The aim of this paper is the analysis and selection of stock trading systems that combine different models with data of different nature, such as financial and microeconomic information. Specifically, based on previous work by the authors and applying advanced techniques of Machine Learning and Deep Learning, our objective is to formulate trading algorithms for the stock market with empirically tested statistical advantages, thus improving results published in the literature. Our approach integrates Long Short-Term Memory (LSTM) networks with algorithms based on decision trees, such as Random Forest and Gradient Boosting. While the former analyze price patterns of financial assets, the latter are fed with economic data of companies. Numerical simulations of algorithmic trading with data from international companies and 10-weekday predictions confirm that an approach based on both fundamental and technical variables can outperform the usual approaches, which do not combine those two types of variables. In doing so, Random Forest turned out to be the best performer among the decision trees. We also discuss how the prediction performance of such a hybrid approach can be boosted by selecting the technical variables.

Financial analysis presents complex challenges that could leverage large language model (LLM) capabilities. However, the scarcity of challenging financial datasets, particularly for Japanese financial data, impedes academic innovation in financial analytics. As LLMs advance, this lack of accessible research resources increasingly hinders their development and evaluation in this specialized domain. To address this gap, we introduce EDINET-Bench, an open-source Japanese financial benchmark designed to evaluate the performance of LLMs on challenging financial tasks including accounting fraud detection, earnings forecasting, and industry prediction. EDINET-Bench is constructed by downloading annual reports from the past 10 years from Japan's Electronic Disclosure for Investors' NETwork (EDINET) and automatically assigning labels corresponding to each evaluation task. Our experiments reveal that even state-of-the-art LLMs struggle, performing only slightly better than logistic regression in binary classification for fraud detection and earnings forecasting. These results highlight significant challenges in applying LLMs to real-world financial applications and underscore the need for domain-specific adaptation. Our dataset, benchmark construction code, and evaluation code is publicly available to facilitate future research in finance with LLMs.

Large Language Models (LLMs) have shown remarkable capabilities in general natural language processing tasks but often fall short in complex reasoning tasks. Recent studies have explored human-like problem-solving strategies, such as self-correct, to push further the boundary of single-model reasoning ability. In this work, we let a single model "step outside the box" by engaging multiple models to correct each other. We introduce a multi-agent collaboration strategy that emulates the academic peer review process. Each agent independently constructs its own solution, provides reviews on the solutions of others, and assigns confidence levels to its reviews. Upon receiving peer reviews, agents revise their initial solutions. Extensive experiments on three different types of reasoning tasks show that our collaboration approach delivers superior accuracy across all ten datasets compared to existing methods. Further study underscores the effectiveness of integrating confidence in reviews, demonstrates the superiority of feedback exchange over mere solution sharing, and highlights the role of capability and diversity in fostering successful collaboration.

Machine learning ("ML") has been gaining precedence over the past few years as organizations are rapidly implementing ML solutions to increase efficiency by delivering more accurate results as well as providing a better customer experience. Notably, when it comes to automation, ML has become a driving force as it involves training the Artificial Intelligence ("AI") to learn a task and carry it out efficiently, minimizing the need for human intervention. In any case, ML was already witnessing rapid adoption and the outbreak of the COVID-19 pandemic last year helped in accelerating that demand, as organizations began to rely heavily on automation to carry out their operations. Markedly, ML is gradually becoming an integral part across various sectors as the trend of digitization is picking up. Notably, ML is finding application in the finance sector as among other usages, it helps in better fraud detection and enabling automated trading for investors.

The COVID-19 pandemic has given employers ample reasons to look for ways to substitute man with machine. The shift in trend toward automation has accelerated in the past year. Businesses were suffering huge losses due to the lockdowns, leading to decline in productivity. Artificial intelligence (AI) tools that were perceived to give a competitive edge or a'nice-to-have' technology prior to 2020 became essential for several companies across the globe to stay afloat. All the more, AI has been deployed by several organizations during the pandemic to provide information to the public, when physical interactions were a complete no-no.

The COVID-19 pandemic has given employers ample reasons to look for ways to substitute man with machine. The shift in trend toward automation has accelerated in the past year. Businesses were suffering huge losses due to the lockdowns, leading to decline in productivity. Artificial intelligence (AI) tools that were perceived to give a competitive edge or a'nice-to-have' technology prior to 2020 became essential for several companies across the globe to stay afloat. All the more, AI has been deployed by several organizations during the pandemic to provide information to the public, when physical interactions were a complete no-no.

Since its invention in 1955, Artificial Intelligence (AI) has constantly been studied by several researchers and many big firms have invested billions each year just to accomplish one goal -simplifying life. This simulation of human intelligence process by machines, especially computer systems is the base of AI. With wide applications that include natural language processing (NLP), speech recognition and machine vision, the field has lot more to provide than just navigating people to their final destination. Today, nearly 40% businesses use AI to run their daily course. A survey by IBM, clearly shows that the scale of AI's existence will grow to 80% to 90% over the next 18-24 months. In fact, this report proves the myth wrong that AI is replacing human workforce.

Artificial intelligence (AI) has taken the world by storm. Burly robots and humanoids are common in sci-fi but it would not be too ambitious to say that the human race may see a cyborg infiltration down the line. With the emergence of AI, various sectors such as defense, medical and aerospace are increasingly making use of the technology to boost productivity and cost-effectiveness. This is why investing in AI-related stocks is a prudent decision at this point. An area which is already experiencing a massive usage of machine learning and robotics is the defense sector. Drew Cukor, chief of the U.S. Department of Defense's (DoD) Algorithmic Warfare Cross-Function Team stated on Mar 6, 2018 that the near future is about to observe humans and computers working "symbiotically" in order to increase the efficacy of a weapon system to be able to detect a target and obliterate it.

Artificial intelligence (AI) has taken the world by storm. Burly robots and humanoids are common in sci-fi but it would not be too ambitious to say that the human race may see a cyborg infiltration down the line. With the emergence of AI, various sectors such as defense, medical and aerospace are increasingly making use of the technology to boost productivity and cost-effectiveness. This is why investing in AI-related stocks is a prudent decision at this point. An area which is already experiencing a massive usage of machine learning and robotics is the defense sector. Drew Cukor, chief of the U.S. Department of Defense's (DoD) Algorithmic Warfare Cross-Function Team stated on Mar 6, 2018 that the near future is about to observe humans and computers working "symbiotically" in order to increase the efficacy of a weapon system to be able to detect a target and obliterate it.

Earlier this week, Salesforce (NYSE: CRM) reported its quarterly results, surpassing market expectations. For a while now, Salesforce has been targeting to become the first $10 billion cloud services company. Recent outlook reveals that the milestone may not be too far off now. For the final quarter of 2016, Salesforce's revenue grew 27% over the year to $2.29 billion, above analyst projections of $2.28 billion. EPS of $0.28 was also significantly ahead of the market's forecast of $0.25 for the quarter.