Knowledge Base Articles

Part II - Artificial Neural Networks as a Substitute to LSMC and Nested Simulations

In this article series we present a machine learning-based approach to solving a common problem in financial modelling where one is faced with the task of estimating the value of a function which requires a significant amount of computation to evaluate. More specifically a function that corresponds to a so called nested simulation aimed at for example estimating a capital requirement for a financial institution or the risk associated with a structured product for a retail investor.

Part I - Introduction to Artificial Neural Networks

In this article series, we present a machine learning-based approach to solving a common problem in financial modelling where one is faced with the task of estimating the value of a function which requires a significant amount of computation to evaluate. More specifically, a function that corresponds to a so-called nested simulation aimed at, for example, estimating a capital requirement for a financial institution or the risk associated with a structured product for a retail investor.

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.

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.

Part I: An Introduction to Self-Normalizing Neural Networks

Machine learning applications have become more prominent in the financial industry in recent years. Our new article series is exploring the benefits and challenges of using self-normalising neural networks (SNNs) for calculating liquidity risk. The first piece of the series introduces the main concepts used in the investigative case study for the Swedish bond market.

Part III: Asset and Liability Management Using LSMC - Allocation Optimisation

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.

Part II: Asset and Liability Management Using LSMC - Accuracy and Performance

The second part of the series exploring the use of Least Squares Monte Carlo in Asset and Liability Management is focused on evaluation of accuracy and performance of this method in comparison to full nested Monte Carlo simulation benchmarks.

Part I: Asset and Liability Management Using LSMC - Introduction to the Framework

In the first part of the ”Asset and Liability Management using LSMC” article series, we outline an ALM framework based on a replicating portfolio approach along with a suitable financial objective. This ALM framework, albeit simplified, is constructed to provide a straightforward replication of the complex interactions between assets and liabilities. Moreover, a brief introduction to the LSMC method used to generate all underlying risk factors is presented.

Introduction to Credit Index Modelling

This article will discuss why it is important to model credit indices and detail a number of different approaches to this problem.

Blog Articles

Exploring ALM in Life Insurance: Strategy, Risks, and Innovations

In this interview, Hans Sterte, a seasoned economist, and Senior Partner at House of Reach, shares his rich insights from a career spanning over three decades in the realms of economics, asset management, and strategic investment. Tracing his journey from government institutions to leadership roles in major Swedish pension funds, Hans delves into the intricacies of Asset and Liability Management (ALM) within life insurance companies, highlighting its evolution and current challenges.

How Realistic Tax Modelling and Risk Analysis Within Your Financial Planning Software Can Support Your Roadmap

As Europe grapples with inflation, financial institutions are tirelessly searching for new ways of improving their business models. Whether updating their services with digital channels, creating more financial products, or exploring untapped markets, the financial executives carry on balancing compliance and innovation in their work. If your organization is embarking on a bold expansion to new countries or updating their selection of investment products, it is critical to ensure that your financial analytics suite is attuned to your strategic roadmap. Today we sat down with Lars Larsson, partner and quantitative finance expert at Kidbrooke®, to better understand how financial organizations can leverage the granularity of modelling and a detailed approach to representation of taxes to support their complex expansion roadmaps.

Setting up OutRank®, the Financial Forecasting Software

Suppose your organization decided to leverage OutRank®, the financial forecasting API, in its digital and hybrid financial journeys. Where do you start and how do you ensure you get the most out of our analytics? We talked to Kidbrooke®’s Customer Success Team to shed some light on the onboarding and maintenance processes, the client requirements and internal expertise needed to ensure that our clients achieve their goals using our technology.

Gamification and Simulation tools: Enhancing the Wealth Management Customer Experience

Managing your savings is not what it used to be a few decades ago. Despite rising interest, it was far too intimidating to invest without having an in depth understanding of how financial markets operate. However, this took a turn when more financial services companies embraced the incorporation of gamification. Elements such as points, badges, and leaderboards were put into non-game contexts. Gamification has since been gaining traction after wealth managers saw an increase in engagement and motivation among users.