People in the real world often possess vague knowledge of future payoffs, for which quantification is not feasible or desirable. We argue that language, with differing ability to convey vague information, plays an important but less-known role in representing subjective expectations. Empirically, we find that in their reports, analysts include useful information in linguistic expressions but not numerical forecasts. Specifically, the textual tone of analyst reports has predictive power for forecast errors and subsequent revisions in numerical forecasts, and this relation becomes stronger when analyst's language is vaguer, when uncertainty is higher, and when analysts are busier. Overall, our theory and evidence suggest that some useful information is vaguely known and only communicated through language.

This article applies natural language processing (NLP) to extract and quantify textual information to predict stock performance. Using an extensive dataset of Chinese analyst reports and employing a customized BERT deep learning model for Chinese text, this study categorizes the sentiment of the reports as positive, neutral, or negative. The findings underscore the predictive capacity of this sentiment indicator for stock volatility, excess returns, and trading volume. Specifically, analyst reports with strong positive sentiment will increase excess return and intraday volatility, and vice versa, reports with strong negative sentiment also increase volatility and trading volume, but decrease future excess return. The magnitude of this effect is greater for positive sentiment reports than for negative sentiment reports. This article contributes to the empirical literature on sentiment analysis and the response of the stock market to news in the Chinese stock market.

In this paper, we investigate the influence of claims in analyst reports and earnings calls on financial market returns, considering them as significant quarterly events for publicly traded companies. To facilitate a comprehensive analysis, we construct a new financial dataset for the claim detection task in the financial domain. We benchmark various language models on this dataset and propose a novel weak-supervision model that incorporates the knowledge of subject matter experts (SMEs) in the aggregation function, outperforming existing approaches. We also demonstrate the practical utility of our proposed model by constructing a novel measure of optimism. Here, we observe the dependence of earnings surprise and return on our optimism measure. Our dataset, models, and code are publicly (under CC BY 4.0 license) available on GitHub.

A key benefit of using general-purpose processors to implement open RAN/vRAN is that the same platforms can be used to support AI inference and other applications at the far edge of the network, such as cell site routers (CSRs) and content delivery and hosting. These edge platforms can be used to host virtualized applications closer to the user, offering significant benefits in terms of lower latency and shared infrastructure. To find out more about which applications service providers plan to support on shared far edge solutions and how they plan to deploy open RAN and vRAN platforms and architectures for 5G networks, Heavy Reading ran an exclusive survey of individuals working for operators with mobile network businesses. The results are presented in an analyst report, Open RAN Platforms and Architectures Operator Survey Report, that can be downloaded for free here. The survey presented options for five edge applications that can share server platforms with virtualized open RAN baseband implementations.

Enterprise Management Associates (EMA) research shows that leveraging artificial intelligence and machine learning (AI/ML) for DevOps, IT operations management, and business management is the top priority for enterprises in 2018 and beyond. AI/ML brings data-driven intelligence to DevOps, IT operations, and the enterprise to optimize processes, recognize relevant trends, proactively prevent issues, rapidly detect and resolve problems, and enable human staff to make optimal and fact-driven decisions. This EMA "Top 3 Decision Guide for Artificial Intelligence and Machine Learning in DevOps, IT Operations, and Business" provides guidance for enterprises seeking to optimally leverage today's AI/ML capabilities, depending on their individual situation and priorities.

While the overall artificial intelligence (AI) market saw steady growth last year, SAS experienced growth at a rate nearly four times faster than the overall market, at 104.6% according to the IDC report, Worldwide Artificial Intelligence Software Platforms Market Shares, 2018: Steady Growth -- Moving Toward Production1. SAS ranked second overall in 2018 in the AI software platforms category. "SAS had impressive growth in the artificial intelligence market, no doubt as a result of its leadership in analytics," said David Schubmehl, Research Director, Cognitive/Artificial Intelligence Systems at IDC. "As organizations move from experimentation to production in AI to solve their business problems, many are looking for a trusted vendor that offers analytics expertise and domain knowledge. SAS' embedded AI capabilities and integration with open source technologies allows organizations to take advantage of the technology to automate processes, without the heavy lifting of training AI models." To continue fostering innovation and progress in an expanding market, SAS has committed to invest $1 billion in AI over the next three years.

Navigating the market for a customer experience or customer service engagement solution such as a chatbot, virtual assistant, asynchronous messaging platform, or IVR can be confusing. With multiple vendors offering solutions that sound alike, how can you choose the best one for your business? Making customer engagement consistent across all channels is key to maintaining your brand's equity. From the time consumers see your ad or engage with your web content to when they are greeted by your chatbot or escalate to an agent for faster resolution, their journey must be seamless. Your experiences need to delight customers at every stage, on all the channels you support.

Danske Bank is a Nordic financial services company that operates in 16 countries, serving more than 1,800 corporations and institutions, 236,000 small and midsized companies and 2.7 million personal customers. As captured by Doug Henschen, Vice President and Principal Analyst with Constellation Research, this case study describes how Danske Bank partnered with Teradata Think Big Analytics to develop and deploy machine learning models resulting in double-digit improvements in fraud detections and further reductions in false positives within a test environment. Download this paper to discover how Danske Bank has combated financial fraud.

In the early 1990s, established businesses were blindsided by the disruptive force of the Internet. Even those who invested heavily in IT infrastructure soon found that they had difficulty competing with nimble new upstarts who could deploy web based applications at blinding speed. It was with this in mind that IBM launched its e-business initiative in 1996. Although considered by many at the time to be an aging dinosaur itself -- it was close to bankruptcy three years earlier -- it was able to leverage its expertise in legacy systems to transform business processes and prepare its customers for the Internet age. Today a similar tidal wave of disruption is sweeping through the corporate world -- artificial intelligence -- and, once again, IBM sees a big opportunity.

The market is a massive, irrational, and fluid amalgamation of all information available in the public domain, at least according to the efficient market hypothesis. The famous quote, that "the market can remain irrational longer than you can remain solvent," holds significance, because it illustrates the perpetual struggle that we face in trying to understand its inner machinations. The market is both a byproduct of human innovation, as well as a microcosm of the world we live in. Just as we can't definitively know how the market will move, we can't definitively know how the choices we make will affect the world we live in. Therein lays the real challenge, in which we take everything we think we know and make an analytical decision, because afterward all that is left is to wait and see if it was the right call.