Knowledge Base Articles
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.
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: Self-Normalizing Neural Networks - Bond Liquidity Classification
In the second part of the article series, we outline a framework utilising both the Self-Normalizing Neural Networks (SNNs) and the logistic regression for bond liquidity classification. This framework is subsequently applied to the Swedish bond market in an investigative case study.
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.
Part III: Cyber Risk Management, Security Controls and Insurance
In continuation of our discussion of cyber risk, this paper investigates the issues of cyber risk management within financial industry. In particular, we look into the process of determining the optimal size of the investments in cyber security as well as the quantification of the appropriate cyber insurance premiums.
Part II: Cyber Risk; A Prime Component of Operational Risk
In continuation of our discussion of cyber risk, this article reviews different methods and models, which can be used to analyse and quantify the risks of information security breaches faced by the contemporary financial industry.
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.
The "Kryptonite" for Machine Vision in Finance
Currently, machine learning algorithms are steadily gaining prominence in multiple different sectors of the financial industry. The use cases include chatbots assisting the customers with small inquiries, valuation of financial instruments, option hedging, marketing and many other tasks which were traditionally performed by human employees. Although it sounds exciting that artificial intelligence takes over huge volumes of challenging human work, it would be irresponsible not to wonder how credible and accurate these systems are. Therefore, in this blog entry, we explore the flaws and opportunities of machine learning algorithms using machine vision solutions as an example.
May 2019 News Update
The introduction of automated financial advice services did not go successfully for some of the large and reputable wealth managers. As some of the industry players cease their robo advice offerings, we explore the reasons why big banks struggled to tap into the customers' demands. Meanwhile, machine learning solutions continue to expand to various business functions throughout the increasingly digitalising economies. However, little attention is paid towards the quality and the transparency of the decision-making powered by these "black boxes". Finally, in the world of accelerating personalisation standards, it is crucial to expand the innovation efforts beyond the interfaces and use the technological capabilities to improve the actual offerings.
April 2019 News Update
We are delighted to present our analysis of the top April trends within the financial industry! This month we identified the growing need for risk expertise among the asset managers striving to provide truly sustainable financial advice. Moreover, we see that a different set of factors determines the competition among the digital offerings in asset management compared to the traditional financial advisory services. Additionally, we firmly believe that it is crucial for the financial institutions to measure and prepare for the impact of the looming -IBOR transition early on, and come up with an appropriate action plan to minimise the adverse PnL effects.
Summary: Swedish FSA releases consumer protection report
The Swedish Financial Supervisory Authority (FI) releases a yearly consumer protection report featuring customer security highlights in the Swedish financial industry. As in the previous years, the main risks related to customer security relate to mortgages and loans. The interest payments can potentially threaten the economies of the individuals in case of the economic downturn or the increase in interest rates. Another threat that is amplifying in the context of the digitalising society relates to customer data protection. The FI calls for more advanced security systems that would protect the consumer at all stages of a payment transaction. The improvement of these solutions is especially relevant in the context of increased instances of financial fraud. Finally, FI announces that the protection of the wealth management customers and the enforcement of the MiFID II requirements regarding third-party inducements becomes a vital area of the regulators' future work.