As financial markets evolve, the need for accurate and efficient valuation of assets remains crucial—particularly for illiquid financial instruments. While artificial intelligence (AI) has reshaped numerous industries, its application in fair value measurement is gaining prominence.
Kidbrooke, a unified analytics platform for investment and wealth, recently explored how leveraging machine learning techniques like hierarchical clustering and artificial neural networks (ANNs), can empower financial institutions to improve the consistency and precision of their valuations, even in markets lacking active trading data.
Fair value measurement is a fundamental principle of the International Financial Reporting Standards (IFRS), guiding companies worldwide in preparing financial statements. Unlike traditional historical cost accounting, which values assets based on acquisition price and amortisation, fair value accounting seeks to reflect an asset’s present worth using available pricing data. This approach provides stakeholders with a clearer understanding of a company’s financial position, Kidbrooke explained.
Financial instruments are a key area where fair value measurement plays a critical role, particularly due to the structured nature of these assets and their trading environment. Under IFRS 9, financial instruments must be valued based on a fair value hierarchy.
Kidbrooke delved into the issue of measuring the fair value of instruments without an active market, but which have similar instruments quoted at active markets (Level 2). To address this, machine learning techniques such as hierarchical clustering and artificial neural networks are employed. Hierarchical clustering helps identify groups of similar instruments, while ANNs can estimate yield-to-maturity values for valuation.
Applying hierarchical clustering for financial valuation
Clustering is a data-driven approach used to identify patterns by grouping similar objects together while keeping dissimilar ones apart. This methodology mimics human categorisation—such as differentiating between cars and bicycles while classifying both under transportation.
In fair value measurement, clustering is essential for identifying comparable assets when an exact market price is unavailable. To achieve this, an AI model incorporating a Variational Autoencoder (VAE) and a Gaussian Mixture Model (GMM) is used. The VAE acts as the “eyes” of the AI, compressing high-dimensional financial data into a lower-dimensional space where similarities can be identified. The GMM, functioning as the “brain,” then classifies these data points into distinct clusters by applying probability-based distributions.
This AI-driven clustering process was tested in bond markets, where it effectively categorised financial instruments into clusters while identifying representative assets and outliers. Compared to conventional clustering techniques, this approach offers enhanced accuracy and adaptability in financial valuation.
Artificial neural networks for yield estimation
Following hierarchical clustering, ANNs can be trained to refine valuation estimates for Level 2 instruments. A trained ANN processes relevant bond characteristics—such as maturity, coupon rates, and other financial attributes—excluding price and yield to maturity. It then predicts the yield to maturity, which serves as a reference for fair value calculation.
This method presents several advantages over traditional valuation techniques. Firstly, it significantly reduces pricing errors, ensuring greater accuracy. Secondly, it performs well even with limited data sets, making it a practical approach for illiquid assets. Additionally, integrating alternative datasets—such as those derived from natural language processing—can further enhance the model’s predictive capabilities.
To validate the effectiveness of this approach, a robust model validation framework is essential. A simple linear regression model can serve as a validation tool, offering three key benefits. Firstly, it enhances transparency by providing a clear understanding of the relationships between input variables and predictions. Secondly, it improves interpretability, allowing users to assess the importance of different financial attributes. Lastly, an easily understood validation model, such as ordinary least squares regression, helps build trust in AI-driven valuation solutions.
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