Neural network-based liquidity risk prediction in Indian private banks

Abstract

Liquidity risk stands as a pivotal financial concern within the Indian banking landscape, particularly in the context of Basel Recommendation. This recommendation underscores the vital role of liquidity risk management, urging banks to establish robust information systems for the assessment, anticipation, and control of liquidity risks. Recognizing the nuanced nature of this risk, banks across India employ diverse tools and methodologies in managing liquidity risk. This study delves into the efficacy of artificial neural networks (ANNs) in forecasting liquidity risk within the private banking sector in India. Drawing from prior research and leveraging accounting data, a unique structure and architecture for a multilayer perceptron neural network was developed. Subsequently, the study harnessed this ANN framework, along with Matlab software, to forecast liquidity risk in private Indian banks over the period spanning 2009 to 2019. The research outcomes shed light on the promising potential of artificial neural networks as a valuable tool for predicting liquidity risk in private Indian banks. This discovery holds significance in enhancing the resilience and stability of the Indian banking sector by providing more precise and timely insights into liquidity risk management.

Keywords

• Liquidity risk
• Credit risk
• Systematic risk
• ANN