01Fourth Quarter 2019: The Most Relevant Trends in WealthTech
The fourth quarter of 2019 was marked by an increased interest in B2B business models among the FinTech robo advisors. Although the shift from B2C to B2B has been in the headlines a few months before, the last three months yielded more concrete examples of how new players tailor their business models to cater to other companies, rather than consumers. Meanwhile, the debate on whether the process of financial advice would be entirely digital or contain hybrid elements continued as well - with many British players robo advisors adding human capabilities to the scope of their services. Finally, in the context of digitalised offerings, it becomes harder for the regulator to tell the difference between financial advice and guidance - and therefore it becomes essential to review the definitions of the services to optimise the consumer protection accordingly.Read More
02Is there any point to optimising asset allocation in portfolios?
Over the years, numerous studies have shown how complex investment strategies fail to outperform simple asset allocation methods. Other studies emphasise the amount of sheer luck that goes into the favourable performance of the investment strategies; it has been repeatedly shown that in many instances, an attempt to deviate from the market portfolio has odds no better than a coin flip. These findings seem to point towards one cold fact - the optimal portfolio weights are impossible to find. Or are they?Read More
Financial advice is being digitalised and is increasingly provided on an automated basis. Download our summary of the latest developments within this exciting field.Download
In the third and the final part of our “Portfolio Construction” article series, the findings of the previous sections are applied to a broader and more realistic set of assets to evaluate the performance of the proposed methods against more conventional techniques.
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.
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