Enhancing market forecast accuracy: A structural equation model analysis of technical indicators in the Bank Nifty index

Abstract

The growing intricacy of international financial markets requires sophisticated approaches to managing investments and minimizing losses. This paper evaluates the use of Structural Equation Modeling (SEM) to improve forecast accuracy by integrating multiple technical indicators within the Bank Nifty Index. The study employs SEM to estimate the effect of key technical indicators such as the Simple Moving Average (SMA), Relative Strength Index (RSI), Volume Weighted Average Price (VWAP), and Moving Average Convergence Divergence (MACD) on trading volumes and closing values. The model considers both direct and indirect relationships among these indicators to determine their overall impact. The study highlights the significance of certain technical indicators in predicting market trends. It demonstrates SEM’s effectiveness in estimating interrelationships among these indicators and formulating predictive models. This study underscores SEM’s effectiveness in financial forecasting by showing that incorporating multiple technical indicators enhances prediction accuracy and improves decision-making in financial markets. Investors and traders can use these findings to develop better trading strategies, improve market stability, and maximize returns. This analysis supports the case for a multi-indicator approach in forecasting models.