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Learn More01Robo Investing Event Summary
Through September 10-11th, Kidbrooke Advisory attended the Robo Investing 2019 Event in London. Existing for over two years, the event has become an excellent platform for knowledge sharing and communication between numerous FinTechs, banks, consultancies and other players of the emerging industry, all across the world. Today we are summarising the core trends and themes discussed at the event. The main topics led the overall direction of the robo-advice offerings as well as tips and tricks on achieving more customer engagement.
Read More02August 2019 News Update
In August, we distinguished three themes gaining momentum in the financial industry's innovation landscape. The first one concerns the positioning of the robo-advice on the Gartner hype cycle, from the peak of inflated expectations to the trough of disillusionment. The second trend explores the meaning of sustainability in the provision of financial advice. Finally, looking into the potential flaws of the machine learning-driven models sums up the third theme of the August press on the financial industry's innovation.
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Financial advice is being digitalised and is increasingly provided on an automated basis. Download our summary of the latest developments within this exciting field.
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The second part of the “Portfolio Construction”-series explores whether introducing parameter uncertainty to the model would improve the out-of-sample performance of the optimal portfolio. Additionally, the article proposes and tests two adjustments to regular utility optimisation.
The rapid evolution of computational technologies has enabled businesses to leverage machine learning methods to tackle challenging, labour-intensive tasks involving various degrees of judgement and decision making. Financial markets are no exception. In this article we present the case of using our AI-driven solution to tackle a common challenge in finance – the fair value measurement of illiquid financial instruments.
There is a number of challenges associated with portfolio construction based on historical data. This three-part article series explores some of the most common issues attributed to the model-based portfolio optimization: the sensitivity to changes in data, large variations in portfolio weights and the bad out-of-sample performance.
As machine learning methods grow in use and popularity, we explore yet another dimension of wealth management that our experts consider fit for applying such frameworks. In this article, we deploy hierarchical clustering to find more consistent ways of predicting the relative future performance of funds.
What we do
We improve decision making under uncertainty
Our work empowers millions of people to make, or benefit from, informed financial decisions under uncertainty. Asset liability management, capital requirements and automated financial advice - everything we do helps support our vision of a world where everyone can make educated
financial decisions.