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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

Employer Branding: Using technology to promote pension awareness

Taking chances on new technology is not often a priority for human resource executives. They strive to achieve various objectives around recruitment, retention and productivity, but not on adopting innovation. However, achieving these objectives may be dramatically accelerated by the intelligent use of technology. Today's blog focuses on the best practices related to pension planning for employees, and how a cooperation between the corporate human resources and financial functions leads to your employees’ confidence in tomorrow.

The Hybrid Wealth Business: Focus on Technology

Modern technology has enabled self-service wealth management where end-customers can investigate, learn and make decisions about their financial situation. This marks the beginning of the democratization of the industry, where seamless guidance through the intricacies of personal finances is available for those who would not previously have the privilege of using the services of a financial advisor. However, combining technological advancements and the human-led service of a financial advisor in many cases leads to a better experience. This effect is achieved through empowering an expert with the tools that help them improve the quality and content of their work to levels previously unachievable. At Kidbrooke, we provide technology enabling financial advisors to build trust and truly future-proof their business models without losing the human connection. In this article, we describe two important elements of the customer experience that a hybrid model could add to any modern physical advisor’s arsenal:

Outside In: Iterate to Innovate

New technology is often implemented by large companies to solve specific problems. Once the initial purpose has been achieved, the technology becomes part of the business as usual or “BAU” infrastructure. But instead of relegating a new functionality to the “new normal” category, it is useful to consider it as a catalyst for change – both in external, customer facing projects and in internal, IT development planning. Skandia, Sweden’s largest insurance company, implemented OutRank to offer customers a superior tool to better understand their pensions and investments. Working together with the Kidbrooke team, the Skandia executives saw the positive impact that a user-friendly customer interface had on sales as well as customer retention.

ESG: Energize your investment portfolio

The COP26 conference in Glasgow may be over, but the themes discussed there will continue to be topics of concern for people, especially in the context of long-term investments in company stocks and mutual funds. There are so many issues to be considered: the environmental, particularly the response to climate change; the social, how companies treat their employees, suppliers and customers; and governance, how companies manage themselves as responsible corporate entities. With all of these facets of ESG, the subject of sustainability can feel overwhelming, but it doesn’t have to be. With the right tools, financial advisors can help their customers easily understand the complexity of the sustainability issues and take action based on that knowledge.