Knowledge Base Articles
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.
Part III - Portfolio Construction - The Real World Analysis
In the third and the final part of our “Portfolio Construction” article series, the findings of the previous sections are applied to a broader and more realistic set of assets to evaluate the performance of the proposed methods against more conventional techniques.
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.
Blog Posts
Should Buying your House Ruin your Life[style]?
A mortgage may be the most tangible way many of us deal with financial risk on a day-to-day or more accurately month-to-month basis. The risk of interest rate rises and the effect they can have on mortgage obligations can be acute unless they are considered properly and meaningfully. Also, most of us buy the property based on the axiom that prices will drift upwards inexorably. Over the long term, this is probably true. However short to medium term fluctuations can have disastrous implications, for example, if your risk analysis does not prepare you properly for unforeseen events like the global pandemic. Again, few tools available on the market offer an all-encompassing ‘holistic’ framework that can contextualise how we can make such decisions to lead to optimal outcomes but let us try to imagine how such a tool could look.
Digital Transformation: Where to Start?
Today we have spoken to Fredrik Davéus, the founder and the CEO of Kidbrooke, about his experience of leading digital transformation in several prominent financial institutions in the Nordics. Fredrik walked us through the initial requirements to meet before starting a project, the components of financial analytics to look out for and how a standardised implementation process might look like.
Choosing Analytical Tools for Digital Financial Journeys: What matters?
The digitalisation process in finance is rarely linear and intuitive, therefore, it might be challenging to find a tool that best suits one’s vision. While there are not (yet) any established criteria to consider when building cutting-edge analytical capabilities within digital financial planning, we have identified a few that we frequently use to secure efficient product development on a day-to-day basis. From model quality and granularity to transparency, we believe these elements to be beneficial when one is planning their digitalisation journey.
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.