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.

This paper discusses the use of multilayer feed forward neural net(cid:173) works for predicting a stock's excess return based on its exposure to various technical and fundamental factors. To demonstrate the effectiveness of the approach a hedged portfolio which consists of equally capitalized long and short positions is constructed and its historical returns are benchmarked against T-bill returns and the S&P500 index.

Artificial Neural Networks can be used to predict future returns of stocks in order to take financial decisions . Should one build a separate network for each stock or share the same network for all the stocks? In this paper we also explore other alternatives, in which some layers are shared and others are not shared. When the prediction of future returns for different stocks are viewed as different tasks, sharing some parameters across stocks is a form of multi-task learning. In a series of experiments with Canadian stocks, we obtain yearly returns that are more than 14% above various benchmarks.

AI is making strides at many levels in the world of investment management. Investors may already be riding the wave of artificial intelligence, unaware of the many ways they've been integrated. There are three main levels where AI is making a mark, says Amit Gupta, a managing director in Accenture's capital market industry group. At the first level, firms are using AI in back-office administrative tasks like net asset value calculations, reconciliation, settlement operations. At the second level, they use it in front-office tasks like client targeting and management, profiling of clients, personalization of service.

From a perspective on contrastive learning as dictionary look-up, we build a dynamic dictionary with a queue and a moving-averaged encoder. This enables building a large and consistent dictionary on-the-fly that facilitates contrastive unsupervised learning. MoCo provides competitive results under the common linear protocol on ImageNet classification. More importantly, the representations learned by MoCo transfer well to downstream tasks. MoCo can outperform its supervised pre-training counterpart in 7 detection/segmentation tasks on PASCAL VOC, COCO, and other datasets, sometimes surpassing it by large margins. This suggests that the gap between unsupervised and supervised representation learning has been largely closed in many vision tasks.

Big data and machine learning have generated tremendous interest in empirical finance research. In this presentation,Yin examines a unique news analytics database provided by Ravenpack. He applies a suite of innovative machine learning algorithms, including adaBoost, spline regression, and other boosting/bagging techniques on both traditional and unstructured news data in predicting stock returns. He finds news sentiment data adds significant incremental predictive power to his machine learning based global stock selection models. Presentation held at the RavenPack 4th Annual Research Symposium, New York, June 16th 2016.

This Best Energy Stocks forecast is designed for investors and analysts who need predictions of the best performing stocks for the whole Energy Industry (See Industry Package). Package Name: Energy Stocks Recommended Positions: Long Forecast Length: 14 Days (04/11/16– 04/25/16) I Know First Average: 27.11% All 10 top stocks for this forecast from the Energy Stocks package increased as predicted by the algorithm. LGCY was the highest-earning stock, more than doubling in share price, with a return of 105.65% for the 14-day time period. DNR also had a strong week with its return of 58.00% and ERF and PES also performed well with returns of 23.33% and 19.83% respectively.

Artificial Neural Networks can be used to predict future returns of stocks in order to take financial decisions. Should one build a separate network for each stock or share the same network for all the stocks? In this paper we also explore other alternatives, in which some layers are shared and others are not shared. When the prediction of future returns for different stocks are viewed as different tasks, sharing some parameters across stocks is a form of multi-task learning. In a series of experiments with Canadian stocks, we obtain yearly returns that are more than 14% above various benchmarks.

Artificial Neural Networks can be used to predict future returns of stocks in order to take financial decisions. Should one build a separate network for each stock or share the same network for all the stocks? In this paper we also explore other alternatives, in which some layers are shared and others are not shared. When the prediction of future returns for different stocks are viewed as different tasks, sharing some parameters across stocks is a form of multi-task learning. In a series of experiments with Canadian stocks, we obtain yearly returns that are more than 14% above various benchmarks.

Artificial Neural Networks can be used to predict future returns of stocks in order to take financial decisions. Should one build a separate network for each stock or share the same network for all the stocks? In this paper we also explore other alternatives, in which some layers are shared and others are not shared. When the prediction of future returns for different stocks are viewed as different tasks, sharing some parameters across stocks is a form of multi-task learning. In a series of experiments with Canadian stocks, we obtain yearly returns that are more than 14% above various benchmarks. 1 Introd uction Previous applications of ANNs to financial time-series suggest that several of these prediction and decision-taking tasks present sufficient non-linearities to justify the use of ANNs (Refenes, 1994; Moody, Levin and Rehfuss, 1993).