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01August 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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02The "Kryptonite" for Machine Vision in Finance

Currently, machine learning algorithms are steadily gaining prominence in multiple different sectors of the financial industry. The use cases include chatbots assisting the customers with small inquiries, valuation of financial instruments, option hedging, marketing and many other tasks which were traditionally performed by human employees. Although it sounds exciting that artificial intelligence takes over huge volumes of challenging human work, it would be irresponsible not to wonder how credible and accurate these systems are. Therefore, in this blog entry, we explore the flaws and opportunities of machine learning algorithms using machine vision solutions as an example.

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Digital Advice

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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Modelling
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.

Machine Learning
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.

Risk
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.

Machine Learning
Part II: Self-Normalizing Neural Networks - Bond Liquidity Classification

In the second part of the article series, we outline a framework utilising both the Self-Normalizing Neural Networks (SNNs) and the logistic regression for bond liquidity classification. This framework is subsequently applied to the Swedish bond market in an investigative case study.

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 that everyone should have access to world class risk management tools.