MATHEMATICIANS from the Ateneo de Manila University have developed artificial intelligence (AI) deep learning tools for predicting secondary market interest rates.

The AI learning models can be used by both the government and businesses to help manage risks and reduce borrowing costs, Ateneo’s Office of the Assistant Vice-President for Research, Creative Work, Innovation said in a statement.

The research paper titled “Deep Learning Approaches in Interest Rate Forecasting” and authored by Ateneo’s Halle Megan L. Bata, Mark Jayson A. Victoria, Wyonna Chezska B. Alvarez, Elvira P. de Lara-Tuprio, and Armin Paul D. Allado was published in the journal AIP Conference Proceedings on Nov. 15.

“Interest rates are among the most important macroeconomic factors considered by both government and private entities when making investment and policy decisions. A reliable forecast is a requisite to sound management of exposure to different types of risk,” the Ateneo researchers were quoted as saying.

The researchers tested two deep learning models for rate forecasting: the Multi-layer Perceptrons (MLP) and Vanilla Generative Adversarial Networks (VGAN).

The MLP model is a type of artificial neural network that passes the data through a series of cells to find complex patterns in data.

Meanwhile, the VGAN is made up of two networks — a synthetic data generator and a discriminator that determines data authenticity — that work opposite each other for analysis.

“Both successfully anticipated changes in Philippine Benchmark Valuation (BVAL) rates before and during the pandemic, showcasing the models’ robust capability to potentially foresee economic fluctuations and market disruptions… The researchers found that both models produced reliable forecasts of one-, three-, six-month, and one-year BVAL rates within the limits of the datasets used. They successfully predicted key trends by incorporating as many as 16 domestic and global economic indicators, including inflation, exchange rates, and credit default swaps,” Ateneo said.

Based on the research, the MLP model performed well with fewer variables and simpler structures, while the VGAN model excelled in analyzing complex scenarios and working with larger datasets.

“The practical implications of these AI deep learning models are substantial, according to the researchers: financial institutions could potentially deploy them to manage market, credit, liquidity, and other risks; and governments could also potentially use these models to optimize debt issuance strategies by reducing borrowing costs,” Ateneo said.

“The study highlights the growing role of AI in financial decision-making and suggests exploring more advanced neural network designs to further enhance forecasting accuracy. It is hoped that businesses and policy makers will come to embrace these technologies in order to gain a competitive advantage in a rapidly evolving data-driven landscape.” — Aaron Michael C. Sy

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