Forecasting economic developments during crisis time is problematic since the realizations of the variables are far away from their average values, while econometric models are typically better at explaining and predicting values close to the average, particularly so in the case of linear models. The situation is even worse for the Covid-19 induced recession, when typically well performing econometric models such as Bayesian VARs with stochastic volatility have troubles in tracking the unprecedented fall in real activity and labour market indicators -- see for example for the US Carriero et al. (2020) and Plagborg-Møller et al. (2020), or An and Loungani (2020) for an analysis of the past performance of the Consensus Forecasts. As a partial solution, Foroni et al. (2020) employ simple mixed-frequency models to nowcast and forecast US and the rest of G7 GDP quarterly growth rates, using common monthly indicators, such as industrial production, surveys, and the slope of the yield curve. They then adjust the forecasts by a specific form of intercept correction or estimate by the similarity approach, see Clements and Hendry (1999) and Dendramis et al. (2020), showing that the former can reduce the extent of the forecast error during the Covid-19 period. Schorfheide and Song (2020) do not include COVID periods in the estimation of a mixed-frequency VAR model because those observations substantially alter the forecasts. An alternative approach is the specification of sophisticated nonlinear / time-varying models. While this is not without perils when used on short economic time series, it can yield some gains, see e.g.
DeepMind Technologies, a Google subsidiary and Artificial Intelligence (AI) firm, disclosed that it will adopt Blockchain technology and make use of Distributed Ledger Technology (DLT).This move will help the company secure patient data more efficiently. DeepMind creates algorithms designed for applications, gaming protocols and stimulation. It earned fame for developing a machine-learning program that can be capable of playing video games. Likewise, DeepMind developed the so-called "Neural Turing Machine" that copies short-term memory of human beings. It signed a five-year contract with Royal Free London NHS Trust recently so it can apply the technology to healthcare.
Two thirds of financial services firms currently deploy machine learning and expect to increase their use of the technology within the next three years. The Bank of England and the UK Financial Conduct Authority conducted a joint survey this year on the current use of machine learning, a methodology where computer programmes fit a model or recognise patterns from data, without being explicitly programmed and with limited or no human intervention. This contrasts with'rules-based algorithms' where the human programmer explicitly decides what decisions are being taken under which states of the world. The study said the median firm uses live machine learning applications in two business areas and this is expected to more than double within the next three years. Bank of England's machine learning paper is also interesting.
If you thought taking a few machine learning courses on Udemy might be enough to inure you against future unemployment then yesterday's report on machine learning in financial services from the Bank of England and Financial Conduct Authority (FCA) will come as a bit of a shock. The report is based on a survey of 106 banks and finance firms in London. It turns out that, yes, machine learning is being used in banks. But, no, it's not hard to find anyone to fill the roles and that this is the least of the worries as machine learning is rolled out across the finance sector. The charts below, from the report, show where machine learning (ML) is already most in use in the banking sector (defined as building societies, international banks, retail banks, UK deposit takers, and wholesale banks) and in the investments and capital markets sector (defined as alternatives, corporate finance firms, fund managers, principal trading firms, wealth managers and stockbrokers, and wholesale brokers.)
The economic numbers from China are pointing to the realities of the trade war – a slowing China's economy. Results of Published Model Trades for THU 10/17 Find below the detailed outcome tracking of our models' trading plans for the day, as well as the results for the last month. Note: Our daily "S&P 500 Outlook, Forecast, and Trading plan" will be posted around 9:00am EDT, every trading day. Overnight futures markets are cheering the last minute deal struck between the UK and the EU on Brexit, despite serious doubts whether the UK parliament would approve the deal. Results of Published Model Trades for WED 10/16 Find below the detailed outcome tracking of our models' trading plans for the day, as well as the results for the last month.
Google has revealed what people in the UK have been searching for in 2018, with the publication of its annual'Year in Search' results. Meghan Markle, who topped the list last year, was knocked off her perch by the World Cup but still managed to be the second-most searched term in 2018. Her marriage to Prince Harry, together with the wedding of Princess Eugenie and Jack Brooksbank, helped'Royal Wedding' reach third on the overall list. Several other celebrities like Stormy Daniels and Ant McPartlin also appeared on the lists, which covered a wide range of categories including top trending'How to...?' and'What is...?' queries. Privacy scandals helped'How to delete Facebook?' become one of the most-asked questions using Google, while the dramatic rise and fall in cryptocurrency prices helped drive searches for'What is bitcoin?'.
On the sidelines of Money20/20, held in Amsterdam this week, bobsguide caught up with Nick Cook, head of regtech and advanced analytics at the UK's Financial Conduct Authority (FCA). All of the external facing regtech sits under my department. Internally, I'm leading our more advanced analytics, both the technology side - cloud analytics - as well as building out our human side - our data science capability. Over time, through training and development we can start to build it out and expand in an osmotic fashion across the wider organisation - effectively enabling us to leverage machine learning. We're also running sandboxes and hackathons to develop and encourage the regtech contingent of the UK startup market.
In 2018, worldwide IT spending is predicted to hit $3.7 trillion, an increase of 4.5% from 2017 according to a recent report by Gartner. The increase in spending will be driven by emerging technologies; IoT, Blockchain and AI are projected to be the key growth areas. Many feel the game-changer for emerging technologies lies in the convergence of IoT, Blockchain and AI. Recently IoT Tech Expo speaker, Cisco's Maciej Kranz* stated that 2018 will be the time when the leading technologies of today; AI, IoT and blockchain will converge to power new solutions. And we can already see an example of this from Porsche which is currently testing IoT, AI and blockchain technology solutions for smart cars.
Aida is the perfect employee: always courteous, always learning and, as she says, "always at work, 24/7, 365 days a year." Aida, of course, is not a person but a virtual customer-service representative that SEB AB, one of Sweden's biggest banks, is rolling out. The goal is to give the actual humans more time to engage in more complex tasks. After blazing a trail in online and digital banking, Sweden's financial industry is now emerging as a pioneer in the use of artificial intelligence (AI). Besides Aida at SEB, there's Nova, which is a chatbot Nordea Bank is introducing at its life and pensions unit in Norway.