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Utilising alternative data to develop a machine learning-based artificial intelligence platform for real-time KPI tracking of UK companies for fundamental investors

Utilising alternative data to develop a machine learning-based artificial intelligence platform for real-time KPI tracking of UK companies for fundamental investors
利用另类数据开发基于机器学习的人工智能平台,为基本面投资者实时跟踪英国公司的 KPI
批准号:
10057969
负责人:
金额:
$24.81万
依托单位:
依托单位国家:
英国
项目类别:
Collaborative R&D
财政年份:
2023
资助国家:
英国
项目状态:
未结题
起止时间:
2023 至 --

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英文摘要
Our founder and CEO Josef Schmalfuss is a recognised industry leader in utilising Alternative Data for Fundamental Investing. Having spent seven years as a partner at a fundamental investment fund as a buyer/in-house creator of alternative data, he decided that innovation is required to transform current processes of data-insight generation for investment decision-making.Key performance indicators(KPIs) are a hugely important source to indicate the overall performance of companies. Currently, investment decision-making is predominantly based on backwards-looking reported data comprising quarterly or annual reports or financial statements. However, there are many other areas that indicate a company's worthiness for investment. We are utilising alternative data for this purpose, to generate KPI trackers that incorporate data such as transaction data, app downloads, social media and mobility data. Our solutions are based on the idea that the investment potential of a company is based on more than just how it looks on an old piece of paper.KP-Intel is a game-changing platform for the autonomous generation of daily data-driven KPI estimations, utilising real-time data, of **every** mid-size/larger UK company and a significant proportion of small-to-medium-sized enterprises. Currently, to generate alternative data insights, companies and investors must seek the input of a data scientist in the process, which can be expensive, difficult and time-consuming. Since most investors do not have substantial data science teams and resources, data-driven decision-making is limited. With a data science team and optimised results, the process is improved but ingrained inefficiencies remain.The resultant societal effect is that good companies can be overlooked for investments, reducing UK growth and innovation. KP-Intel will democratise access to relevant data insights so that no data science background is required to utilise data-driven decision-making; removing its associated inefficiencies/increasing accessibility. Long-term, this will drive innovation and growth of the UK economy while improving investor transparency.
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