Neural network and machine learning use cases: Indian bond market predictions

Jesmine Mary Antony () and Sundaram Natarajan ()

The Economics and Finance Letters, 2024, vol. 11, issue 1, 57-79

Abstract: This study examines machine learning techniques to investigate how artificial intelligence (AI) affects predicting future trends in the bond market. The bond market offers a global perspective on capital costs for a business by establishing the fair value of the bond issue, which is based on multiple factors. The asset price market, which has employed machine learning (ML) and deep learning (DL) techniques to address the primary forecasting difficulty, surprisingly plays a significant role in predicting future bond market returns. As an outcome, if this gap can be forecast, it can act as the bond market's data-driven long-term direction and yield additional profits. Daily security-specific data for the 10-to-3-year Indian Treasury Bond (ITB) was gathered from 2013 to 2022 and is available in the global government bonds database. The researchers looked at how well the auto-regressive integrated moving average (ARIMA), linear regression, and deep recurrent neural network-long short-term memory (DLSTM) models could predict bond yields and returns in future bond markets. The empirical results demonstrate that the DLSTM models most fairly predict the price of government bonds over both the short and longer horizons when compared to ARIMA and linear regression.

Keywords: Artificial intelligence; Auto-regressive integrated moving average; Bond spread strategy; Bond yield forecasting; Deep long short-term memory recurrent neural network; Linear regression; Yield curve analysis. (search for similar items in EconPapers)
Date: 2024
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