Knowledge Base Articles
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.
March 2019 News Update
We've selected the most relevant global news within the fields of automated financial advice, data intelligence and balance sheet risk this March. The rise of the robo-advisors concern the brokers, although many see this development as a helpful complement to the traditional wealth management business, stressing the regulatory burden as well as IT legacy systems' challenges. Meanwhile, Brexit leads to a spike in risk-aversion among the customers of Do-It-Yourself investment platforms. On the balance sheet risk side, the experts stress the importance of timely preparations to IFRS 17, FRTB and the transition from LIBOR. The machine learning tools are put to use in fraud detection in the banking industry context, and the Positive-Incentive ESG-based Loans gain prominence among the banks and corporations.