The field of artificial intelligence (AI) agents is evolving rapidly, driven by the capabilities of Large Language Models (LLMs) to autonomously perform and refine tasks with human-like efficiency and adaptability. In this context, multi-agent collaboration has emerged as a promising approach, enabling multiple AI agents to work together to solve complex challenges. This study investigates the application of role-based multi-agent systems to support stock selection in equity research and portfolio management. We present a comprehensive analysis performed by a team of specialized agents and evaluate their stock-picking performance against established benchmarks under varying levels of risk tolerance. Furthermore, we examine the advantages and limitations of employing multi-agent frameworks in equity analysis, offering critical insights into their practical efficacy and implementation challenges.

Tech companies like to make two grand pronouncements about the future of artificial intelligence. First, the technology is going to usher in a revolution akin to the advent of fire, nuclear weapons, and the internet. And second, it is going to cost almost unfathomable sums of money. Silicon Valley has already triggered tens or even hundreds of billions of dollars of spending on AI, and companies only want to spend more. Their reasoning is straightforward: These companies have decided that the best way to make generative AI better is to build bigger AI models.

We initiate a novel approach to explain the out of sample performance of random forest (RF) models by exploiting the fact that any RF can be formulated as an adaptive weighted K nearest-neighbors model. Specifically, we use the proximity between points in the feature space learned by the RF to re-write random forest predictions exactly as a weighted average of the target labels of training data points. This linearity facilitates a local notion of explainability of RF predictions that generates attributions for any model prediction across observations in the training set, and thereby complements established methods like SHAP, which instead generates attributions for a model prediction across dimensions of the feature space. We demonstrate this approach in the context of a bond pricing model trained on US corporate bond trades, and compare our approach to various existing approaches to model explainability.

For a financial analyst, the question and answer (Q\&A) segment of the company financial report is a crucial piece of information for various analysis and investment decisions. However, extracting valuable insights from the Q\&A section has posed considerable challenges as the conventional methods such as detailed reading and note-taking lack scalability and are susceptible to human errors, and Optical Character Recognition (OCR) and similar techniques encounter difficulties in accurately processing unstructured transcript text, often missing subtle linguistic nuances that drive investor decisions. Here, we demonstrate the utilization of Large Language Models (LLMs) to efficiently and rapidly extract information from earnings report transcripts while ensuring high accuracy transforming the extraction process as well as reducing hallucination by combining retrieval-augmented generation technique as well as metadata. We evaluate the outcomes of various LLMs with and without using our proposed approach based on various objective metrics for evaluating Q\&A systems, and empirically demonstrate superiority of our method.

Investing has long been a complex and often overwhelming process, requiring extensive research, analysis, and decision-making. However, with the rise of artificial intelligence (AI), there has been growing interest in using AI algorithms to manage investments. Can AI be the investment manager of the future? Let's explore the research and real-time experiments to find out. According to a study published in the Journal of Banking and Finance in 2020, robo-advisors, or AI-powered investment management services, can provide cost-effective and personalized investment advice to clients, particularly for those with limited investment knowledge or smaller portfolios.

According to a recent PwC U.S. CEO survey, 84% of CEOs plan to increase their investment in digital transformation.1 This investment is especially important in the financial services (FinServ) industry, where legacy systems often impede the smooth and speedy flow of data that is necessary for transaction processing. According to a recent Economist Intelligence Unit report sponsored by Snowflake, "The persistence of data silos puts a unified view of data out of reach for many FinServ firms. And that, in turn, makes it hard for them to achieve strategic goals such as offering individual and institutional customers a personalized experience across departments, channels, and touchpoints; meeting global regulatory standards; detecting and protecting against risk and fraud; and increasing overall operational efficiency." Snowflake's Data Cloud equips banks, brokerages, insurers, and financial technology startups with the power of unified data that is easy to share securely.

Prominent venture capitalist Peter Thiel today announced he has invested in a company that's rivaling Elon Musk's Neuralink Corp. in the emerging brain-machine interface technology space. The co-founder of Palantir Technologies Inc., who was also an early backer of Facebook Inc. and founded PayPal Inc. with Musk back in 1998, is backing a company called Blackrock Neurotech in a $10 million funding round. Christian Angermayer's re.Mind Capital led the round, with Thiel, German entrepreneur Tim Sievers and Sorenson Impact's University Venture Fund II also participating. Blackrock, owned by its parent company Blackrock Microsystems, LLC, was founded in 2008 and is based in Salt Lake City. It has been in the business of neuroscience hardware and software for more than a decade.

Humans have always welcomed other beings in finance: over twenty years ago, some of the best Wall Street traders were outsmarted by Raven, a chimpanzee who picked stocks by throwing darts. Her index, called MonkeyDex, became one of the biggest sensations at the turn of the century after delivering a 213% gain. Perhaps because animals are not so easy to fit in offices, people have turned to other kinds of brains to choose equities. Big institutions are resorting to artificial intelligence (AI) to analyse stocks collating all sorts of information coming from a plethora of sources. In fact, while investments could previously be assessed based on financial reports and share price movement – what is called structured data – markets have been heavily influenced by unstructured data over the past few years.

The late economist Hyman Minsky was a pioneer in understanding finance's grip on the US economy – and the consequences for society. In the 1980s, he predicted the rise of "money manager capitalism" and foresaw that institutional investors would become masters of the universe. Today, we are in a world of "money machine manager capitalism", where algorithms control the buying and selling of securities. Those paid to pick shares, mindful perhaps that their sales pitch was being undermined, claim such passive investing is "worse than Marxism". The rise of the robots has been undeterred by such criticism. The pioneer of this approach is the US firm BlackRock, which is the world's largest asset manager and last year became Britain's biggest one too.

From the shiny corridors of BlackRock's Palo Alto laboratory, to the cramped shared workspaces of scientifically minded hedge fund start-ups, to the hallways of quantitative investing stalwarts such as Renaissance Technologies and Two Sigma, artificial intelligence (AI) is being adopted as the new temple of asset management. Even discretionary managers are starting to bring in data scientists and machine learning experts. Most attempts to apply AI so far have been in stock price forecasting. But risk managers are asking how the technology can be harnessed in their domain also. One area of exploration is the use of machine learning to replace traditional approaches to risk modelling.