We introduce a $\phi^{4}$ lattice field theory with frustrated dynamics as a multi-agent system to reproduce stylized facts of financial markets such as fat-tailed distributions of returns and clustered volatility. Each lattice site, represented by a continuous degree of freedom, corresponds to an agent experiencing a set of competing interactions which influence its decision to buy or sell a given stock. These interactions comprise a cooperative term, which signifies that the agent should imitate the behavior of its neighbors, and a fictitious field, which compels the agent instead to conform with the opinion of the majority or the minority. To introduce the competing dynamics we exploit the Markov field structure to pursue a constructive decomposition of the $\phi^{4}$ probability distribution which we recompose with a Ferrenberg-Swendsen acceptance or rejection sampling step. We then verify numerically that the multi-agent $\phi^{4}$ field theory produces behavior observed on empirical data from the FTSE 100 London Stock Exchange index. We conclude by discussing how the presence of continuous degrees of freedom within the $\phi^{4}$ lattice field theory enables a representational capacity beyond that possible with multi-agent systems derived from Ising models.

There is a general consensus of the good sensing and novelty characteristics of Twitter as an information media for the complex financial market. This paper investigates the permeability of Twittersphere, the total universe of Twitter users and their habits, towards relevant events in the financial market. Analysis shows that a general purpose social media is permeable to financial-specific events and establishes Twitter as a relevant feeder for taking decisions regarding the financial market and event fraudulent activities in that market. However, the provenance of contributions, their different levels of credibility and quality and even the purpose or intention behind them should to be considered and carefully contemplated if Twitter is used as a single source for decision taking. With the overall aim of this research, to deploy an architecture for real-time monitoring of irregularities in the financial market, this paper conducts a series of experiments on the level of permeability and the permeable features of Twitter in the event of one of these irregularities. To be precise, Twitter data is collected concerning an event comprising of a specific financial action on the 27th January 2017:{~ }the announcement about the merge of two companies Tesco PLC and Booker Group PLC, listed in the main market of the London Stock Exchange (LSE), to create the UK's Leading Food Business. The experiment attempts to answer five key research questions which aim to characterize the features of Twitter permeability to the financial market. The experimental results confirm that a far-impacting financial event, such as the merger considered, caused apparent disturbances in all the features considered, that is, information volume, content and sentiment as well as geographical provenance. Analysis shows that despite, Twitter not being a specific financial forum, it is permeable to financial events.

There is a consensus about the good sensing characteristics of Twitter to mine and uncover knowledge in financial markets, being considered a relevant feeder for taking decisions about buying or holding stock shares and even for detecting stock manipulation. Although Twitter hashtags allow to aggregate topic-related content, a specific mechanism for financial information also exists: Cashtag. However, the irruption of cryptocurrencies has resulted in a significant degradation on the cashtag-based aggregation of posts. Unfortunately, Twitter' users may use homonym tickers to refer to cryptocurrencies and to companies in stock markets, which means that filtering by cashtag may result on both posts referring to stock companies and cryptocurrencies. This research proposes automated classifiers to distinguish conflicting cashtags and, so, their container tweets by analyzing the distinctive features of tweets referring to stock companies and cryptocurrencies. As experiment, this paper analyses the interference between cryptocurrencies and company tickers in the London Stock Exchange (LSE), specifically, companies in the main and alternative market indices FTSE-100 and AIM-100. Heuristic-based as well as supervised classifiers are proposed and their advantages and drawbacks, including their ability to self-adapt to Twitter usage changes, are discussed. The experiment confirms a significant distortion in collected data when colliding or homonym cashtags exist, i.e., the same \$ acronym to refer to company tickers and cryptocurrencies. According to our results, the distinctive features of posts including cryptocurrencies or company tickers support accurate classification of colliding tweets (homonym cashtags) and Independent Models, as the most detached classifiers from training data, have the potential to be trans-applicability (in different stock markets) while retaining performance.

Forecasting building energy consumption has become a promising solution in Building Energy Management Systems for energy saving and optimization. Furthermore, it can play an important role in the efficient management of the operation of a smart grid. Different data-driven approaches to forecast the future energy demand of buildings at different scale, and over various time horizons, can be found in the scientific literature, including extensive Machine Learning and Deep Learning approaches. However, the identification of the most accurate forecaster model which can be utilized to predict the energy demand of such a building is still challenging.In this paper, the design and implementation of a data-driven approach to predict how forecastable the future energy demand of a building is, without first utilizing a data-driven forecasting model, is presented. The investigation utilizes a historical electricity consumption time series data set with a half-hour interval that has been collected from a group of residential buildings located in the City of London, United Kingdom

In news that will likely surprise no-one, Britain's average CEO is a 55-year-old privately educated white man who studied Economics at Cambridge and makes £4,196,000 a year. Data from the FTSE100 - a list of the 100 biggest companies listed on the London Stock Exchange - has been used to work out the most common background for a UK CEO. Researchers from People Managing People used artificial intelligence (AI) to combine the LinkedIn profile pictures of Britain's top 100 CEOs. The resulting composite image reveals the face of the average CEO – an oddly familiar man dubbed Andrew. Finn Bartram, Editor of People Managing People, said the uncanny digital portrait shows the'undeniable privileges and gender disparities for the top jobs at some of the biggest companies in the country.'

We are the world's leading global information services company. During life's big moments – from buying a home or a car, to sending a child to college, to growing a business by connecting with new customers – we empower consumers and our clients to manage their data with confidence. We help individuals to take financial control and access financial services, businesses to make smarter decisions and thrive, lenders to lend more responsibly, and organisations to prevent identity fraud and crime. We have 20,000 people operating across 44 countries and every day we're investing in new technologies, talented people, and innovation to help all our clients maximise every opportunity. We are listed on the London Stock Exchange (EXPN) and are a constituent of the FTSE 100 Index.

Financial services technology firm Refinitiv has launched a new artificial intelligence (AI) assistant for Microsoft Teams to provide financial professionals with stock market news and actionable insights. Refinitiv AI Alerts - which is powered by technology from AI specialist ModuleQ - uses permissions and Microsoft 365 interactions to automatically learn the user's individual priorities and recommends content based on email conversations and upcoming meetings. "Microsoft Teams has become an indispensable platform for professionals across financial services, with institutions accelerating their adoption, and increasingly integrating critical data and tools into the platform to simplify the workflow and user experience of professionals," said Andrea Remyn Stone, group head of data and analytics at London Stock Exchange Group, which owns Refinitiv. "Refinitiv AI Alerts brings critical content and insights to Refinitiv's customer base within this platform, allowing users to discover and act on timely information across Teams, Refinitiv solutions and Microsoft 365 seamlessly." The new solution is the latest result of Refinitiv's partnership with Microsoft, which has previously helped financial services organisations connect, collaborate and leverage data to make more informed decisions.

In a blockbuster $27 billion deal that's certain to threaten Bloomberg's financial-data empire, the London Stock Exchange struck an agreement this week to buy the data company Refinitiv. The tie-up highlights trading venues' desire to move beyond low-margin trading and clearing into the more lucrative business of selling data. LSE CEO David Schwimmer, who joined the stock-exchange group less than a year ago from Goldman Sachs, is driving the industry-changing transaction. Our banking reporter Dakin Campbell talked to Schwimmer's former colleagues and clients, who told him why Schwimmer is ideally placed to do the deal. If you aren't yet a subscriber to Wall Street Insider, you can sign up here.

That algorithms played a part in the financial crash of 2007, for instance, is well documented. In 2006, around 40% of all trades conducted on the London Stock Exchange were executed by computers, with this figure reaching 80% in some U.S. equity markets. For many economists and experts, the fact that transactions were made by "algos" written by quantitative analysts (or "quants" for short) was one of the main reasons why global markets built up so much risk prior to the collapse. As Richard Dooling--the author of Rapture for the Geeks: When AI Outsmarts IQ--wrote for the New York Times in 2008, "Somehow the genius quants--the best and brightest geeks Wall Street firms could buy--fed $1 trillion in subprime mortgage debt into their supercomputers, added some derivatives, massaged the arrangements with computer algorithms and--poof!--created

Listen up, payment provider community. Increased digitalization, continued pursuit of integration across channels, ever-increasing complexity, more partnerships and the integration of technologies like the Internet of Things, machine learning and artificial intelligence may be what's needed to propel the payment provider industry forward, at least according to Creditcall's CEO Lars Pedersen. "We must embrace these new technologies and the resulting complexity so we can provide solutions that merchants can readily deploy to increase revenues, reduce costs, and gain greater insight into customer preferences and business logistics," Pedersen said. As a company that was just named to the London Stock Exchange Group's 1,000 Companies to Inspire Britain, all while being 100 percent self-funded, payments provider Creditcall may just have that certain je ne sais quoi. "The main reason we have been able to grow in a self-funded way is that we have a rigorous process for deciding what activity to take on," Pedersen said.