Enhanced portfolio returns using AI
Award-winning AI expertise providing Asset and Wealth Managers with a unique competitive edge
We are a diverse team of scientists with engineering backgrounds and industry practitioners objectively collaborating on solutions for our investors.
The team encourages a proven solution led culture in an environment that is biased towards clients and problem solving, which is one we remain committed to defending as the business grows.
Level E Research is an Edinburgh based FinTech established in 2018 as a start-up from the world-renowned University of Edinburgh’s School of Informatics.
We help leading Asset and Wealth Management organisations achieve even greater results by augmenting their current offerings with proven bespoke AI solutions created to boost existing returns.
Our disruptive technology, the E-platform, combines machine learning, data science and behavioural economics, enabling firms to develop, test and implement smart investment strategies at the highest levels of automation and significantly lower cost than traditional asset management business models.
The modular SaaS platform offers portfolio recommendations through its AI-Signal Engine (AISE), executed in the client’s own account with leading brokers integrated in the platform, coupled with order, portfolio and risk management systems, compliance and reporting. All functions are available via web browser and real-time Mobile Management Application (MoMA).
Our autonomous machine learning investment solutions can deliver various levels of risk-adjusted returns and target volatility, while also lowering trading and administration costs, further boosting returns and operational efficiencies.
Investment Managers & Hedge Funds
At Level E Research we believe financial markets continuously evolve, creating fundamental uncertainty, meaning that no single investment strategy can perform well in all market conditions. Financial markets are better understood through evolutionary adaptation rather than the traditional lens of market equilibrium. Consequently, the most up-to-date information about a security is embedded in its current price rather than in fundamental information such as financial statements. Price moves are determined by the way market participants behave and only become visible by analysing every movement, impossible for most investors to detect as it requires efficient analysis of terabytes of data.
Our machine learning technology can perform this analysis because it has been designed with a data-centric and hierarchical architecture of three distinct but interrelated layers: signal generation, portfolio construction and risk management. Artificial Analysts continuously analyse and learn from large pools of data to identify investment opportunities (i.e. signals) that are relevant to a portfolio. The signals generated are fed into Artificial Traders which constantly evaluate the signals according to client specific objectives and risk constraints. Decisions are converted into orders, automatically allocating capital to the signals.
The E-platform is designed to be a fully functional, autonomous investment management system built as a multi-agent framework where dedicated agents perform large scale data analysis tasks and processing in order to increase operational efficiencies across the entire investment management cycle. This, combined with real time risk and compliance monitoring and reporting is available online through a web-browser and mobile app.
Sept 2020 – Why the AI revolution matters
Sept 2020 – AI and Financial Services
research projects and internships
We are a technology and research company based in Edinburgh specialising in the development of automated trading systems that learn continually. We are always looking for leading talent to add to our team, searching for individuals that have an intellectual curiosity and demonstrate a highly disciplined approach to research.
Our disruptive technology, the E-platform, combines machine learning, data science and behavioural economics enabling firms to develop, test and implement smart investment strategies at the highest levels of automation and significantly lower cost than traditional asset management business models.
PhD and Post-Doctoral Internships are available throughout the year for machine learning research. These typically last 3-6 months, and can potentially lead to a full time position.
In addition, we welcome collaboration with machine learning research departments from academic Institutions around the world. Eligible joint-projects would involve researchers at the PhD and Post doctoral level only.