChatGPT works and believes ChatGPT has the potential to replace human beings whose job is to refer to volumes of information contained on the internet, in textbooks, or in memory, and produce information based on that available content. Astvansh is an Assistant Professor in the Department of Marketing at the Kelley School of Business at Indiana University and an Adjunct Professor of Data Science at the Luddy School of Informatics, Computing, and Engineering at Indiana University. Don't Miss: Valley of Hype: The culture that built Elizabeth Holmes WATCH HERE: About Yahoo Finance: At Yahoo Finance, you get free stock quotes, up-to-date news, portfolio management resources, international market data, social interaction and mortgage rates that help you manage your financial life. Yahoo Finance Plus: With a subscription to Yahoo Finance Plus get the tools you need to invest with confidence. Discover new opportunities with expert research and investment ideas backed by technical and fundamental analysis.

To understand the application of Artificial Intelligence in capital markets, we must first dive into the definition of Artificial Intelligence. Artificial Intelligence is intelligence developed inside the machines with the use of huge datasets and training models with the help of which, the machine in return, helps find out hidden patterns and gives predictions based upon the inference. Artificial Intelligence is a valuable tool with the help of which manual labor as well time could be saved and if applied correctly, can provide exceptional results. What drives the price of an asset? Irrespective of the market, be it a capital market, commodity market, or forex market, the factors that determine the prices are common to all.

Artificial Intelligence is the behaviour or rules followed by or created by machines to imitate human or animal intelligence. There are several scenarios where one might use artificial intelligence for problem solving or various tasks. Artificial intelligence can be achieved through machine learning. Machine learning is the process of achieving artificial intelligence in a computer system either by supervision or learning itself. The 2 types of artificial intelligence are a rule-based model that simply follows instructions given to it and a machine learning model that trains on useful data first before predicting future results or solving problems.

Have you seen talented traders in the movies do their calculations in their minds or actively record their recent trades while tracking the real market movements? Such "traditions" are gradually becoming obsolete and disappearing in trading. Now those who strive to be on the wave of progress and make even higher profit ask mathematicians, programmers and analysts for help. Recent research in stock trading shows that traders who use outdated automation techniques are watching a drop in earnings. At the same time, those who use artificial intelligence (AI) for trading and analytics, like the trading company M8Trade, are getting results significantly higher than the market average.

When we are investing in the market, we are all looking for an'edge'. As the markets are a zero-sum game, an edge is an unfair advantage that we believe will land us on the right side of a decision. This edge (whether real or perceived) can be your analysis of stocks, trusting the right expert, some intuition, technical analysis or using technology. The edge helps you be right a little more than 50 percent of the time, like a loaded coin in a coin toss that shows heads 51 out of 100 times. The only constant thing is that the edge is dynamic.

Company: Big Data Federation, Inc uses machine-learning algorithms, big data, and fundamental analysis to uncover patterns and precise drivers of performance to predict financial and economic events. This approach underpins our SaaS Enterprise solution which is designed to support companies in planning, protecting, and growing their businesses. This new platform provides real-time data on a company and its ecosystem, as well as predictions several quarters out. These predictions are also used to generate company-specific insights into how to capitalize on emerging end market opportunities and manage latent threats. As a venture-backed start up, we are seeking enthusiastic new members to join our team of technologists, mathematicians, data scientists, and programmers.

Jon Ball, the chief investment officer of the Intrinsic Asset Fund, expect the Dow Jones Industrial Average to reach 30,000 in 18 months. As the bull market passed its ninth birthday in March, it became the longest and greatest in terms of percentage gains for the Dow Jones Industrial Average since World War II, according the Leuthold Group. Since business cycles end and bull markets turn into bears, where do we stand now? Is there more room to grow? "I think the Dow is going to 30,000 in the next 18 months," said Jon Ball, chief investment officer of the Intrinsic Asset Fund, a West Palm Beach, Fla., hedge fund.