Financial performance management is at the core of business management and has historically relied on financial ratio analysis using Balance Sheet and Income Statement data to assess company performance as compared with competitors. Little progress has been made in predicting how a company will perform or in assessing the risks (probabilities) of financial underperformance. In this study I introduce a new set of financial and macroeconomic ratios that supplement standard ratios of Balance Sheet and Income Statement. I also provide a set of supervised learning models (ML Regressors and Neural Networks) and Bayesian models to predict company performance. I conclude that the new proposed variables improve model accuracy when used in tandem with standard industry ratios. I also conclude that Feedforward Neural Networks (FNN) are simpler to implement and perform best across 6 predictive tasks (ROA, ROE, Net Margin, Op Margin, Cash Ratio and Op Cash Generation); although Bayesian Networks (BN) can outperform FNN under very specific conditions. BNs have the additional benefit of providing a probability density function in addition to the predicted (expected) value. The study findings have significant potential helping CFOs and CEOs assess risks of financial underperformance to steer companies in more profitable directions; supporting lenders in better assessing the condition of a company and providing investors with tools to dissect financial statements of public companies more accurately.

Large language models (LLM), based on generative pre-trained transformers (GPT), such as ChatGPT show high efficiency in the analysis of complex texts. These days, we can observe the emerging of many new smaller open source LLMs, e.g. Llama, Falcon, GPT4All, GPT-J, etc. Open source LLMs can be fine-tuned for specific custom problems and deployed on custom servers, e.g. in cloud computing services such as AWS, GCP. LLMs have some new features as compared to conventional language models based on transformers. One of them is zero-shot and few-shot learning, which consists in good performance of the model when we show it only few training examples or even no examples at all, but only the instructions describing what should be done. Another important feature is the reasoning when a model can generate new patterns and conclusions which are based on an input prompt and facts known by the model and which were not included into it directly during a training process. So, the model can generate analytical texts with unexpected but useful chains of thoughts. One of the approaches of using LLMs is based on retrieval augmented generation (RAG), which uses the results from other services e.g.

The stock market darling on everyone's lips is Nvidia, which makes the processing chips that power everything from home computers to industrial machinery to cutting-edge artificial intelligence technology. On Thursday, the company stunned Wall Street with results that blew the roof off analysts' expectations, reporting $13.5bn in quarterly profits, $2bn higher than pundits had predicted. Its performance is being driven in particular by the AI boom, which has tripled the value of its shares this year and given it a market value of more than $1tn. California-based Nvidia is one of just five companies to have reached that milestone – along with Apple, Amazon, Microsoft and Google's owner, Alphabet – and the only one that isn't a household name. So why are its chips so hot, and what does the future hold?

Chip designer Nvidia has forecast third-quarter revenue above Wall Street targets and said it will buy back $25bn more of its shares as sales benefit from soaring demand for its chips that power nearly all the world's major artificial intelligence apps. Shares of the Santa Clara, California-based company rose 8 percent in trading after the bell, hitting an all-time high. Nvidia's forecast on Wednesday beat expectations by billions of dollars, demonstrating that a boom in generative AI technologies that can read and write in human-like ways – and powered almost exclusively by Nvidia's chips – shows no signs of slowing down. Nvidia's additional $25bn in share repurchases come as shares have already tripled this year, making the company the first-ever trillion-dollar chip business as investors bet Nvidia will be the key beneficiary of the AI boom. Analysts have estimated that demand for Nvidia's prized AI chips is exceeding supply by at least 50 percent, adding that the imbalance will stay in place for the next several quarters.

The advent of large language models (LLMs) and their adoption by the legal community has given rise to the question: what types of legal reasoning can LLMs perform? To enable greater study of this question, we present LegalBench: a collaboratively constructed legal reasoning benchmark consisting of 162 tasks covering six different types of legal reasoning. LegalBench was built through an interdisciplinary process, in which we collected tasks designed and hand-crafted by legal professionals. Because these subject matter experts took a leading role in construction, tasks either measure legal reasoning capabilities that are practically useful, or measure reasoning skills that lawyers find interesting. To enable cross-disciplinary conversations about LLMs in the law, we additionally show how popular legal frameworks for describing legal reasoning -- which distinguish between its many forms -- correspond to LegalBench tasks, thus giving lawyers and LLM developers a common vocabulary. This paper describes LegalBench, presents an empirical evaluation of 20 open-source and commercial LLMs, and illustrates the types of research explorations LegalBench enables.

Apple boss Tim Cook announced the company was investing heavily in artificial intelligence (AI) on Thursday as the company announced its third consecutive quarter of declining revenues, the company's most prolonged sales slump since 2016. Apple's sales for the fiscal third quarter ending 1 July fell 1.4% to $81.8bn. Over the quarter the company made a profit of $19.9bn, higher than analysts had expected. IPhone sales slightly missed analyst estimates, but were made up for by strong sales in the services segment that contains Apple TV and by sales in China that grew 8% year over year. Apple shares were flat in extended trading after the results.

Nintendo just announced its highest first quarter profit ever thanks to sales of The Legend of Zelda: Breath of the Wild and The Super Mario Bros. Movie. The company earned 185.44 billion yen ($1.3 billion) on sales of 461.34 billion yen ($3.2 billion), easily battering its previous fiscal Q1 record of 144.7 billion set in 2020, the company revealed in its latest earnings report. The numbers on those two properties are impressive. Around the world, 168.10 million people watched The Super Mario Bros. Movie, netting the company $1.349 billion as of July 26th -- the highest ever for an original film based on a video game, and the second-highest for an animated film. Meanwhile, The Legend of Zelda: Tears of the Kingdom has sold 18.51 million copies since it launched in May, while Mario Kart 8 Deluxe sold 1.67 million units last quarter. "Sell-through of this one title [Zelda] constitutes approximately half of the first-party software sold this fiscal year," Nintendo said.

Due to complexity and dynamics of construction work, resource, and cash flows, poor management of them usually leads to time and cost overruns, bankruptcy, even project failure. Existing approaches in construction failed to achieve optimal control of resource flow in a dynamic environment with uncertainty. Therefore, this paper introducess a model and method to adaptive control the resource flows to optimize the work and cash flows of construction projects. First, a mathematical model based on a partially observable Markov decision process is established to formulate the complex interactions of construction work, resource, and cash flows as well as uncertainty and variability of diverse influence factors. Meanwhile, to efficiently find the optimal solutions, a deep reinforcement learning (DRL) based method is introduced to realize the continuous adaptive optimal control of labor and material flows, thereby optimizing the work and cash flows. To assist the training process of DRL, a simulator based on discrete event simulation is also developed to mimic the dynamic features and external environments of a project. Experiments in simulated scenarios illustrate that our method outperforms the vanilla empirical method and genetic algorithm, possesses remarkable capability in diverse projects and external environments, and a hybrid agent of DRL and empirical method leads to the best result. This paper contributes to adaptive control and optimization of coupled work, resource, and cash flows, and may serve as a step stone for adopting DRL technology in construction project management.

Stock selection is important for investors to construct profitable portfolios. Graph neural networks (GNNs) are increasingly attracting researchers for stock prediction due to their strong ability of relation modelling and generalisation. However, the existing GNN methods only focus on simple pairwise stock relation and do not capture complex higher-order structures modelling relations more than two nodes. In addition, they only consider factors of technical analysis and overlook factors of fundamental analysis that can affect the stock trend significantly. Motivated by them, we propose higher-order graph attention network with joint analysis (H-GAT). H-GAT is able to capture higher-order structures and jointly incorporate factors of fundamental analysis with factors of technical analysis. Specifically, the sequential layer of H-GAT take both types of factors as the input of a long-short term memory model. The relation embedding layer of H-GAT constructs a higher-order graph and learn node embedding with GAT. We then predict the ranks of stock return. Extensive experiments demonstrate the superiority of our H-GAT method on the profitability test and Sharp ratio over both NSDAQ and NYSE datasets

PsychoGenics CEO Emer Leahy of Paramus, New Jersey, explains how the first potential AI-discovered treatment for schizophrenia was developed through machine learning. Fox News Digital spoke with her. In humanity's quest to live longer, healthier lives, technology -- particularly artificial intelligence -- is playing an ever-bigger role and expanding into more areas of health care. A California-based medical technology company named Prenuvo, for instance, offers full-body MRI scans that leverage AI to screen patients for over 500 conditions -- including tumors, aneurysms and cysts -- in less than an hour. Now, Prenuvo is announcing a partnership with Cenegenics, a Las Vegas-based company that offers "personalized performance health age management" for its patients.