Unlocking Market Insights and AI-Driven Stock Return Analysis of the KMI-30 Index

Abstract

Monitoring and assessing market risks is becoming crucial now-a-days for investors, financial institutions, authorities, and other parties. This study examines several models considering the business risk metric Value at Risk (VaR) to determine the optimal framework for the KMI-30 stock market. In this study, we have investigated the potential of artificial intelligence (AI) in assisting investors to navigate the highly volatile stock markets and minimize financial Risk. Our analysis focuses on the KMI-30 index, utilizing a comprehensive dataset spanning the past decade, from January 2012 to December 2022. To facilitate our research, the researcher initially organized the data in Excel and imported it into STATA, where AI-driven algorithms were employed to calculate investment returns. The researcher implemented the GARCH (Generalized Autoregressive Conditional Heteroskedasticity) model to enhance our risk assessment with parameters set at 1,1. It allowed us to estimate the Value at Risk (VAR) and gain valuable insights into market dynamics and risk exposure. Our findings demonstrate the efficacy of AI in rapidly processing and analyzing large volumes of financial data, enabling investors to make informed decisions promptly. Researchers have observed that investors can significantly improve their decision-making processes by correctly utilizing AI methods. The results underscore the potential for AI to enhance decision-making in the financial world, particularly in volatile stock markets. This study contributes to a growing body of research highlighting the practical utility of AI in finance and its potential to mitigate financial risks for investors, ultimately leading to more informed and profitable investment strategies.