Knowledge Base Articles
Part III - Portfolio Construction - The Real World Analysis
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.
Part II - Portfolio Construction - Sampling & Optimisation
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.
Part I - Portfolio Construction - Parameter & Model Uncertainty
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.
Hierarchical Clustering: Prediction of Systematic Underperformance
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.
Beyond Modern Portfolio Theory: Expected Utility Optimisation
The modern wealth management industry still relies on the 50-year-old approaches to portfolio management, widely popularized by Markowitz's Modern Portfolio Theory (1952). Despite heavy criticism within the academic circles, the alternative methods remain undeservingly overlooked in practice. In the context of the modern leap for hyper-customization, we look into one of the alternatives to Modern Portfolio Theory in greater detail - the Utility-based approach.
Blog Posts
Is 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?
Robo 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.
August 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.
June 2019 News Update
This June, we analysed three topics that gain prominence in the context of rapidly digitalising financial industry. As widely known, the machine learning solutions become more widespread in addressing the operational and compliance issues within banks and insurers. However, we highlight that the interpretability of such models is as relevant as their performance. Moreover, in the context of maturing robo-advisory offerings, we see that the common strategy is to focus on the space of clients which are underserved by traditional financial advisors. Finally, we look into the process of building trust by the emerging challenger banks, which may threaten the positions of the centuries-old incumbents in the industry of tomorrow.