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

Silver linings and green shoots: a client lifecycle approach to financial planning

Now that the world is emerging from the Covid 19 pandemic, people will be making decisions on their lifestyle choices, careers, housing, education and eventual retirement. A year of working from home, for those who are not key workers, has led some individuals to contemplate a new kind of life, with better work-life balance, among other objectives. Pundits have published articles about “the future of work” and related topics. Yet these decisions must be made in the context of holistic financial planning so that the rewards, risks and tradeoffs can be fully understood. The ability to see complex financial scenarios including the “known unknowns” is usually the subject of actuaries and portfolio managers; ordinary people and the financial professionals who advise them need tools to understand their finances quickly and simply.

Tools to manage risk, reward and possibilities: new perspectives for pensions and pre-retirement planning

For British people age 55+ with a defined contribution pension, being able to access the first 25% of your savings tax-free can be liberating. They can control how they spend the remainder with an income drawdown scheme. Annuities are no longer the only option. Regular payments can be taken from a pension fund and taxed as income. It is also possible to take the entire amount as cash and be taxed at the appropriate nominal tax rate. People who have assumed that an employer’s pension scheme or a financial planner will take care of their finances now wish to manage their own money. However, what they really need to do is manage three kinds of risk:  market risk, longevity risk and inflation risk. Given the three types of risk that retirees need to manage, how can technology empower people to manage their pension assets in a strategic, systematic and safe way? A financial tool grounded in transparency of assumptions is the way forward.

Davids and Goliaths: The Role of Big Tech in Financial Services

We shouldn’t be surprised that Fintech firms are pioneering the current wave of mass digital adoption. Account management, payments and identity verification are just three areas where digital technology has and continues to augment products. In the past decade, much of the innovation has decoupled from the mainstream. Firms in hubs such as Stockholm and London have been at the forefront of pioneering fresh ideas and translating them into new consumer-centric tools. Now, as the industry matures, mainstream Tech firms are looking to add their heft into the mix: Will they overpower the relative minnows swimming in the Fintech waters, or will their efforts sink without a trace?

Leaning Into the Curve of the Pandemic

Few would have predicted a few months ago that 2020 would prove to be such a seismic event, notwithstanding that it’s US election year. Decisions made now will reverberate for years, if not decades to come. We will stay in our lane here and look at how FinTech can support better outcomes as we come out the other side.