Evaluating Artificial Intelligence Superiority Over ARIMA in Forecasting Interest Rates and Corporate Bond Pricing

Abstract

The paper examines whether AI models outperform traditional econometric methods, such as GARCH and ARIMA, in predicting interest rate movements and corporate bond pricing. Both approaches were evaluated using historical data with error metrics such as MAE and RMSE. Results have shown that AI models achieve greater accuracy and adaptability, especially during periods of market volatility. Unlike traditional models, which are based on linear assumptions and require manual recalibration, AI models excel at processing large datasets and detecting non-linear relationships. For example, neural networks can identify complex patterns and dependencies that traditional methods often miss, thus offering more reliable forecasts. During periods of high market volatility, the error rate for AI models was considerably lower, underlining their ability to adapt to sudden market changes. Besides, another important advantage of AI is the possibility of real-time data processing, which enables it to stay in step with continuous updates of forecasts. This reduces dependence on static models that are behind the sudden market turn most of the time. An AI-based system can also integrate a greater range of relevant data sources, news sentiment, and macroeconomic indicators into one integral entity, offering an essentially more holistic approach to the forecasting process. This will contribute to better accuracy and relevance of predictions in dynamic financial markets. However, there are challenges in adopting AI models for financial forecasting. Many AI systems are resource-intensive to run, require expertise, and demand high-volume and quality data to train the models-perhaps limiting access to smaller institutions. Besides, some AI models are complex and like a "black box" because very little understanding of how decisions are reached can be explained, which raises a number of concerns regarding the transparency of accountability. Addressing such challenges will be important to trust broader adoption of the AI-driven forecasting tools. These results form a meaningful foundation for implications, both for investors and for the policymakers. To investors, AI-driven algorithms can enrich the investment strategy through better forecasts of interest rate directions and bond price dynamics, enlarging portfolio management with enhanced risk management. In policy terms, deeper insights from the models on market dynamics support informed decision-making with respect to monetary policy and regulations. The final conclusion is that, for accuracy and adaptability, especially in unpredictable financial environments, AI models outperform traditional econometric methods. In financial forecasting, leveraging the power of AI will make it more robust and effective. Future studies can be directed toward hybrid models that combine the strengths of both approaches for better forecasting performance.

Keywords: Gradient Boosting Machines, Artificial Intelligence, GARCH, ARIMA, Interest Rate Forecasting, Corporate Bond Pricing, Financial Time Series, Machine Learning, Volatility, Market Sentiment