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All Posts Level E Research launches alpha and smart beta investment solutions August 19, 2021 Level E Research, the Edinburgh based leader in artificial intelligence (AI) investment solutions, has launched a series of alpha and smart-beta investment solutions, enabling fund managers to easily access, evaluate and employ non-correlated strategies to enhance their returns and reduce volatility, offering bespoke and systematic diversification to total return, multi-strategy and fund of fund managers.   The business, founded by Dr Sonia Schulenburg in 2018, combines machine learning, data science and behavioural economics enabling institutional investors to develop, test and implement smart investment strategies at the highest levels of automation and at significantly lower cost than traditional investment management business models.   Ruaridh Munro (Investment Solutions Analyst at Level E Research) “Portfolio managers running multi-asset funds often look for, yet struggle to identify, unique sources of non-correlated investments to complement their particular risk and volatility profile without detracting from fund performance or liquidity. Constituent strategies part of our range of alpha and smart beta strategies are analysed in detail against client portfolio returns in order to model their existing fund characteristics and identify complementary, but crucially uncorrelated strategies that enhance their returns while maintaining or reducing their risk parameters.  Dr Sonia Schulenburg (CEO of Level E Research) “Managers we are speaking to are excited to run these strategies and acknowledge that this would not be possible without our autonomous machine learning expertise and infrastructure consisting of a multi-agent environment with a strict scientific process optimising portfolio objectives in an on-line fashion.   We recently moved into production yet another use case utilising a combination of both, alpha and smart beta strategies, which confirms how we are supporting fund managers to easily integrate diversified sources of returns into their investment process aiming to achieve better outcomes, demonstrating the value our machine learning platform brings to investment management.” Previous Post Level E Research launches alpha and smart beta investment solutions August 19, 2021 Read More Why the AI revolution matters September 24, 2020 Read More AI and financial services September 10, 2020 Read More

All Posts Why the AI Revolution Matters September 24, 2020 In 2016, Klaus Schwab, founder and executive chairman of the Geneva-based World Economic Forum coined the term ‘The Fourth Industrial Revolution’ at Davos. Simply put, it refers to how technologies like artificial intelligence, autonomous vehicles and the internet of things are merging into everyday life. This fourth generation will be different from the previous three – steam and waterpower, electricity and assembly lines and computerisation – because it challenges our ideas about what it means to be human. In the last chapter of his book Homo Deus, Yuval Noah Hariri goes so far to suggest the possibility that humans are algorithms and may cease to dominate a universe where big data is the paradigm.   As one of the enabling technologies of this new industrial revolution, AI is expected to attract significant investment in anticipation of future revenues. Here are some interesting figures:   • PwC estimates investments over $15 trillion by 2030 with healthcare, automotive and financial services being the three leading sectors.   • According to Fortune Business Insights, the global AI market is expected to grow by $202.6 billion by 2026 from $20.7 billion in 2018.   • Last year, venture capital funded 956 deals valued at $13.5 billion through the third quarter with private equity investment doubling in just one year.   Will this new industrial revolution really make big differences? Autonomous vehicles represent perhaps the most obvious manifestation. According to Transport for London, a four-mile evening taxi fare across London costs between £16 & £24. With several studies suggesting autonomous taxis could reduce costs-per-mile by 50%-90%, a fare of £1.60 would be lower than all but the most efficient private cars. When you add this to other negatives of car ownership – in use only 5% of the time, 127 hours a year stuck in traffic, 90% of accidents down to human error – we strongly believe that the impact will be significant on many levels. Previous PostNext Post Level E Research launches alpha and smart beta investment solutions August 19, 2021 Read More Why the AI revolution matters September 24, 2020 Read More AI and financial services September 10, 2020 Read More

All Posts AI and Financial Services September 10, 2020 Last year, The Economist reflected on how algorithms have come to dominate financial markets, reporting that ‘funds run by computers that follow rules set by humans account for 35% of America’s stock market, 60% of institutional equity assets and 60% of trading activity.’ For some, AI and machine learning in particular is the next frontier; algorithms that act like humans, learning and adapting their behaviour as market conditions change.   In 2019, a joint survey by the Bank of England and Financial Conduct Authority on the use of machine learning in financial services revealed that:   • two-thirds of respondents use it in some form and expect machine learning use to double in the next three years    • firms mostly design and develop capability in-house but sometimes use third-party platforms and infrastructure e.g. cloud computing   • benefits include efficiency gains, product customisation and more effective fraud prevention and anti-money laundering    At the same time, the CFA Institute examined trends and use cases of AI and big data technologies in investments. They reported that relatively few investment professionals are currently exploiting AI and big data applications in their investment processes but identified three themes:   • natural language processing, computer vision and voice recognition to process text, image and audio data   • machine learning techniques to improve the effectiveness of algorithms used in the investment process   • AI to process big data, including alternative and unstructured data sets for investment insights   Investment managers manage approximately $90 trillion in assets. Yet there are currently only around 300 funds globally managing approximately $17 billion that use AI in some part of their investment process, less than 0.02pct, a tiny proportion of the overall industry.   So, it is early days in terms of AI and investment management, but some bets both big and small are being placed on the future applications of AI and machine learning.  Next Post Level E Research launches alpha and smart beta investment solutions August 19, 2021 Read More Why the AI revolution matters September 24, 2020 Read More AI and financial services September 10, 2020 Read More