Model

Knowledge Base Articles

Determining whether one’s funds would be sufficient to sustain their lifestyle is vital for everybody. At Kidbrooke, we provide the technology that enables everyone, not only the most fortunate, to plan their financial future reliably, securely and accurately, while at the same time enabling financial advisors to deploy their most valuable asset, human advisor time, in the most efficient way possible from a business and revenue perspective. A step outside the HNWI space often leads to the place where tools offered by the market-leading financial institutions fail to provide adequate help to the customers planning their financial lives or the lives of their families. Today’s case study examines a real-life experience of a Swedish family who struggled to receive adequate help from the local wealth management service providers.

Founded in 1855, Skandia is one of the indisputable market leaders in financial advisory services within the Nordics. Throughout its long history, the institution has relied on the expertise and experience of its financial advisors, who earned trust and appreciation from thousands of customers over the decades. Today, as the company transitions to a multichannel, digitally empowered business model, a focused area is the delivery of a superior service through all available channels – from traditional face-to-face meetings to digital financial experiences. Therefore, the insurer strives to build communication channels in a digital space that would match the physical experiences in engagement levels and even improve the service quality in a way that has not been achievable before.

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.

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.

In the third and concluding article in the ALM using LMSC series, we focus on analyzing the optimal asset allocations in the context of changing asset classes as well as finding the optimal allocation by maximizing the risk-adjusted net asset value. The estimates based on the LSMC method are then compared to the estimates obtained from the full nested Monte Carlo method.